statistika bisnis chapter 1

8/11/2019 statistika bisnis chapter 1

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Business Statistic: Chapter 1The Where, Why, and How ofData Collection

Prepared by:Renna Magdalena


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Tools of Business Statistics

Descriptive procedures

Collecting, presenting, and describing data

Inferential procedures

Drawing conclusions and/or makingdecisions concerning a population based

only on sample data

Goal: Convert data into meaningful information!


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Descriptive Procedures

Collect data

e.g., Survey, Observation,

Experiments Present data

e.g., Charts and graphs

Characterize data

e.g., Sample mean =n

xi


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Making statements about a population by examiningsample results

Sample statistics Population parameters(known)

Inference unknown, but canbe estimated fromsample evidence

Sample Population

Inferential Procedures


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Inferential Statistics

Estimation

e.g., Estimate the populationmean weight using the samplemean weight

Hypothesis Testing e.g., Use sample evidence to

test the claim that thepopulation mean weight is 120pounds

Drawing conclusions and/or making decisions concerning apopulation based on sample results.


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Procedures for Collecting Data

Data Collection Procedures

Writtenquestionnaires

Experiments

Telephonesurveys

Direct observation andpersonal interview


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Survey Design Steps

Define the issue What are the purpose and objectives of the

survey?

How will the survey be administered? (e.g. phone, email, face to face)

Define the population of interest

Develop survey questions Make questions clear and unambiguous Use universally-accepted definitions Limit the number of questions


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Survey Design Steps

Pre-test the survey

Pilot test with a small group ofparticipants

Assess clarity and length

Determine the sample size andsampling method

Select sample and administer thesurvey

(continued)


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Types of Questions

Closed-end Questions Select from a short list of defined choices

Example: Major: __business __liberal arts__science __other

Open-end Questions Respondents are free to respond with any value, words, or

statement

Example: What did you like best about this course?

Demographic Questions Questions about the respondents personal characteristics

Example: Gender: __Female __ Male


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Observations and Interviews

Observations Data collected is observed and recorded

based on what takes place

Very subjective Example: Observe reactions of customersto a new store layout

Interviews Can be structured fixed set of questions Can use a variety of questions Requires more time from the researcher


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Data Collection Pitfalls

Interview bias

Non response

Selection bias Observer bias

Measurement error

Internal/External validity

The objective is to collect accurate and reliable data!
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A Population is the set of all items orindividuals of interest

Examples: All likely voters in the next electionAll parts produced todayAll sales receipts for November

A Sample is a subset of the population Examples: 1000 voters selected at random for interview

A few parts selected for destructive testingEvery 100th receipt selected for audit

Populations and Samples


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Key Definitions

A population is the entire collection of things underconsideration and referred to as the frame

The sampling unit is each object or individual in the frame

A parameter is a summary measure computed to describea characteristic of the population

A sample is a subset of the population selected foranalysis

A statistic is a summary measure computed to describe acharacteristic of the sample drawn from the population


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Population vs. Sample

a b c d

ef gh i jk l m n

o p q rs t u v w

x y z

Population Sample

b c

g i n

o r u

y


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Why Sample?

Less time consuming than a census

Less costly to administer than acensus

It is possible to obtain statistical

results of a sufficiently highprecision based on samples

Strive for representative samples to reflect the

population of interest accurately!


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Sampling Techniques

Convenience

Sampling Techniques

Nonstatistical Sampling

Judgment

Statistical Sampling

Simple

Random

Systematic

StratifiedCluster


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Nonstatistical Sampling

Convenience Collected in the most convenient manner

for the researcher

Judgment Based on judgments about who in thepopulation would be most likely to provide

the needed information


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Statistical Sampling

Items of the sample are chosen basedon known or calculable probabilities

Statistical Sampling(Probability Sampling)

SystematicStratified ClusterSimple Random


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Simple Random Sampling

Every possible sample of a given sizehas an equal chance of being selected

Selection may be with replacement orwithout replacement

The sample can be obtained using a

table of random numbers or computerrandom number generator


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Stratified Random Sampling

Divide population into subgroups (called strata)according to some common characteristic

e.g., gender, income level

Select a simple random sample from each subgroup

Combine samples from subgroups into one

Population

Divided

into 4

strata

Sample


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Decide on sample size: n

Divide ordered (e.g., alphabetical) frame of Nindividuals into groups of k individuals: k=N/n

Randomly select one individual from the 1st group Select every kth individual thereafter

Systematic Random Sampling

N = 64

n = 8

k = 8First Group


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Cluster Sampling

Divide population into several clusters,each representative of the population (e.g.,county)

Select a simple random sample of clusters All items in the selected clusters can be used, or items can

be chosen from a cluster using another probability samplingtechnique

Population divided

into 16 clusters.

Randomly selected

clusters for sample


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Data Types

Data

Qualitative

(Categorical)

Quantitative

(Numerical)

Discrete Continuous

Examples:

Marital Status Political Party Eye Color

(Defined categories) Examples:

Number of Children Defects per hour

(Counted items)

Examples:

Weight Voltage

(Measured

characteristics)


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Data Types

Time Series Data Ordered data values observed over

time

Cross Sectional Data Data values observed at a single point in

time


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Data Types

Sales (in $1000s)

2009 2010 2011 2012

Atlanta 435 460 475 490Boston 320 345 375 395

Cleveland 405 390 410 395

Denver 260 270 285 280

TimeSeriesData

Cross SectionalData


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Data Measurement Levels

Ratio/Interval Data

Ordinal Data

Nominal Data

Highest Level

Complete Analysis

Higher Level

Mid-level Analysis

Lowest Level

Basic Analysis

Categorical Codese.g., ID Numbers, gender

RankingsOrdered Categoriese.g., age range 25-34

Measurementse.g., temperature


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Steps to Categorizing Data

1. Identify each factor in the data set2. Determine if the data are time-series

or cross-sectional3. Determine which factors are

quantitative and which are qualitative

4. Determine the level of datameasurement

e.g., nominal, ordinal


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Chapter Summary

Reviewed key data collection methods

Introduced key definitions:Population vs. Sample Primary vs. Secondary data types

Qualitative vs. Quantitative data Time Series vs. Cross-Sectionaldata

Examined descriptive vs. inferential

procedures Described different sampling techniques

Reviewed data types and measurement levels


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THANK YOU.

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