Commands in destra v11 - Q-DAS

Commands in destra v11

destra v11 (2015-07-24 / 11.0.3.2)

Commands in destra v11 2/89

Version: 2.0 © 2015 Q-DAS GmbH, 69469 Weinheim Doku-Nr.: QDOC-572-67

Contents 1. Overview of specific commands in destra v11 ................................................................ 5

2. Start tab ......................................................................................................................... 6

2.1 “Create“ group in the Start tab ................................................................................. 6

2.1.1 New option in the “Create“ group of the Start tab .............................................................................. 7

2.1.2 Measurement System Analysis option in the “Create“ group of the Start tab .................................... 7

How to create a data source for measurement system analysis, type-1 study ........................................... 8



How to create a data source for measurement system analysis, stability .................................................. 9

How to create a data source for measurement system analysis, linearity ................................................ 10

How to create a data source for measurement system analysis, signal detection ................................... 10

How to create a data source for measurement system analysis, kappa coefficient ................................. 11

How to create a data source for measurement system analysis, type-1 study (2nd true position) ............ 13

2.1.3 Process Capability Analysis option in the “Create“ group of the Start tab ....................................... 14

How to create a data source for machine capability analysis ................................................................... 14

How to create a data source for process capability analysis .................................................................... 15

How to create a data source for a 1-part report ........................................................................................ 15

How to create a data source for a 5-part report ........................................................................................ 16

How to create a data source for discrete data .......................................................................................... 16

How to create a data source for an error log sheet .................................................................................. 17

How to create a data source for a machine capability analysis including positional tolerances ............... 17

2.1.4 Model f(x) | Correlation option in the “Create“ group of the Start tab ............................................... 18

How to create a data source for Pearson’s correlation coefficient ............................................................ 18

2.1.5 Model f(x) | Regression option in the “Create“ group of the Start tab .............................................. 19

How to create a data source for simple regression .................................................................................. 19

How to create a data source for multiple regression ................................................................................ 20

How to create a data source for multivariate regression .......................................................................... 20

How to create a data source for logistic regression .................................................................................. 21

2.1.6 Model f (x) | Analysis of Variance option in the “Create” group of the Start tab ............................... 22

How to create a data source for an analysis of variance (ANOVA) for single or multiple factors ............. 22

How to create a data source for an analysis of covariance (ANCOVA) .................................................... 23

How to create a data source for a multivariate analysis of variance (MANOVA) ...................................... 24

How to create a data source for a repeated measures ANOVA ............................................................... 25

2.1.7 Design option in the “Create“ group of the Start tab ........................................................................ 25

How to create a new design ..................................................................................................................... 26



How to create a new design based on the data of an existing one .......................................................... 27

How to augment an available design ........................................................................................................ 28

2.1.8 Test procedures option in the “Create“ group of the Start tab ......................................................... 29

How to create a data source for a 1-sample t-test .................................................................................... 29

How to create a data source for a 1-sample χ²-test .................................................................................. 29

How to create a data source for a Wilcoxon test ...................................................................................... 29

How to create a data source for a 2-sample t-test (independent samples) .............................................. 30

How to create a data source for a 2-sample t-test (paired samples) ........................................................ 30

How to create a data source for a Mann-Whitnex test .............................................................................. 30

How to create a data source for a F-test .................................................................................................. 30

How to create a data source for a rank test for dispersions ..................................................................... 31

How to create a data source for a simple analysis of variance (test) ....................................................... 31

How to create a data source for a Bartlett test ......................................................................................... 31

How to create a data source for the Kruskal-Wallis H-test ....................................................................... 31

How to create a data source for a Levene test ......................................................................................... 31

How to create a data source for a 1-sample µ-test .................................................................................. 33

How to create a data source for a 1-sample p-test ................................................................................... 33

How to create a data source for a 2-sample µ-test .................................................................................. 33

How to create a data source for a 2-sample p-test (BD) .......................................................................... 34

How to create a data source for a k-sample chi-squared test (PD) .......................................................... 34

How to create a data source for a test of homogeneity (PD) .................................................................... 34

How to create a k-sample chi-squared test (BD) ...................................................................................... 34

2.1.9 Reliability Analysis option of the “Create“ group in the Start tab ...................................................... 35

How to create a data source for reliability analysis................................................................................... 35

2.2 “Evaluation“ group in the Start tab ..........................................................................36

2.2.1 Evaluation strategy option in the “Evaluation“ group of the Start tab ............................................... 36

2.2.2 Standard procedure option in the “Evaluation“ group of the Start tab .............................................. 36

2.2.3 Distributions option in the “Evaluation“ group of the Start tab .......................................................... 37

2.2.4 Model f(x) | Correlation option in the “Evaluation“ group of the Start tab ......................................... 38

Evaluation based on Pearson’s correlation .............................................................................................. 38

2.2.5 Model f(x) | Regression option in the “Evaluation“ group of the Start tab ........................................ 40

How to evaluate simple regression ........................................................................................................... 40

How to evaluate multiple regression ......................................................................................................... 42

How to evaluate multivariate regression ................................................................................................... 44

How to evaluate logistic regression .......................................................................................................... 45

2.2.6 Model f(x) | Analysis of Variance option in the “Evaluation“ group of the Start tab .......................... 47



How to evaluate simple or multiple analysis of variance (ANOVA) .......................................................... 47

How to evaluate an analysis of covariance (ANCOVA) ............................................................................ 49

How to evaluate a multivariate analysis of variance (MANOVA) .............................................................. 50

How to evaluate repeated measures ANOVA .......................................................................................... 52

2.2.7 Design option in the “Evaluation“ group of the Start tab .................................................................. 54

2.2.8 Test procedures option in the “Evaluation“ group of the Start tab ................................................... 54

Test procedures for continuous characteristic values............................................................................... 54

How to evaluate a 1-sample t-test (normal distribution: parameter µ) ...................................................... 55

How to evaluate a 1-sample chi-squared test (normal distribution: parameter sigma) ............................. 57

How to evaluate the Wilcoxon test (median) ............................................................................................ 59

How to evaluate the 2-sample t-test (normal distribution: parameter µ, independent samples) ............... 61

How to evaluate a 2-sample F-test (normal distribution: sigma) ............................................................... 63

How to evaluate a 2-sample Mann-Whitnex U-test (median) ................................................................... 65

How to evaluate a 2-sample rank test for dispersions (dispersion) .......................................................... 67

How to evaluate a simple analysis of variance (normal distribution: parameter µ) .................................. 69

How to evaluate a k-sample Bartlett test (normal distribution: parameter σ) ............................................ 71

Howe to evaluate a k-sample Kruskal-Wallis test (location: median) ....................................................... 72

How to evaluate a k-sample Levene test (variance) ................................................................................. 74

Test procedures for discrete values ......................................................................................................... 76

How to evaluate a 1-sample µ-test (Poisson distribution: parameter µ) .................................................. 76

How to evaluate a 1-sample p-test (binomial distribution: parameter p) ................................................... 78

How to evaluate a 2-sample µ-test (Poisson distribution: parameter µ) .................................................. 79

How to evaluate a 2-sample p-test (binomial distribution: parameter p) ................................................... 81

How to evaluate a k-sample chi-squared test (Poisson distribution: parameter µ) .................................. 82

How to evaluate a k-sample χ²-test (Poisson distribution: parameter µ) .................................................. 84

How to evaluate a k-sample χ²-test (binomial distribution: parameter p) .................................................. 85

3. Index .............................................................................................................................88



1. Overview of specific commands in destra v11 This reference manual focuses on the Start tab including the “Create” and “Evaluation” groups of

commands. The included commands or options are very important in destra since each data

set you create and evaluate in the Project Explorer either creates a data source node or a session

node. You thus have to consider some special aspects and differences compared to the

application of our software products solara.MP and qs-STAT. These differences are

explained in the following.

This reference manual helps you understand the principle behind the new Project Explorer

and explains how to find a certain analysis function.

We will be pleased about any feedback and any suggestions for improvement of this

reference manual.



2. Start tab

Figure 2-1: Start tab including the “Create“, “Evaluation“ and “Reports“ groups of commands

Two groups of the Start tab are of particular importance.

Create

Evaluation

Use the options of the Create group to create new data sources. You either transfer data to

these sources or enter them. Now you can evaluate these data sources by using the options of

the Evaluation group.

The single options are explained in the following.

2.1 “Create“ group in the Start tab

Figure 2-2: “Create“ group in the Start tab

Almost all of these options create a new data source in the Project Explorer (the only

exception is the New option). However, this does not always mean that you actually create

nothing but a new data source in the Project Explorer by selecting one of these options. The

number and type of new structural elements the program creates in the Project Explorer

always depend on the node you selected in the Project Explorer when you click the

respective button. You will observe that the program often creates further nodes in the

Project Explorer in addition to the respective data source.

The reason is the specified strict hierarchy of the Project Explorer‘s structural

elements.

A project phase always represents the next level below the project node.

A work space always represents the next level below a project phase.

A data source always represents the next level below a work space.



The following table gives an overview of the structural elements the program creates

automatically in the Project Explorer as soon as you select any option from the ”Create”

group.

Selected node Automatically generated structural elements in the Project Explorer

Project Project phase – work space – data source

Project phase Work space – data source

Work space Data source

Data source Data source Table 2-1: Overview of possible automatically generated structural elements when applying an option in the “Create“ group of the Start tab

2.1.1 New option in the “Create“ group of the Start tab

Figure 2-3: New option

New | Project phase: This option creates a new project phase node in the Project Explorer.

New | Work space: This option creates a new work space in the Project Explorer.

