The Impact of Health Related Problems on

Academic Achievements of Second and Third Year

Biology Department Students, In Wolaita Sodo

University.

Prepared By: Alela Miesa

Ararso Ditta

Sofiyan Osman

Advisor: Muzeyin Ahmed (MSc.)

Submitted to: Wolaita Sodo University College of

Natural and Computational

Sciences, Department of

Statistics.

In Partial Fulfillment of the Requirement for Degree of

Bachelor of Science in Statistics

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Wolaita Sodo, Ethiopia

May, 2014

2

ACRONOMY

CGPA Cumulative Gpa

MOH Minister of Health.

SSR Sum of Square of Regression.

SSE Sum Square of Error.

SST Sum Square of Total.

MSE Mean Square of Error.

MST Mean Square of Treatement.

ANOVA

i

AcknowledgementOur first and greatest thanks go to the almighty God for this

goodness and mercy that makes us to be alive and this all helps

at moment in our entry life journey.

Next we would like to express our heartfelt thanks to our advisor

Muzeyin A (MSc) who provided us within valuable and consistent

advices, constructive correction and suggestion at any time until

to the end of this paper. As well as he made us to have more

opportunity to realize the concept of research deeply and

extensively in the left of our future life.

We are deeply indebted to all our in formants and respondents who

spends their time for giving data responsibly.

Last but not least, our heartiest appreciation goes to our family

for their support in all our life and also for the financial

support to conduct the research. We would like to extend our

appreciation and thanks to our brother and Sister made for their

encouragement and moral support.

ii

iii

Abstract

Most of the time students are affected by different disease like

malaria, typhoid, diarrhea, gastric and common cold which is

directly affected their academic achievement. The purpose of this

study was to understand impact of health related problems on

academic achievement of students. It was initiated especially to

identify which factor was highly affected for the academic

achievement. For this study secondary data was used to conduct

this research and this secondary data was taken from register and

students clinic. To analysis the investigation we use the

statistical method analysis such as: - Descriptive statistics,

chi-square, inferential statistics, and multiple linear

regressions were used. The result of this study shows that

malaria, common cold, typhoid, sex, diarrhea, gastric and tension

are significantly affected the results of the students. From the

analysis the result implies that health related problems and CGPA

of the students are indirectly relationship. Impact on academic

achievement of students, to reduce both government and the

university had better to work to avoid those problems.

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Table of ContentsContents

Page

Acknowledgement……………………………………………………………………….ii

Abstract…………………………………………………………………………………..iii

1. Introduction ………………………………………………………………………… ..1

1.1. Background of the Study……………………………………………………………. 1

1.2 Statement of the Problem………………………………………………………...........2

1.3 Objectives of the Study……………………………………………………………. 2

1.3.1 General Objective…………………………………………………………………2

1.3.2 Specific Objective.........………………………………………………………………2

1.4 Significance of the study…………………………………………………………....3

1.5 Limitations of the study……………………………………………………………..3

2. Literature Review............…………………………………………………………………4

v

2.1 The association between Academic Achievement and Health

.................................................……………………4

2.2. External Factors which affects Academic Achievement of

Student……………….5

3. Methodology.….........................................….6

3.1. Description of Study Area............................6

3.2. Description of the data......……………………………………………………………6

3.3 Variables included in the study……………………………………………………..6

3.3.1 Dependent variable………………………………………………………………..6

3.3.2 Independent variables..............................6

3.4. Sampling design and Sample Size Determination.......7

3.5. Statistical Methods.................................7

3.5.1 Descriptive Statistics...........................7

3.5.2 Inferential Statistics……………………………………………………………...7

3.5.1. Multiple linear regression models ................8

3.5.2 Hypothesis testing ………………………………………………………………8

3.6 Model Diagnosis…………………………………………………………………..10

4 Results and Discussions…………………………………………………………….11

4.1 Descriptive Statistics and Bivariate

Analysis.......................................................11

4.2 Result of multiple linear

regressions…………………………………………….... 12

4.3 Variable selection………………………………………………………………....13

4.4 Model Adequacy checking………………………………………………………..15

4.5 Discussion………………………………………………………………………..16

5 Conclusion &Recommendation……………………………………………………..17

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5.1 conclusions……………………………………………………………………….17

5.2 Recommendations……………………………………………………………….17

Reference………………………………………………………………………..18

Appendix………………………………………………………………………

vii

1 INTRODUCTION

1.1. Background

Most of the time students are affected by different disease like

malaria, typhoid, and common cold. Not all students are able to

succeed in the university, and that certain groups of students

are consistently less likely to have success than others

(MOH,009) Guide line to malaria control program, Addis Ababa, pp

17-19,21-22).Students’ nation united health behaviors and

educational challenges may influence each other.