2.1.2 Measurement System Analysis option in the “Create“ group of the Start tab

Figure 2-4: Measurement System Analysis option



How to create a data source for measurement system analysis, type-1 study

Start | Create | Measurement System Analysis | Type 1 Study 1: This option creates a data source

for type-1 study in the Measurement System Analysis module. The Measurement System

Analysis – Type-1 Study dialog box opens. It includes an input mask for the following information.

Name: Enter the characteristic description.

Characteristic type: deactivated

Unit: Enter the unit of the measured quantity.

Nominal size: Enter the nominal dimension of the characteristic.

LSL: Enter the lower specification limit.

USL: Enter the upper specification limit.

RE: Enter the resolution of the measurement system.

XRef: Enter the reference value of the measurement standard as given in the

corresponding calibration certificate.

Figure 2-5: Upper part of the Measurement System Analysis – Type-1 Study dialog box

Confirm your input to open the values mask. You enter the measurement results there. Now

you start an evaluation by selecting the respective data source in the Project Explorer and

clicking Start | Evaluation | Standard procedure.


Start | Create | Measurement System Analysis | Type 2 study: This option creates a data source for

type-2 study in the Measurement System Analysis module. The Measurement System








k: Enter the number of operators.

r: Enter the number of repeated measurements.

n: Enter the number of parts applied in this study.








Start | Create | Measurement System Analysis | Type 3 study: This option creates a data source for

type-3 study in the Measurement System Analysis module. The Measurement System









n: Enter the number of applied parts.





How to create a data source for measurement system analysis, stability

Start | Create | Measurement System Analysis | Type 5 study (stability): This option creates a data

source for stability analysis (bias and repeatability) in the Measurement System Analysis

module. The Measurement System Analysis – Stability dialog box opens. It includes an input mask

for the following information.





XRef: Enter the reference value of the measurement standard as given in the

corresponding calibration certificate.



Figure 2-8: Upper part of the Measurement System Analysis – Stability dialog box




How to create a data source for measurement system analysis, linearity

Start | Create | Measurement System Analysis | Type 4 study (linearity): This option creates a data

source for testing bias by using several measurement standards in the Measurement

System Analysis module. The Measurement System Analysis – Linearity dialog box opens. It

includes an input mask for the following information.








n: Enter the number of applied measurement standards/reference parts.

m: Enter the number of repeated measurements per measurement

standard/reference part.

Figure 2-9: Upper part of the Measurement System Analysis – Linearity dialog box




How to create a data source for measurement system analysis, signal detection

Start | Create | Measurement System Analysis | Signal detection: This option creates a data source

for the method of signal detection in the Measurement System Analysis module. The

Measurement System Analysis – Signal Detection dialog box opens. It includes an input mask for

the following information.










r: Enter the number of repeated measurements per part.


m: Enter the number of reference values per part (“normally” m = 1).

Figure 2-10: Upper part of the Measurement System Analysis – Signal detection dialog box

Confirm your input to open the values mask. You enter the numerical reference

measurement results in the first column.

Note: The values for USL and LSL relate to these reference measurements!

You use numerical values (numbers) to indicate the test decisions of the respective operator.

0 indicates Part is not o.k.

1 indicates Part is o.k.

Now you start an evaluation by selecting the respective data source in the Project Explorer

and clicking Start | Evaluation | Standard procedure.

How to create a data source for measurement system analysis, kappa coefficient

Start | Create | Measurement System Analysis | Kappa coefficient: This option creates a data source

for Fleiss’ or Cohen’s kappa in the Measurement System Analysis module. The

Measurement System Analysis – Kappa coefficient dialog box opens. It includes an input mask for




Catalogue: Select the catalogue you want to apply.




m: Enter the number of reference values per part.



Figure 2-11: Measurement System Analysis – Kappa coefficient dialog box

Values indicating the decision of operators have to be available in a catalogue. In other

words: You first have to create the content of such a catalogue, such as Part o.k. or Part

n.o.k. The lower part of the dialog box shows to options specifying the selection of the

catalogue to be applied.

1. Catalogue is part of data source

2. Catalogue is read from config DB

Figure 2-12: Lower part of the Measurement System Analysis – Kappa coefficient dialog including two radio buttons for the selection of catalogues to be applied

The Catalogue is part of data source radio button is enabled by default. By using this option,

you have to enter possible values for test decisions manually in the Level values list (framed

in red in Figure 2-11). The program saves these level values to the project.

Select the Catalogue is read from config DB radio button and the program only applies

values from the ordinal classes catalogue of the database adjusted in the program

configuration (File | Configuration | Catalogues).

Note: In case you activate the second radio button – catalogue from database – you have to consider that the contents of the ordinal classes catalogue is identical in both database when you want to exchange the project file between users working at different sites or between customer and supplier. Otherwise, you will notice a considerable difference between the evaluation results of the two users…

Note: In order to learn how to handle the database, we recommend you to attend the

respective seminar of our affiliated company TEQ.



Click on the button in the Catalogue column of the Measurement System Analysis – Kappa coefficient

dialog box to open the Characteristic catalogue: … dialog box.

Figure 2-13: Characteristic catalogue: … dialog box

Select the Create new catalogue radio button to create a new catalogue with an individual

catalogue name.

In case the data source already includes a catalogue that you want to apply, you activate the

Selection of available catalogues option. After you selected the respective catalogue, you may

select the included level values for the characteristic.

Note: Create a new catalogue with an individual name before specifying level values in the automatically generated default catalogue. Since we do not change the name of the default catalogue but create a new catalogue, the new catalogue does not take over the entries of the default catalogue. It is thus empty!

Confirm your input to open the values mask. You enter the characteristic values. Now you

start an evaluation by selecting the respective data source in the Project Explorer and


How to create a data source for measurement system analysis, type-1 study (2nd true position)

Start | Create | Measurement System Analysis | Type-1 study (2nd true position: This option creates a

characteristic group for two-dimensional and three-dimensional evaluations of type-1 studies

in the Measurement System Analysis module. The Measurement System Analysis – true

position MSA type 1 study dialog box opens. You only adjust the dimension here – 2D or 3D. Do

not enter any further information; otherwise you might have to delete erroneous values later

on…

Figure 2-14: Upper part of the Measurement System Analysis – true position MSA type 1 study dialog box

Confirm your input to open the values mask. You enter the measurement results for the

subordinate characteristics. Values of superordinate characteristics are not relevant to this



type of evaluation. However, you have to enter the information about the single

characteristics now.

Tip: Open the Edit tab and select Characteristics mask to open the characteristics mask. Here you edit the data of each single characteristic.


and clicking Start | Evaluation | Standard procedure.

2.1.3 Process Capability Analysis option in the “Create“ group of the Start tab

Figure 2-15: Process Capability Analysis option

How to create a data source for machine capability analysis

Start | Create | Process Capability Analysis | Short-term capability of 1 sample (Cm, Cmk): The program

generates a data source including a continuous (variable) characteristic for machine

capability analysis in the Sample Analysis module.

Figure 2-16: Upper part of the Process Capability Analysis – Machine Capability dialog box

The Process Capability Analysis – Machine Capability dialog box opens. It includes an input mask






LSL: Enter the lower specification limit of the characteristic.



USL: Enter the upper specification limit of the characteristic.

Confirm your input to open the values mask. You enter the characteristic values here. Now



How to create a data source for process capability analysis

Start | Create | Process Capability Analysis | Process capability of k sample (Pp, Ppk, Cp, Cpk): The

program generates a data source including a continuous (variable) characteristic for process

capability analysis in the Process Capability Analysis module.

Figure 2-17: Upper part of the Process Capability Analysis – Process Capability dialog box

The Process Capability Analysis – Process Capability dialog box opens. It includes an input mask











How to create a data source for a 1-part report

Start | Create | Process Capability Analysis | 1 Part Report: The program generates a data source

including a continuous (variable) characteristic for a 1-part report in the Sample Analysis

module.

Figure 2-18: Upper part of the Process Capability Analysis – 1 Part Report dialog box

The Process Capability Analysis – 1 Part Report dialog box opens. It includes an input mask for the

following information.












How to create a data source for a 5-part report

Start | Create | Process Capability Analysis | 5 Part Report: The program generates a data source

including a continuous (variable) characteristic for a 5-part report in the Sample Analysis

module.

Figure 2-19: Upper part of the Process Capability Analysis – 5 Part Report dialog box

The Process Capability Analysis – 5 Part Report dialog box opens. It includes an input mask for the








Confirm your input to open the values mask. You transfer the five characteristic values to this

mask. Now you start an evaluation by selecting the respective data source in the Project

Explorer and clicking Start | Evaluation | Standard procedure.

How to create a data source for discrete data

Start | Create | Process Capability Analysis | Discrete data (proportion or events per unit): The program

generates a data source including a discrete (attribute) characteristic in the Process

Capability Analysis module.



Figure 2-20: Upper part of the Process Capability Analysis – Attribute analysis dialog box

The Process Capability Analysis – Attribute Analysis dialog box opens. You enter the name of the

characteristic and the subgroup size n.

Note: The content of the Characteristic type attribute field is fixed by default. In case you need a variable subgroup size n, select Characteristics mask from the Edit tab. Now you change the attribute entry in the ”Characteristic type” field of the characteristics mask and select variable from the drop-down menu.

Confirm your input to open the values mask. You enter or transfer the values. Now you start

an evaluation by selecting the respective data source in the Project Explorer and clicking Start

| Evaluation | Standard procedure.

How to create a data source for an error log sheet

Start | Create | Process Capability Analysis | Error log sheet (events per unit): The program generates

a data source including a group of discrete (attribute) characteristics for evaluating an error

log sheet in the Process Capability Analysis module.

Figure 2-21: Upper part of the Process Capability Analysis – Error log sheet dialog box

The Process Capability Analysis – Error log sheet dialog box opens. You enter the name of the

error log sheet, the number of error types NDC available in the error log sheet and the

subgroup size n. The sampling scheme is either adjusted to the value fixed (= invariable and

thus constant) or variable.

Confirm your input to open the values mask. You enter or transfer the values. Now you start

an evaluation by selecting the respective data source in the Project Explorer and clicking Start

| Evaluation | Standard procedure.

How to create a data source for a machine capability analysis including positional tolerances

Start | Create | Process Capability Analysis | Short-term capability of 2D / 3D characteristics (Pm, Pmk):

The program generates a data source including a group of continuous (variable)



characteristics for evaluating capability of 2D or 3D positional tolerances in the Sample

Analysis module.

2.1.4 Model f(x) | Correlation option in the “Create“ group of the Start tab

Figure 2-22: Model f(x) option

How to create a data source for Pearson’s correlation coefficient

Start | Create | Model f(x) | Correlation | Pearson: The program generates a data source for

correlation analysis in the Analysis of Regression / Variance module.

Figure 2-23: Upper part of the Process Improvement – Pearson correlation dialog box

The Process Improvement – Pearson correlation dialog box opens. It includes an input mask for the


Name: Enter the description of the characteristics.

Characteristic type: Select the characteristic type.

Catalogue: remains empty

Unit: Enter the unit of the characteristic.

Edit the characteristics and confirm your input to open the values mask. You enter or transfer

the characteristic data.

Note: Please always consider that each line refers to a specific characteristic. In case the number of cells of the different columns differs, it is quite likely that an error slipped in.


and clicking Start | Evaluation | Model f(x) | Correlation | Pearson.



Note: You may apply the Model f(x) | Correlation | Spearman or Model f(x) | Correlation | Kendall functions in the same way as described above.

2.1.5 Model f(x) | Regression option in the “Create“ group of the Start tab

How to create a data source for simple regression

Start | Create | Model f(x) | Regression | Simple: The program generates a data source for simple

regression in the Analysis of Regression / Variance module.

Figure 2-24: Upper part of the Process Improvement – Simple regression dialog box

The Process Improvement – Simple regression dialog box opens. It includes an input mask for the





Unit: Enter the unit of the characteristic.

X: Select the characteristic you want to use as a factor.

Y: Select the characteristic you want to use as a response.

The dialog box includes two continuous (variable) characteristics by default – a factor (or

independent variable) referred to as X and the response (or dependent variable) referred to

as Y.


the characteristic values.



and clicking Start | Evaluation | Model f(x) | Regression | Simple.



How to create a data source for multiple regression

Start | Create | Model f(x) | Regression | Multiple: The program generates a data source for

multiple regression in the Analysis of Regression / Variance module.

Figure 2-25: Upper part of the Process Improvement – Multiple regression dialog box

The Process Improvement – Multiple regression dialog box opens. The dialog box includes three

continuous (variable) characteristics by default – two factors (or independent variable)

referred to as X1 and X2 and the response (or dependent variable) referred to as Y.





and clicking Start | Evaluation | Model f(x) | Regression | Multiple.

How to create a data source for multivariate regression

Start | Create | Model f(x) | Regression | Multivariate: The program generates a data source for

multivariate regression in the Analysis of Regression / Variance module.

The Process Improvement – Multivariate regression dialog box opens. It includes an input mask for



Characteristic type: remains unchanged


Unit: Enter the unit of the characteristics.

X: Select characteristics you want to use as a factor.

Y: Select characteristics you want to use as a response.



The dialog box includes two continuous (variable) responses (or dependent variables) by

default referred to as Y1 and Y2 and a continuous (variable) factor (or independent variable)

referred to as X.

Figure 2-26: Upper part of the Process Improvement – Multivariate regression dialog box





and clicking Start | Evaluation | Model f(x) | Regression | Multivariate.

How to create a data source for logistic regression

Start | Create | Model f(x) | Regression | Logistic: The program generates a data source for logistic

regression in the Analysis of Regression / Variance module.

The Process Improvement – Logistic regression dialog box opens. It includes an input mask for the




Catalogue: usually remains empty

Unit: Enter the unit of the characteristics.

V: Select a characteristic you want to use as a factor.

Y: Select a characteristic you want to use as a response.

F: Select a characteristic you want to use as a container for the frequency of

collection



The dialog box includes an ordinal response (or dependent variables) by default referred to

as Y and two factors (or independent variables) referred to as X1 and X2. In addition, there is

a column for frequencies.

Figure 2-27: Upper part of the Process Improvement – Logistic regression dialog box

Note: Even if you only want to include single measurements in the data source, you must not delete the frequency column! You just have to enter a “1” into the cells of this column in the values mask.

Enter possible values of the ordinal or nominal response into the Level values column on the

right-hand side of the dialog box. Edit the characteristics and confirm your input to open the

values mask. You enter or transfer the characteristic values. Now you start an evaluation by

selecting the respective data source in the Project Explorer and clicking Start | Evaluation |

Model f(x) | Regression | Logistic.

2.1.6 Model f (x) | Analysis of Variance option in the “Create” group of the Start tab

How to create a data source for an analysis of variance (ANOVA) for single or multiple factors

Start | Create | Model f(x) | Analysis of variance | ANOVA: The program generates a data source for

an analysis of variance in the Analysis of Regression / Variance module.

The Process Improvement – Analysis of variance dialog box opens. It includes an input mask for




Catalogue: You either create a new catalogue or select an existing one.

Unit: Enter the unit of each characteristic.

X: Select a characteristic you want to use as a factor with fixed level values.

Z: Select a characteristic you want to use as a factor with random level values.




The dialog box includes a continuous (variable) response by default referred to as Y and two

ordinal factors (or influence quantities) referred to as A and B. Enter possible values of each

factor into the Level values column on the right-hand side of the dialog box.

Note: In case you forget to enter level values here and you enter level values in the values mask later on, the program will show a message informing you that the entered value in unknown.

Note: In case you forget to enter level values here and you transfer data from the Windows

clipboard to the values mask later on, the program will show a number of error messages

and will NOT transfer the level values.

Tip: In case you face data including factors whose level values are not entirely known, you cannot enter the list of level values here. Save the data as a CSV file – e.g. in Microsoft Excel – instead. Now you import this CSV file by selecting File | File… | Import. The program transfers the factors as nominal values and creates the ordinal classes catalogue automatically for level values.

Figure 2-28: Upper part of the Process Improvement – Analysis of Variance dialog box

Edit the characteristics and confirm your input to open the values mask. You transfer the

characteristic values. Now you start an evaluation by selecting the respective data source in

the Project Explorer and clicking Start | Evaluation | Model f(x) | Analysis of Variance | ANOVA.

How to create a data source for an analysis of covariance (ANCOVA)

Start | Create | Model f(x) | Analysis of variance | ANCOVA: The program generates a data source

for an analysis of covariance in the Analysis of Regression / Variance module.

The Process Improvement – ANCOVA dialog box opens. It includes an input mask for the following

information.



Catalogue: You either create a new catalogue or select an existing one.



V: Select a characteristic you want to use as a covariable (continuous values).




Figure 2-29: Upper part of the Process Improvement – ANCOVA dialog box

The dialog box includes a continuous (variable) response by default referred to as Y, an

ordinal factor referred to as A and a continuous (variable) covariable called X. Enter possible

values of the ordinal factor into the Level values column on the right-hand side of the dialog

box.



the Project Explorer and clicking Start | Evaluation | Model f(x) | Analysis of Variance | ANCOVA.

How to create a data source for a multivariate analysis of variance (MANOVA)

Start | Create | Model f(x) | Analysis of variance | MANOVA: The program generates a data source

for a multivariate analysis of variance in the Analysis of Regression / Variance module.

The Process Improvement – Multivariate analysis of variance dialog box opens. It includes an input

mask for the following information.



Catalogue: You either select an existing catalogue for factors or create a new one by

entering values in the Level values column.



V: Select a characteristic you want to use as a factor with continuous level values.

Y: Select a characteristic you want to use as a response (continuous values).

The dialog box includes two continuous (variable) responses by default referred to as Y1 and

Y2 and an ordinal factor referred to as A.

Figure 2-30: Upper part of the Process Improvement – Multivariate analysis of variance dialog box



the Project Explorer and clicking Start | Evaluation | Model f(x) | Analysis of Variance | MANOVA.



How to create a data source for a repeated measures ANOVA

Start | Create | Model f(x) | Analysis of variance | Repeated measures: The program generates a data

source for a multivariate repeated measures ANOVA in the Analysis of Regression /

Variance module.

The Process Improvement – Multivariate repeated measures ANOVA dialog box opens. It includes an

input mask for the following information.



Catalogue: You either select a factor level catalogue for ordinal characteristics or

enter possible factor levels in the Level values column.



R: Select a characteristic you want to use as a repeated measure container.


Figure 2-31: Upper part of the Process Improvement – Multivariate repeated measures ANOVA dialog box

The dialog box includes a responses Y, a repeated measures container RM and a factor A

with fixed level values by default.



the Project Explorer and clicking Start | Evaluation | Model f(x) | Analysis of Variance | Repeated

measures.

2.1.7 Design option in the “Create“ group of the Start tab

Figure 2-32: Design option



How to create a new design

Start | Create | Design | Create design: The program generates a data source based on the

selected design in the Analysis of Regression / Variance module. The Design of experiments

window opens. Select the respective design in the Selection tab.

Figure 2-33: Upper part of the Design of experiments window

Available designs in the Design of experiments window

1st level 2nd level 3rd level

Screening designs Plackett-Burman designs Not available

Factorial designs 2-level designs

D-optimal designs

Multi-level designs

Response surface designs Central composite designs Not available

Box-Behnken designs Not available

D-optimal designs Not available

Mixture designs Simplex lattice designs Not available

Simplex centroid designs Not available

D-optimal designs (mixture) Not available Table 2-2: Overview of designs available in the Design of experiments window

Note: When you select a 2-level design, the Selection of factorial designs with 2 factor levels dialog box opens. Select the respective design by click.