Based on seeming under recognized evidence over all finding is

that there is reason to believe health does have an impact on the

academic achievement of students. Education and health are known

to be highly correlated, that is more indicates better health and

vice verse. The effect of health on academic achievement has been

well researched in developing countries. Students in university

faced more series challenges (miller, 2009).

For the past decade university has under stood and acted the need

to dramatically increase students’ academic achievement. In

recent year university has placed strong focus on the academic

achievement gab works across traditional jurisdiction to

determine how ,when and where to effectively and efficiently

provide the educational /learn supports and services students

need; and pay closer attention to how university can create more

caring culturally relevant and engaging environments for all

students. (Hillman.CH, MB.pontifex and Remer, 2009).

1

Students have the best chance to succeed when they are healthy.

Health in this context includes nutritious died, physical

activity, emotional well being satisfy security, the absence of

chronic condition such as malaria ,common cold, typhoid etc.

students who are exposed to violence show pure academic

achievement . Many of these students experience post traumatic

stress disorder feel hopeless and depressed have reduce

motivation and persistence to learn. (Costante, C.C. (2002).

Healthy learners). Thus this study has been intended to fill this

gap with focus on the impact of health related problems on

academic achievement of students in Wolaita Sodo University.

1.2 Statement of the problem

In order to minimize the impact of health related problems we

should take in account the major factors and problems that

affect the academic achievements of students. The factors can

be known and can be get solution by conducting a research on

health related problems .Some previous studies have shown

factors affecting the academic achievements of the

students ,Miller 1992,noted that malaria, gastric, typhoid,

common cold,tension greatly affecting academic achievements of

students .This study can be attempted to investigate the most

important and immediate health problems that affect the

academic achievements of students in second and third year

biology students in Wolaita Sodo University .Accordingly the

study is expected to answer the following basic questions .

These are:-2

Which health related factors significantly affect the

academic achievements of students?

Which groups have high risk of health related problems when

categorized by sex?

1.3 Objectives of the Study

1.3.1 General Objective

The objective of this study would be investigated the impact

of health related problems on academic achievement of

students.

1.3.2Specific Objective

To determine the significant factors that affect academic

achievement of students.

To identify which combinations of predictors are highly

influential on academic achievement of students.

To provide information for policy makers and researchers.

1.4 Significance of the study

The finding of this research is expected to help health care

works the university, and other concerned bodies to anticipate

and in inform students about the heath related factors that leads

to failure in academic achievements. In addition, the result of

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this study could ultimately improve the care provided at all

levels of health care to assist the monitoring and planning

resources needs in health system and designing appropriate

intervention ,tailored toward communities at high risk. Moreover,

the result of this study could also be used as some of

information for other researchers.

1.5. Limitations of the study

This research has faced a number of limitations .Among those

are:-

Shortage of time

Lack of internet connection service

Absence of most recent relevant reference.

Absence of record data for each type of data.

4

2 Literature Review

Based on seeming under recognized evidence over all finding is

that there is reason to believe health does have an impact on the

academic achievement of students. Education and health are known

to be highly correlated, that is more indicates better health and

vice verse. The effect of health on academic achievement has been

well researched in developing countries. Students in university

faced more series challenges (miller, 2009).

For the past decade university has under stood and acted the need

to dramatically increase students’ academic achievement. In

recent year university has placed strong focus on the academic

achievement gab works across traditional jurisdiction to

determine how ,when and where to effectively and efficiently

provide the educational /learn supports and services students

need; and pay closer attention to how university can create more

caring culturally relevant and engaging environments for all

students. (Hillman.CH, MB.pontifex and Kremer, 2009).

Students have the best chance to succeed when they are healthy.

Health in this context includes nutritious died, physical

activity, emotional well being satisfy security, the absence of

chronic condition such as malaria ,common cold, typhoid etc.

students who are exposed to violence show pure academic

achievement . Many of these students experience post traumatic

stress disorder feel hopeless and depressed have reduce

5

motivation and persistence to learn. (Costante, C.C. (2002).

Healthy learners).

2.1 The association between Academic Achievement and Health

A fairly large body of evidence both in the economy and students

health literature documents a positive relation between health

and academic achievement, due to the empirical challenges

involved in assessing causality in the relationship (Ding et

al,2006, Gong 2010).

The link between education and health has different potentials,

students’ who learned in university provides an incentive to

individuals to study health and rap in order to achieve their

academic achievement. In this regard highlight difference in

preferences and individual’s valuation of his or her future that

may be affected by the level of education as relevant factors

explained health. As key input in the health production function,

students full health helps individuals maintain and to achieve

the academic achievement. One factors which is affected the

academic achievement of student is unhealthy, which mean that

affected by different diseases like malaria, typhoid etc (Gang,

Gong, 2010).