Figure 2-34: Selection of factorial designs with 2 factor levels dialog box



Let’s go back to the Design of experiments window. Depending on the selected type of

design, it offers up to five different tabs.

Figure 2-35: Upper part of the Design of experiments window including several tabs

In general, you enter the required information into the tabs from left to right.

The following table provides a brief overview of the contents available in the single tabs.

Tab Contents

Selection Tree structure including all available designs

Definition (Contents of this tab depend on the selected design.)

Definition of

responses

factors

level values of factors

number of replicates

number of centre points

number of blocks

randomization option

…

Settings D-opt design (Only available after selection of D-optimal design)

Only available after selection of D-optimal design. Select the type of model, number of blocks and define number of additional runs.

Design Evaluation (Contents of this tab depend on the selected design.)

Gives an overview specific to the selected design. It includes statistics helping you evaluate the properties of the created design.

Design (Contents of this tab depend on the selected design.)

Shows all lines of the created design completely. You may still adjust the sorting of single or several factor columns.

Table 2-3: Overview of tabs available in the Design of experiments tab

How to create a new design based on the data of an existing one

Start | Create | Design | Reuse design: In order to use this function, you have to select an

available data source in the Project Explorer that already includes a design. The

program opens the Design of experiments window. Select the respective type of design in the

Selection tab. The new design applies all factors and level values but you may still adapt them,



if required. destra creates the new design as a new data source in the Analysis of

Regression / Variance module.

How to augment an available design

Start | Create | Design | Augment design: In order to use this function, you have to select an

available data source in the Project Explorer that already includes a factorial design

with 2 factor levels. This option augments (= changes!) the existing design in the data

source. You may either add centre points to a factorial design with 2 factor levels that does

not include any centre point so far or add further centre and axial points to a factorial design

with 2 factor levels. Caution: You cannot undo these steps!

Figure 2-36: Add centre points dialog box

Figure 2-37: Add axial points dialog box



2.1.8 Test procedures option in the “Create“ group of the Start tab

Figure 2-38: Test procedures | Continuous values option

How to create a data source for a 1-sample t-test

Start | Create | Test procedures | Continuous values | 1-sample t-test (location: µ): The program

generates a data source in the Analysis of Regression / Variance module including a

continuous characteristic for comparing the location parameter µ of a population (normally

distributed population assumed) to a default value. Start the evaluation by activating the

respective data source in the Project Explorer and selecting Start | Evaluation | Test procedures |

Continuous values | 1-sample t-test (location: µ).

How to create a data source for a 1-sample χ²-test

Start | Create | Test procedures | Continuous values | 1-sample ²-test (variance): The program


continuous characteristic for comparing the standard deviation of a population (normally

distributed random variable assumed) to a default value. Start the evaluation by activating

the respective data source in the Project Explorer and selecting Start | Evaluation | Test

procedures | Continuous values | 1-sample ²-test (variance).

How to create a data source for a Wilcoxon test

Start | Create | Test procedures | Continuous values | 1-sample Wilcoxon test (location: median): The

program generates a data source in the Analysis of Regression / Variance module

including a continuous characteristic for comparing the median of a unimodal population to a

default value. Start the evaluation by activating the respective data source in the Project

Explorer and selecting Start | Evaluation | Test procedures | Continuous values | 1-sample Wilcoxon

test (location: median).



How to create a data source for a 2-sample t-test (independent samples)

Start | Create | Test procedures | Continuous values | 2-sample t-test (location: µ): The program

generates a data source in the Analysis of Regression / Variance module including two

continuous characteristics for comparing the location parameter µ of two populations

(normally distributed random variables assumed). Start the evaluation by activating the


Continuous values | 2-sample t-test (location: µ).

How to create a data source for a 2-sample t-test (paired samples)

Start | Create | Test procedures | Continuous values | 2-sample t-test (paired: µ): The program


paired continuous characteristics from two populations (normally distributed random

variables assumed). Start the evaluation by activating the respective data source in the

Project Explorer and selecting Start | Evaluation | Test procedures | Continuous values | 2-sample t-

test (paired: µ).

How to create a data source for a Mann-Whitnex test

Start | Create | Test procedures | Continuous values | 2-sample Mann-Whitnex U-test (location: median):

The program generates a data source in the Analysis of Regression / Variance module

including two continuous characteristics for a test comparing the medians of both

populations. Start the evaluation by activating the respective data source in the Project

Explorer and selecting Start | Evaluation | Test procedures | Continuous values | 2-sample Mann-

Whitnex U-test (location: median).

How to create a data source for a F-test

Start | Create | Test procedures | Continuous values | 2-sample F-test (variance: ): The program


continuous characteristics for comparing the variance of two populations (normally

distributed random variables assumed). Start the evaluation by activating the respective data

source in the Project Explorer and selecting Start | Evaluation | Test procedures | Continuous

values | 2-sample F-test (variance: ).



How to create a data source for a rank test for dispersions

Start | Create | Test procedures | Continuous values | 2-sample rank test for dispersions (dispersion):


including two continuous characteristics for comparing the variance of two populations. Start

the evaluation by activating the respective data source in the Project Explorer and selecting

Start | Evaluation | Test procedures | Continuous values | 2-sample rank test for dispersions (dispersion).

How to create a data source for a simple analysis of variance (test)

Start | Create | Test procedures | Continuous values | k-sample analysis of variance (location: µ): The

program generates a data source in the Analysis of Regression / Variance module

including k = 3 continuous characteristics for comparing the location parameter µ (normally

distributed random variables assumed) of k populations. Start the evaluation by activating the


Continuous values | k-sample analysis of variance (location: µ).

How to create a data source for a Bartlett test

Start | Create | Test procedures | Continuous values | k-sample Bartlett test (variance: ): The program

generates a data source in the Analysis of Regression / Variance module including k = 3

continuous characteristics for comparing the variance of k populations (normally distributed

random variables assumed). Start the evaluation by activating the respective data source in

the Project Explorer and selecting Start | Evaluation | Test procedures | Continuous values | k-sample

Bartlett test (variance: ).

How to create a data source for the Kruskal-Wallis H-test

Start | Create | Test procedures | Continuous values | k-sample Kruskal-Wallis test (location: median):


including k = 3 continuous characteristics for comparing the location of k populations. Start

the evaluation by activating the respective data source in the Project Explorer and selecting

Start | Evaluation | Test procedures | Continuous values | k-sample Kruskal-Wallis test (location: median).

How to create a data source for a Levene test

Start | Create | Test procedures | Continuous values | k-sample Levene test (variance): The program

generates a data source in the Analysis of Regression / Variance module including k = 3

continuous characteristics for comparing the variance of k populations. Start the evaluation



by activating the respective data source in the Project Explorer and selecting Start | Evaluation

| Test procedures | Continuous values | k-sample Levene test (variance).



Figure 2-39: Test procedures | Discrete values option

How to create a data source for a 1-sample µ-test

Start | Create | Test procedures | Discrete values | 1-sample test – events per unit (PD): The program


discrete (attribute) characteristic for comparing the parameter µ of a population to a default

value (random variable of Poisson distribution assumed). Start the evaluation by activating


procedures | Discrete values | 1- sample test – events per unit (PD).

How to create a data source for a 1-sample p-test

Start | Create | Test procedures | Discrete values | 1-sample test – proportion (BD): The program


discrete (attribute) characteristic for comparing the parameter p of a population to a default

value (random variable of binomial distribution assumed). Start the evaluation by activating


procedures | Discrete values | 1- sample test – proportion (BD).

How to create a data source for a 2-sample µ-test

Start | Create | Test procedures | Discrete values | 2-sample test – events per unit (PD): The program


discrete (attribute) characteristics for comparing the parameter µ of two populations (random

variable of Poisson distribution assumed). Start the evaluation by activating the respective

data source in the Project Explorer and selecting Start | Evaluation | Test procedures | Discrete

values | 2- sample test – events per unit (PD).



How to create a data source for a 2-sample p-test (BD)

Start | Create | Test procedures | Discrete values | 2-sample test – proportions (BD): The program


discrete (attribute) characteristics for comparing the parameter p of two populations (random

variable of binomial distribution assumed). Start the evaluation by activating the respective

data source in the Project Explorer and selecting Start | Evaluation | Test procedures | Discrete

values | 2-sample test – proportions (BD).

How to create a data source for a k-sample chi-squared test (PD)

Start | Create | Test procedures | Discrete values | k-sample χ²-test – events per unit (PD): The program

generates a data source in the Analysis of Regression / Variance module including k=3

discrete (attribute) characteristics for comparing the parameter µ (random variable of Poisson

distribution assumed) of k populations. Start the evaluation by activating the respective data

source in the Project Explorer and selecting Start | Evaluation | Test procedures | Discrete values |

k-sample χ²-test – events per unit (PD).

How to create a data source for a test of homogeneity (PD)

Start | Create | Test procedures | Discrete values | k-sample χ²-test of homogeneity: The program


discrete (attribute) characteristics for comparing homogeneity of k populations. Start the

evaluation by activating the respective data source in the Project Explorer and selecting Start

| Evaluation | Test procedures | Discrete values | k-sample χ²-test of homogeneity.

How to create a k-sample chi-squared test (BD)

Start | Create | Test procedures | Discrete values | k-sample χ²-test - proportions: The program


discrete (attribute) characteristics for comparing the parameter p (random variable of

binomial distribution assumed) of k populations. Start the evaluation by activating the


Discrete values | k-sample χ²-test –proportions (BD).



2.1.9 Reliability Analysis option of the “Create“ group in the Start tab

How to create a data source for reliability analysis

Start | Create | Reliability analysis | Weibull: The program generates a data source in the

Reliability Analysis module including a continuous (variable) characteristic for lifetime

analysis.

Figure 2-40: Reliability analysis option

The program opens a dialog box that does not have any title. Enter the name of the

characteristic and select the requirements for cumulative frequencies.

Figure 2-41: Upper part of the window that does not have any title

Note: By selecting the requirements for cumulative frequencies, you are not provided automatically with the correct evaluation. You also have to select the correct evaluation strategy!


characteristic values.

In order to choose the correct evaluation strategy, select Start | Evaluation | Evaluation strategy.

The Evaluation dialog box opens.

Figure 2-42: Part of the Evaluation dialog box

In case you want to apply Eckel’s method, you have to select the Cumulative frequencies

based on Eckel‘s method evaluation strategy. However, for any other evaluation method, this

evaluation strategy is the wrong choice.



With or without failure-free time: In case the name of the evaluation strategy contains

“without failure-free time“, the respective evaluation strategy always applies a Weibull

distribution including two parameters – a scale and a shape parameter. All evaluation

strategies including “with failure-free time“ in their name always apply a Weibull distribution

with three parameters – a scale, a shape and an offset parameter.

Regression analysis or maximum likelihood: The term included in the name of the evaluation

strategy often refers to the estimation procedure estimating Weibull parameters. In case it

includes the word “regression“, it applies the method of least squares according to

Legendre/Gauss. Evaluation strategies containing “maximum likelihood“ apply the respective

estimation procedure developed by Sir R. A. Fisher.

Start the evaluation by activating the respective data source in the Project Explorer and

selecting Start | Evaluation | Test procedures.

2.2 “Evaluation“ group in the Start tab

Figure 2-43: “Evaluation“ group in the Start tab

2.2.1 Evaluation strategy option in the “Evaluation“ group of the Start tab

Start | Evaluation | Evaluation strategy: The Evaluation window opens. The contents of this

window depend on the currently active evaluation module and on the settings of the

respective evaluation strategy. Only users with administrator rights are able to change

an evaluation strategy or create a new one. Any other user just sees the settings but

cannot adjust them.

2.2.2 Standard procedure option in the “Evaluation“ group of the Start tab

Start | Evaluation | Standard procedure: This option evaluates all data sources of the following

modules.

Measurement System Analysis

o Type-1 to type-3 study, linearity, stability, kappa methods, signal detection

Sample Analysis

o 1-part and 5-part reports, machine capability analyses



Process Capability Analysis

o Process capability analyses, error log sheets, discrete characteristics

Reliability analysis

First you select the data source in the Project Explorer. Look at the title line of the program to see

the evaluation module of the data source. In case it is one of the modules listed above, select

Start | Evaluation | Standard procedure.

The program now appends a session node to the selected data source. It includes the initial

evaluation result.

Note: In case the data source already has a session node, you do not select the Standard procedure option again. Just click the session node to show the available window of results. If required, select the respective options from the Graphics and Results tabs to show further graphics and tables of results.

2.2.3 Distributions option in the “Evaluation“ group of the Start tab

Start | Evaluation | Distributions: This option opens the Distributions window showing the result of

the distributions the program selected automatically. The selection of distributions is normally

based on the set evaluation strategy and the program searches for the suitable distribution of

each characteristic automatically.

Note: When you open the window, the program does not show the distribution selected automatically based on the evaluation strategy since it shows Distributions (without offset) by default even though the Calculate best possible offset option might be activated in the evaluation strategy… We recommend you to close the Distributions window by clicking the Cancel button in order to avoid unintended changes of selected distributions.

Figure 2-44: Upper part of the Distributions window

The meaning of the single columns is explained in the following.



cal.: The distributions given in evaluation strategy are already activated (boxes are checked).

If required, you may activate further evaluation strategies manually; however, this only

affects the software temporarily.

curr.: The radio button of the distribution model selected automatically based on the

evaluation strategy is selected by default. You may select a different model – but only

temporarily.

Offset: In case there is an offset parameter available for the respective distribution, this

column shows its (estimated) value.

r100%: The “100% regression coefficient“ is a measure of goodness-of-fit between an

empirical distribution function (= “cumulative line“ of all characteristic values) and the

theoretical distribution function (mathematical distribution model). The closer the r-value to 1,

the better fits the theoretical distribution model with the characteristic values.

r25%: The “25% regression coefficient“ is a measure of conformance between the distribution

model and the first (0 to 25%) quartile or the fourth quartile (75% to 100%) of characteristic

values. Based on the Cpk calculation, the program decides which of the two quartiles will be

applied in each individual case. The software always selects the quartile lying next to the

critical specification limit.

²: This column indicates the chi-square test statistic of the chi-square goodness-of-fit test.

The smaller the test statistic, the better fits the model with the data – this is adequate

evidence for a good fit (theoretically expected and observed class frequencies match quite

well in case of small chi-square values). Attention: The test depends on the selected

classification model – if you select a different classification, you will have a different test

result…

Note: In case the Regression coefficient option is activated for the selection of distributions in the evaluation strategy, the program searches automatically for the best-suitable distribution model with the highest cumulative value = r100% + r25%.

2.2.4 Model f(x) | Correlation option in the “Evaluation“ group of the Start tab

Evaluation based on Pearson’s correlation

Start | Evaluation | Correlation | Pearson: This option starts a correlation analysis according to

Pearson to evaluate an existing data source.

Note: This option only evaluates data sources of the following modules.

Sample Analysis




Analysis of Regression / Variance

Select the data source you want to evaluate by click in the Project Explorer. The data source

has to contain at least two continuous (variable) characteristics with paired values.

Note: In case the number of values of the characteristics to be correlated differs, it is quite

likely that these characteristics do not include paired values.

In general, it is not reasonable to start a correlation analysis with characteristics whose

values are not paired. In such a case, you maybe should use a part ID or time stamp to filter

out characteristic values that can be paired.

The Selection of correlation variables dialog box opens.

Figure 2-45: Selection of correlation variables dialog box

Select the characteristics you want to correlate and confirm your choice. The Pearson dialog

box opens.

Figure 2-46: Pearson dialog box; the background colour of the results text depends on the actual value of the correlation coefficient r.



The Pearson dialog box contains a matrix of different graphics. The diagonal from the upper

left-hand corner to the lower right-hand corner shows the value plot of each characteristic.

The upper right-hand corner shows the scatter plot of all combinations of paired

characteristic values and the field in the lower left-hand corner displays the results. These

results include the following information.

r = Pearson’s correlation coefficient

a = Axis intercept of the best-fit line adapted to the pairs of values

b = Gradient of the best-fit line adapted to the pairs of values

Note: You may apply the options Correlation | Kendall and Correlation | Spearman in the same way as described above. Kendall’s and Spearman’s correlation calculate with ranks (ordinally scaled characteristic values); this is the reason why both types of analyses are often referred to as “distribution-free methods“. Compared to these methods, Pearson’s correlation coefficient, however, requires characteristics considered to be normally distributed random variables.

2.2.5 Model f(x) | Regression option in the “Evaluation“ group of the Start tab

Note: You cannot apply the content of additional data fields as factors in an analysis of

variance or as input quantities in a regression analysis.

How to evaluate simple regression

Start | Evaluation | Regression | Simple: This option starts a simple regression analysis to

evaluate a data source of the Analysis of Regression / Variance module containing at

least two continuous (variable) characteristics.

Select the data source you want to evaluate in the Project Explorer. Now select Start |

Evaluation | Model f(x) | Regression | Simple. The SelectGrid dialog box opens and shows the

Characteristics selection tab.

Figure 2-47: SelectGrid dialog box – Characteristics selection tab

This tab includes a table consisting of several columns.



Characteristic: Name of the characteristic

Characteristic type: Only shows variable characteristics of the data source.

N: Shows the number of values each characteristic contains.

Levels: Shows the number of numerically different characteristic values.

X: Select the characteristic you want to use as an input quantity.


Activate column X to use a characteristic as an input quantity X and highlight column Y to

define a characteristic as a response Y.

Open the Model tab to select a model.

Figure 2-48: SelectGrid dialog box – Model tab

Factor | characteristic: Lists the characteristic you defined as an input quantity

before.

Model: Shows the description of all calculated models

Regression function: Shows the functional equations of all calculated models

Regression plot: Shows a preview of each calculated model

R²: Shows the coefficient of determination of each calculated model

R²adj: Shows the corrected coefficient of determination of each calculated model

Select the desired model by click, e.g. the model with the highest corrected coefficient of

determination. Confirm your choice by clicking OK. The program creates a new session node

below the selected data source. You append a result node to this session node; it includes

the result of the regression analysis. The software shows the evaluation of the model based

on the analysis of variance by default.

Tip: You may show further graphics or tables of results for the regression analysis by selecting the result node and opening e.g.

Graphics | Linear model | Model graphics | Regression plot

Graphics | Linear model | Parameter tables | Coefficients

Graphics | Linear model | Diagnostics | Residuals



How to evaluate multiple regression

Start | Evaluation | Regression | Multiple: This option starts a multiple regression analysis to

evaluate a data source of the Analysis of Regression / Variance module containing at

least three continuous (variable) characteristics.


Evaluation | Model f(x) | Regression | Multiple.

The SelectGrid dialog box opens and shows the Characteristics selection tab.






Characteristic type: Only shows variable characteristics of the data source.

n: Shows the number of values each characteristic contains.


X: Select the characteristic you want to use as an input quantity.


Activate at least two characteristics in column X as influence quantities and one or several

characteristics as a response in column Y. Now you open the Model tab of this dialog box.

Note: After you selected two or more responses, the program calculates an own model for each response. Each model includes the same influence quantities X. The selected model you are going to adjust in the Model tab is initially the same for each response. If you want to select different model for the single responses, you can only do this after completing the current evaluation. You select Edit | Model selection.

After you opened the Model tab in the SelectGrid window, the program shows several buttons to

select a model.


Click on a button to select the corresponding model. Confirm your choice by clicking OK.

The program creates a new session node below the selected data source. You append a

result node for each response to this session node. The software shows the evaluation of the

model based on the analysis of variance by default.

Tip: You may show further graphics or tables of results for the regression analysis by selecting the result node and opening e.g.

Graphics | Linear model | Model graphics | Regression plot

Graphics | Linear model | Parameter tables | Coefficients




How to evaluate multivariate regression

Start | Evaluation | Regression | Multivariate: This option starts a multivariate regression analysis

to evaluate a data source of the Analysis of Regression / Variance module containing at

least three continuous (variable) characteristics.

Note: The characteristic values in a single line need to be paired values.


Evaluation | Model f(x) | Regression | Multivariate. The SelectGrid dialog box opens and shows the





Characteristic type: Measuring scale of characteristics


Levels: Shows the number of different characteristic values.

X: Select the characteristic you want to use as an influence quantity.


Activate two or more characteristics in column Y as a response and at least one characteristic

as a factor in column X. Now you open the Model tab of this dialog box.




Click on a button under “Standard models” to select the corresponding model. Confirm your

choice by clicking OK.

The program creates a new session node below the selected data source. This node is

called Reg by default. You may append several result nodes to this session node.

The Multivariate evaluation result node includes the Type III SS window showing the results of

the multivariate model evaluation. In addition, each response has an own result node

containing the results of simple or multiple regression analyses.

Figure 2-53: Type III SS window including the results of multivariate model evaluation

How to evaluate logistic regression

Start | Evaluation | Regression | Logistic: This option starts a logistic regression analysis to

evaluate a data source of the Analysis of Regression / Variance module. The data

source has to include at least

an ordinally scaled characteristic containing response values

a continuous (variable) characteristic containing factor values

a continuous characteristic containing frequencies.

Note: The characteristic values in a single line need to be paired values.


Evaluation | Model f(x) | Regression | Logistic. The SelectGrid dialog box opens and shows the









Levels: Shows the number of different characteristic values.

X: Select the characteristic you want to use as an influence quantity with fixed level

values (discrete, ordinally scaled factor).

V: Select the characteristic you want to use as a continuous (variable) influence

quantity.

Y: Select the characteristic you want to use as an ordinal response.

F: Select the characteristic you want to use to count frequencies.

Activate the respective characteristics in columns X to F and open the Model tab.

Click on a button under “Standard models” to select the corresponding model.


Open the Options tab.

Figure 2-56: SelectGrid dialog box –Options tab



Select the value of response Y you want to consider as an event. Confirm your choice by

clicking OK.

The program creates a session node. It is called LOG by default. A result node is appended

to the node. It has the name of the response. Click on the new result node and the

Summary/input window opens by default (even though you will not assume it from its name,

it really contains results).

Tip: You may show further graphics or tables of results for the logistic regression analysis by selecting the result node and opening e.g.

Graphics | Linear model | Parameter tables | Model information

Graphics | Linear model | Model graphics | Predicted probability

Graphics | Linear model | Model graphics | Odds ratios

Graphics | Linear model | Model graphics | ROC

2.2.6 Model f(x) | Analysis of Variance option in the “Evaluation“ group of the Start tab

Note: You cannot apply the content of additional data fields as factors in an analysis of variance or as input quantities in a regression analysis. The manual import of only few additional data fields is currently a poor function; it is not worth showing. Moreover, the manual import is rather annoying. Who wants to import additional data for 20 characteristics in the age of Big Data? We will seek a new user-friendly solution in the future.

How to evaluate simple or multiple analysis of variance (ANOVA)

Start | Evaluation | Model f(x) | Analysis of variance | ANOVA: This option starts a simple analysis

of variance including a data source from the Analysis of Regression / Variance module.

The data source has to contain at least a continuous (variable) characteristic and a discrete

(ordinal) characteristic. Please always consider that each line refers to the values of a

specific characteristic.

Select the respective data source in the Project Explorer and click Start | Evaluation | Model f(x) |

Analysis of variance | ANOVA.

The SelectGrid dialog box opens and shows the Characteristics selection tab. This tab includes a

table consisting of several columns.







X: Select the characteristic you want to use as a factor with a fixed level value.

Z: Select the characteristic you want to use as a factor with random level values


You clicked on column X of characteristic X to use this characteristic as a factor with fixed

level values in the following example. In addition, you clicked on column Y of characteristic Y

to use this characteristic as a response.


Open the Model tab to select a model. Choose the respective model by click and confirm your

choice by clicking OK.


After you confirmed your choice, the program appends a session node called GLM to the

evaluated data source. In addition, this session node includes a result node with the name of

the evaluated response. Select this node to see the evaluation result of the analysis of

variance.

Tip: You may show further graphics or tables of results for the current analysis of variance by selecting the result node and opening e.g.

Graphics | Linear model | Parameter tables | Model evaluation

Graphics | Linear model | Parameter tables | LS-Means

Graphics | Linear model | Parameter tables | Pairwise differences

Graphics | Linear model | Parameter tables | Expected mean squares



Graphics | Linear model | Model graphics | Main effect plots


How to evaluate an analysis of covariance (ANCOVA)

Start | Evaluation | Model f(x) | Analysis of variance | ANCOVA: This option starts an analysis of

covariance including a data source from the Analysis of Regression / Variance module.

The data source has to contain at least two continuous (variable) characteristics and a

discrete (ordinal) characteristic. Please always consider that each line refers to the values of

a specific characteristic.


Analysis of variance | ANCOVA.








V: Select a continuous (variable) characteristic you want to use as a covariable.

Y: Select a continuous (variable) characteristic you want to use as a response.

The following figure shows an example of model selection. The ordinal characteristic A was

selected as a factor with fixed levels. The continuous (variable) characteristic B serves as a

covariable and the continuous (variable) characteristic Y is the response. Now you open the

Model tab.


The Model tab in the SelectGrid dialog box includes several buttons for model selection. Select

the respective standard model and confirm your choice.




After you confirmed the respective model, the Type III (adj.) dialog box opens showing the

model’s evaluation results of the analysis of variance.

The program appends a session node called GLM to the evaluated data source in the Project

Explorer. In addition, this session node includes a result node with the name of the evaluated

response. Select this node to see the evaluation result of the analysis of variance.








How to evaluate a multivariate analysis of variance (MANOVA)

Start | Evaluation | Model f(x) | Analysis of variance | MANOVA: This option starts a multivariate

analysis of variance including a data source from the Analysis of Regression /

Variance module. The data source has to contain at least two continuous (variable)

characteristics and a discrete (ordinal) characteristic.

Note: Please always consider that each line refers to the values of a specific characteristic.




Analysis of variance | MANOVA.








V: Select a characteristic you want to use as a continuous (variable) covariable.



Select the respective characteristics as response or factor and open the Model tab to select

a model.


The Model tab in the SelectGrid dialog box includes several buttons for model selection. Select

the respective standard model and confirm your choice.

Tip: Activate the Extended radio button under “Input“ to display the input field under “Advanced model input“. Use this field to modify the formula of the model you selected before.

Symbol Meaning Example

+ Additive main effects A + B

* Multiplicative interaction A + B + A * B



() Nested factors A + B(A)

[] Crossed factors A + B + [A*B](C) Table 2-4: Symbols used in the advanced model input

The program appends a session node called GLM to the evaluated data source in the Project

Explorer. In addition, this session node includes a result node with the name of the evaluated

response.








How to evaluate repeated measures ANOVA

Start | Evaluation | Model f(x) | Analysis of variance | Repeated measures: This option starts a

repeated measures ANOVA including a data source from the Analysis of Regression /

Variance module.

Note: Please always consider that each line refers to the values of a specific characteristic.


Analysis of variance | Repeated measures.







X: Select the characteristic you want to use as a factor with fixed level values.

V: Select a characteristic you want to use as a covariable.

R: Select a characteristic you want to use as a repeated measure variable.




The following figure shows an exemplary selection of characteristics in the SelectGrid dialog

box.

Figure 2-63: SelectGrid dialog box – Characteristics selection tab; here you select the characteristics for factors and responses in column X (factor with fixed level values), V (covariable), R (repeated measure) and Y (response)

After you defined factors and responses, you open the Model tab.

Figure 2-64: SelectGrid dialog box –Model tab; click the respective button to select a model

The Model tab opens. It includes several buttons for model selection under “Standard models“.

Select the respective standard model and confirm your choice. The program appends a

session node called GLM to the evaluated data source in the Project Explorer. In addition,

this session node includes a result node with the name of the evaluated response.

Select the result node to show the evaluation results of repeated measures ANOVA.


Graphics | Linear model | Parameter tables | Mauchlys tests

Graphics | Linear model | Parameter tables | Type I SS



2.2.7 Design option in the “Evaluation“ group of the Start tab

Start | Evaluation | Design: This option evaluates the available design of experiments. First you

select a data source including an experimental design (including response values!) in the

Project Explorer.

Note: The design of experiments has to be created in destra. You cannot import an experimental design from any other software product.

This option generates a new session node called DOE in the Project Explorer. In addition,

the program appends an own result node to the session node for each response available in

the design of experiments. The descriptions of the result nodes refer to the names of the

responses.

Tip: The program provides the Table of effects and Model selection options in the “Model” group of the Edit tab. They specifically apply to regression and design models. Use these options to delete significant model terms from the regression model or to adapt models to user-specific requirements.

2.2.8 Test procedures option in the “Evaluation“ group of the Start tab

Test procedures for continuous characteristic values

Figure 2-65: Test procedures | Continuous values



How to evaluate a 1-sample t-test (normal distribution: parameter µ)

Start | Evaluation | Test procedures | Continuous values | 1-sample t-test (location: µ): This option

starts a 1-sample t-test based on an available data source; this type of test compares

location µ to a default value. The selected data source has to be available in one of the

following modules.

Sample Analysis



Tip: After you selected a data source in the Project Explorer, the title line shows the module of this data source.

First you activate the data source to be evaluated by click. Then select Start | Evaluation | Test

procedures| Continuous values | 1-sample t-test (location µ).

The Assistant (test procedure) window opens. Click on t-test 1.

Figure 2-66: Assistant (test procedure) window – Selection tab; click on t-test 1

The Data tab of the Assistant (test procedure) window opens. Select the characteristic you want

to evaluate by click.



Figure 2-67: Assistant (test procedure) window – Data tab; select the continuous (variable) characteristic you want to evaluate by clicking on the active column in the respective line

Click the Next button (green circle with the arrow pointing to the right).

Figure 2-68: Assistant (test procedure) window – Planning tab; take the next step by clicking the Next button (green circle with arrow to the right)

Take the next step by clicking the Next button. The Test tab opens.



Figure 2-69: Assistant (test procedure) window – Test tab; enter a target value for the average µ0, activate the hypothesis to be tested and click the „calculator“ icon to calculate results

First you enter the specified value µ0 and select the hypothesis to be tested. Now you click the

“calculator“ icon on the left-hand side of the window (not in the ribbon!).

The window shows the test result. You can find the Assistant (test procedures) window by

default in the Test Stat session node of the Project Explorer. The program appended the

session node automatically to the evaluated data source.

How to evaluate a 1-sample chi-squared test (normal distribution: parameter sigma)

Start | Evaluation | Test procedures | Continuous values | 1-sample χ²-test (variance): This option starts

a chi-squared test based on an available data source; this type of test compares variance

sigma to a default value. The selected data source has to be available in one of the following

modules.

Sample Analysis



Tip: After you selected a data source in the Project Explorer, the title line shows the module of this data source.


procedures| Continuous values | 1-sample χ²-test (variance).



The Assistant (test procedure) window opens. Click on χ²-test.

Figure 2-70: Assistant (test procedure) window – Selection tab; click on χ²-test



Figure 2-71: Assistant (test procedure) window – Data tab; select the characteristic you want to evaluate by clicking on the active column in the respective line

Click the Next button (green circle with the arrow pointing to the right) twice since you do not

have to make any adjustments in the Planning tab. The Test tab opens. First you enter the

specified value (target value) serving as a default value for the standard deviation 0 and select

the hypothesis to be tested. Now you click the “calculator“ icon on the left-hand side of the

window (not in the ribbon!).



Figure 2-72: Assistant (test procedure) window – Test tab; enter a target value for the standard deviation, activate the hypothesis to be tested and click the „calculator“ icon

The program created a session node automatically in the Project Explorer. This node is referred

to as Test Stat by default. The program always appends the session node to the evaluated

data source.

How to evaluate the Wilcoxon test (median)

Start | Evaluation | Test procedures | Continuous values | 1-sample Wilcoxon test (location: median):

This option starts a 1-sample Wilcoxon test based on an available data source; this type of

test compares the median of a population to a default value. The selected data source has to

be available in one of the following modules.

Sample Analysis




procedures| Continuous values | 1-sample Wilcoxon test (location: median).

The Assistant (test procedure) window opens. Click on 1-sample Wilcoxon signed-rank test.



Figure 2-73: Assistant (test procedure) window – Selection tab; click on 1-sample Wilcoxon signed-rank test



Figure 2-74: Assistant (test procedure) window – Data tab; select the characteristic you want to evaluate by clicking on the active column in the respective line

Click the Next button (green circle with the arrow pointing to the right). The Test tab opens. First

you enter the target value of the median and select the hypothesis to be tested. Now you click

the “calculator“ icon on the left-hand side of the window (not in the ribbon!).



Figure 2-75: Assistant (test procedure) window – Test tab; enter a target value for the median, activate the hypothesis to be tested and click the ”calculator“ icon to calculate results



data source.

How to evaluate the 2-sample t-test (normal distribution: parameter µ, independent samples)

Start | Evaluation | Test procedures | Continuous values | 2-sample t-test (location: µ): This option

starts a 2-sample t-test based on an available data source; this type of test compares the

locations µ of two populations. The selected data source has to be available in one of the

following modules.

Sample Analysis




procedures| Continuous values | 2-sample t-test (location: µ).

The Assistant (test procedure) window opens. Click on t-test 2 in the Selection tab.



Figure 2-76: Assistant (test procedure) window – Selection tab; click on t-test 2

The Data tab of the Assistant (test procedure) window opens. Select the characteristics you want

to compare by click on the active column. The following figure shows an example.

Figure 2-77: Assistant (test procedure) window – Data tab; select the characteristics you want to compare by clicking on the active column in the respective two lines

Click the Next button (green circle with the arrow pointing to the right). The Planning tab opens

but you do not have to make any adjustments here. Click the Next button once again to open

the Test tab. First you select the hypothesis to be tested. Now you click the “calculator“ icon on

the left-hand side of the window (not in the ribbon!).



Figure 2-78: Assistant (test procedure) window – Test tab; activate the hypothesis to be tested and click the “calculator“ icon to calculate results



data source.

How to evaluate a 2-sample F-test (normal distribution: sigma)

Start | Evaluation | Test procedures | Continuous values | 2-sample F-test: This option starts a 2-

sample F-test; it tests the equality of variances σ of two populations. The selected data

source has to be available in one of the following modules.

Sample Analysis




procedures| Continuous values | 2-sample F-test.

The Assistant (test procedure) window opens. Click on F-test in the Selection tab.



Figure 2-79: Assistant (test procedure) window – Selection tab; click on F-test


to compare by click on the active column.




the Test tab. First you select the hypothesis to be tested. Now you click the “calculator“ icon on

the left-hand side of the window (not in the ribbon!).



Figure 2-81: Assistant (test procedure) window – Test tab; activate the hypothesis to be tested and click the ”calculator“ icon to calculate results



data source. The window shown above is the content of this session node.

How to evaluate a 2-sample Mann-Whitnex U-test (median)

Start | Evaluation | Test procedures | Continuous values | 2-sample Mann-Whitnex test (location:

median): This option starts a 2-sample Mann-Whitnex U-test; it tests the equality of medians

of two populations. The selected data source has to be available in one of the following

modules.

Sample Analysis




procedures| Continuous values | 2-sample Mann-Whitnex test (location: median).

The Assistant (test procedure) window opens. Click on U-test in the Selection tab.



Figure 2-82: Assistant (test procedure) window – Selection tab; click on U-test





you select the hypothesis to be tested. Now you click the “calculator“ icon on the left-hand side

of the window (not in the ribbon!).






data source.

How to evaluate a 2-sample rank test for dispersions (dispersion)

Start | Evaluation | Test procedures | Continuous values | 2-sample rank test for dispersions

(dispersion): This option starts a 2-sample rank test for dispersions based on an available data

source; it tests the equality of medians of two populations. The selected data source has to

be available in one of the following modules.

Sample Analysis




procedures| Continuous values | 2-sample rank test for dispersions (dispersion).

The Assistant (test procedure) window opens. Click on Siegel Turkey test in the Selection tab.



Figure 2-85: Assistant (test procedure) window – Selection tab; click on Siegel Turkey test





you select the hypothesis to be tested. Now you click the “calculator“ icon on the left-hand side

of the window (not in the ribbon!).






data source.

How to evaluate a simple analysis of variance (normal distribution: parameter µ)

Start | Evaluation | Test procedures | Continuous values | k-sample analysis of variance (location: µ):

This option starts a simple analysis of variance based on an available data source; it tests

the homogeneity of locations of k populations. The selected data source has to be available

in one of the following modules.

Sample Analysis




procedures| Continuous values | k-sample analysis of variance (location: µ).

The Assistant (test procedure) window opens. Click on One-way ANOVA in the Selection tab.



Figure 2-88: Assistant (test procedure) window – Selection tab; click on one-way ANOVA



Figure 2-89: Assistant (test procedure) window – Data tab; select the characteristics you want to compare by clicking on the active column in the respective lines

Click the Next button (green circle with the arrow pointing to the right). The Test tab opens.

Click the “calculator“ icon on the left-hand side of the window (not in the ribbon!).

Figure 2-90: Assistant (test procedure) window – Test tab; click the ”calculator“ icon to calculate results





data source.

How to evaluate a k-sample Bartlett test (normal distribution: parameter σ)

Start | Evaluation | Test procedures | Continuous values | k-sample Bartlett test (variance: σ): This

option starts a Bartlett test based on an available data source; it tests the homogeneity of

variance σ of k populations. The selected data source has to be available in one of the

following modules.

Sample Analysis




procedures| Continuous values | k-sample Bartlett test (variance: σ).

The Assistant (test procedure) window opens. Click on Bartlett test in the Selection tab.

Figure 2-91: Assistant (test procedure) window – Selection tab; click on Bartlett test











data source.

Howe to evaluate a k-sample Kruskal-Wallis test (location: median)

Start | Evaluation | Test procedures | Continuous values | k-sample Kruskal-Wallis test (location:

median): This option starts a Kruskal-Wallis test based on an available data source; it tests

the homogeneity of location of k populations. The selected data source has to be available in

one of the following modules.

Sample Analysis




procedures| Continuous values | k-sample Kruskal-Wallis test (location: median).

The Assistant (test procedure) window opens. Click on H-test Kruskal-Wallis in the Selection tab.



Figure 2-94: Assistant (test procedure) window – Selection tab; click on H-test Kruskal-Wallis











data source.

How to evaluate a k-sample Levene test (variance)

Start | Evaluation | Test procedures | Continuous values | k-sample Levene test (variance): This option

starts a Levene test based on an available data source; it tests the homogeneity of variance

of k populations. The selected data source has to be available in one of the following

modules.

Sample Analysis




procedures| Continuous values | k-sample Levene test (variance).

The Assistant (test procedure) window opens. Click on Levene test in the Selection tab.

Figure 2-97: Assistant (test procedure) window – Selection tab; click on Levene test










data source.



Test procedures for discrete values

Figure 2-99: Test procedures | Discrete values

How to evaluate a 1-sample µ-test (Poisson distribution: parameter µ)

Start | Evaluation | Test procedures | Discrete values | 1-sample test – events per unit (PD): This option

starts a one-sample test based on an available data source; it compares parameter µ of a

population based on a Poisson distribution to a default value. The selected data source has

to be available in one of the following modules.




procedures| Discrete values | 1-sample test – events per unit (PD).

The Assistant (test procedure) window opens. Click on p-test (PD) 1 in the Selection tab.

Figure 2-100: Assistant (test procedure) window – Selection tab; click on Levene test




to check by click on the active column.

Figure 2-101: Assistant (test procedure) window – Data tab; select the characteristic you want to check by clicking on the active column in the respective line



the Test tab. First you enter the target value for parameter µ0 of the population based on a

Poisson distribution. Then you select the hypothesis to be tested and click the “calculator“ icon

on the left-hand side of the window (not in the ribbon!) to gain the test results.

Figure 2-102: Assistant (test procedure) window – Test tab; enter a default value for parameter µ0, activate the hypothesis to be tested and click the ”calculator“ icon to calculate results



data source.



How to evaluate a 1-sample p-test (binomial distribution: parameter p)

Start | Evaluation | Test procedures | Discrete values | 1-sample test – proportion (BD): This option

starts a one-sample p-test based on an available data source; it compares parameter p of a

binomially distributed population to a default value p0. The selected data source has to be

available in one of the following modules.




procedures| Discrete values | 1-sample test – proportion (BD).

The Assistant (test procedure) window opens. Click on p-test (BD) 1 in the Selection tab.

Figure 2-103: Assistant (test procedure) window – Selection tab; click on p-test (BD) 1


to check by click on the active column.

Figure 2-104: Assistant (test procedure) window – Data tab; select the characteristic you want to check by clicking on the active column in the respective line



the Test tab. First you enter the default value p0 of the population. Then you select the

hypothesis to be tested and click the “calculator“ icon on the left-hand side of the window (not

in the ribbon!) to start the evaluation.



Figure 2-105: Assistant (test procedure) window – Test tab; enter a default value p0, activate the hypothesis to be tested and click the ”calculator“ icon to calculate results



data source.

How to evaluate a 2-sample µ-test (Poisson distribution: parameter µ)

Start | Evaluation | Test procedures | Discrete values | 2-sample test – events per unit (PD): This option

starts a 2-sample test based on an available data source; it test the homogeneity of µ of two

populations. The selected data source has to be available in one of the following modules.




procedures| Discrete values | 2-sample test – events per unit (PD).

The Assistant (test procedure) window opens. Click on p-test (PD) 2 in the Selection tab.



Figure 2-106: Assistant (test procedure) window – Selection tab; click on p-test (PD) 2


to compare by click on the respective active columns.


Click the Next button (green circle with the arrow pointing to the right). The test tab opens. First

you select the hypothesis to be tested and click the “calculator“ icon on the left-hand side of the







data source.

How to evaluate a 2-sample p-test (binomial distribution: parameter p)

Start | Evaluation | Test procedures | Discrete values | 2-sample test – proportions (BD): This option

starts a 2-sample test based on an available data source; it test the homogeneity of the

populations’ proportions. The selected data source has to be available in one of the following

modules.




procedures| Discrete values | 2-sample test – proportions (BD).

The Assistant (test procedure) window opens. Click on p-test (BD) 2 in the Selection tab.

Figure 2-109: Assistant (test procedure) window – Selection tab; click on p-test (BD) 2


to compare by click on the respective active column.




Click the Next button (green circle with the arrow pointing to the right). The test tab opens. First

you select the hypothesis to be tested and click the “calculator“ icon on the left-hand side of the





data source.

How to evaluate a k-sample chi-squared test (Poisson distribution: parameter µ)

Start | Evaluation | Test procedures | Discrete values | k-sample χ²-test – events per unit (PD): This

option starts a chi-squared test based on an available data source; it test the homogeneity of

the k populations’ parameter µ. The selected data source has to be available in one of the

following modules.




procedures| Discrete values | k-sample χ²-test – events per unit (PD).

The Assistant (test procedure) window opens. Click on χ²-test in the Selection tab.







Click the Next button (green circle with the arrow pointing to the right). The test tab opens. You

click the “calculator“ icon on the left-hand side of the window (not in the ribbon!).






data source.

How to evaluate a k-sample χ²-test (Poisson distribution: parameter µ)

Start | Evaluation | Test procedures | Discrete values | k-sample χ²-test of homogeneity: This option

starts a test of homogeneity developed by Sir. R. A. Fisher; it tests the homogeneity of the k

populations’ parameter µ. The selected data source has to be available in one of the

following modules.




procedures| Discrete values | k-sample χ²-test of homogeneity.

The Assistant (test procedure) window opens. Click on χ² homogeneity test in the Selection tab.

Figure 2-115: Assistant (test procedure) window – Selection tab; click on χ² homogeneity test







click the “calculator“ icon on the left-hand side of the window (not in the ribbon!) to calculate

the result.




data source.

How to evaluate a k-sample χ²-test (binomial distribution: parameter p)

Start | Evaluation | Test procedures | Discrete values | k-sample χ²-test – proportions (BD): This option

starts a k-sample χ²-test; it tests the homogeneity of the k populations’ parameter p. The

selected data source has to be available in one of the following modules.




procedures| Discrete values | k-sample χ²-test – proportions (BD).

The Assistant (test procedure) window opens. Click on χ²-test in the Selection tab.








click the “calculator“ icon on the left-hand side of the window (not in the ribbon!) to calculate

the result.






data source.



3. Index µ-test (1 sample)

create ................................................ 33 evaluate ............................................. 76

µ-test (2 samples) create ................................................ 33

µ-test (2 samples) evaluate ............................................. 79

1-part report create ................................................ 15 evaluate ............................................. 36

5-part report create ................................................ 16 evaluate ............................................. 36

Analysis of covariance (ANCOVA) create ................................................ 23 evaluate ............................................. 49

Analysis of variance (ANOVA) create ................................................ 22 evaluate ............................................. 47

Analysis of variance (test) create ................................................ 31

Bartlett test create ................................................ 31 evaluate ............................................. 71

Chi-squared test (µ) create ................................................ 34 evaluate ............................................. 82

Chi-squared test (p) create ................................................ 34

Chi-squared test (sigma) create ................................................ 29 evaluate ............................................. 57

Correlation create ................................................ 18 evaluate ............................................. 39

Design augment ............................................. 28 create ................................................ 26 evaluate ............................................. 54 reuse ................................................. 27

Discrete characteristic create ................................................ 17 evaluate ............................................. 36

Distributions .......................................... 37 Error log sheet

create ................................................ 17 evaluate ............................................. 36

Evaluation strategy ................................ 36 F-test

create ................................................ 30 evaluate ............................................. 63

Kappa coefficient create ................................................ 11

Kappa method

evaluate .............................................36 Kruskal-Wallis H-test

create .................................................31 evaluate .............................................72

Levene test create .................................................32 evaluate .............................................74

Linearity create .................................................10 evaluate .............................................36

Logistic regression create .................................................21 evaluate .............................................45

Machine capability analysis create .................................................14 evaluate .............................................36

Machine capability positional tolerance create .................................................18

Machine capability positional tolerances evaluate .............................................36

Mann-Whitney U-test create .................................................30 evaluate .............................................65

Multiple regression create .................................................20 evaluate .............................................42

Multivariate analysis of variance (MANOVA) create .................................................24 evaluate .............................................51

Multivariate regression create .................................................20 evaluate .............................................44

New Create project phase ........................... 7 Create work space .............................. 7

Process capability analysis create .................................................15 evaluate .............................................36

p-test (1 sample) create .................................................33 evaluate .............................................78

p-test (2 samples) create .................................................34 evaluate .............................................81

Rank test for dispersions create .................................................31 evaluate .............................................67

Repeated measures ANOVA create .................................................25 evaluate .............................................52

Signal detection create .................................................11 evaluate .............................................36



Simple analysis of variance evaluate ............................................. 69

Simple regression create ................................................ 19 evaluate ............................................. 41

Stability create .................................................. 9 evaluate ............................................. 36

Test of homogeneity create ................................................ 34 evaluate ............................................. 84

t-test (1 independent samples) create ................................................ 30

t-test (1 paired samples) create ................................................ 30

t-test (1 sample) create ................................................ 29 evaluate ............................................. 55

t-test (2 independent samples) evaluate ............................................. 61

Type-1 study

create .................................................. 8 evaluate .............................................36

Type-1 study 2nd true position create .................................................13

Type-1 study positional tolerance evaluate .............................................36

Type-2 study create .................................................. 8 evaluate .............................................36

Type-3 study create .................................................. 9 evaluate .............................................36

Weibull analysis create .................................................35 evaluate .............................................36

Wilcoxon test create .................................................29 evaluate .............................................59

χ²-test (p) evaluate .............................................85

Commands in destra v11 - Q-DAS

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