2.2. External Factors which affects Academic Achievement of

Students

The presence of both observed and unobserved common determinants

of education tends to complicate the empirical estimation of the

relation between educations and economic, aspects such as family

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back ground and individual characteristics of student’s plays an

important role in the academic achievement at university. Hence

failure to take those often hard to be observed factors in to

account would seriously bias any coefficients on the health

variable in Southern part of Ethiopia in South Nation (Smith,

2008).

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3 Data and Method

In this section, methodology, study area, description of the

study data and sample size determination of the study variables

should be discussed. This section ends by discussing statistical

method of data analysis which is appropriate for the study.

3.1 Study Area

The study should be conducted on the impact of health related

problems on academic achievement of second year and third year

students in the Wolaita Sodo University. Wolaita Sodo University

is located in southern part of Ethiopia in south Nation,

Nationality and People region in Sodo town , about 383 kilometers

from Addis Ababa, capital city of Ethiopia . It was established

as the institute of higher education in 1999 E.C. Today the

University contains eleven (11) Faculties under which it has many

academic and administrative staff members.

3.2 Description of the Data

The study would be used data obtained from students’ clinic and

register. The data is already recorded for different purposes

(secondary data)

3.3 variables in the study

3.3.1 Dependent variable

The dependent variable that should be analyzed in this study is

CGPA of students of senior biology department.

3.3.2 Independent variables 8

The independent variables which are expected to affect the GPA of

students are:

Independent variables

Malaria Typhoid

Gastric Diahria

Sex Tension

Age Common cold

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3.4 Sampling Design and Sample size determination

Sampling methods are scientific procedures of selecting those

sampling units which would provide the required estimator with

associated margins of uncertainty arising from examining only

apart not the whole of the population. But, in this study the

whole data should have taken, since the study was used secondary

data. For this reason sample size determination is unnecessary

for this study.

3.5 Statistical Methods

The most useful statistical methods that this study used to

analysis the data is as follows;

descriptive

inferential statistics

3.5.1 Descriptive Statistics

Descriptive statistics is used for displaying, describing and

summarization of data by using tables, graphs, ratio and measures

of central tendency like mean, variance, quartiles and other

descriptive summary measures.

It also consists of methods of organization and summarization of

data from the large original version. Such a data is not very

helpful in drawing conclusions or making decision. The

descriptive statistics that this study used are; tables

containing frequency and percent to display the visual impression

of data, summarizing, describing and presenting the data in a

simple and in an understandable form.

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 3.5.2 Inferential Statistics

The most appropriate inferential statistics that this study used

are; analysis of variance (ANOVA) to ensure weather the overall

regression model is significant or not and multiple linear

regression.

3.5.2.1 Multiple Linear Regression Model

Multiple linear regressions are a type of regression in which we

have a dependent and two or more regressor (independent)

variables. This model is used to study relationship among

variables. The multiple regression models are also used for when

the response variable is quantitative and continuous, which is

normally distributed in population from which it is drawn. But

independent variables can be either categorical or continuous.

The general form of a multiple linear regression model is given

by:-

Yi=β0+β1x1i+β2x2i+---+ βkxki+ ε i for

i=1,2,---,n ,--------------(1)

Where Yi=the dependent variable

Xi=the independent variables

βo= is intercept

β1,β2,---, βK= are coefficients of the independent

variables X1 ,X2,…..XK respectively.

Equation (1) has one key feature. It assumes that all

individuals’ parameters are drawn from a single population with

common population parameters. The term ϵ is the residual or

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random error for individual I and represents the deviation of the

observed value of the response for this individual from the

expected by the model. The errors terms are assumed to have a

normal distribution with mean zero and variance (σ2).

3.5.2.1.1 Hypothesis Testing of Multiple Linear Regression

Model

Hypothesis testing: is testing for the testing significance of

the slope and intercept of the fitted model, and it is used to

test the coefficients of the explanatory variables.

1. HO: βO=0 vs H1:βO≠0 ---for the intercept.

2. Alpha(α) level of significance

3. Test statistics tcal=β̂i−β0√se (β̂i)

4. Decision reject the null hypothesis if tcal ¿ tα/2 (n-2)else and

otherwise accept Ho

5. Conclusion: βi is statically significance if we reject the

null hypothesis

Where βi=the ith coefficients (intercept and slope).

3.5.3 Assumptions of Multiple Linear Regression Model

Regression model has linear relationship between dependent

variable and explanatory variable

The error terms follow normally distribution i.e. ε ~N(0, σ

2),homoscadecity

The values explanatory variable is fixed

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ε ( ε,ε -1)=0 no autocorrelation among error term.

Absence of multicollinearity problems. That means

independent variables are not correlated with each other.

The error terms and independent variables are independent.

i.e ε ~( ε I ,xi)=0

The rank of the explanatory variables is k and k number of

parameters or numbers of column and it should be less than

number of observation(n).

X and ε are independent, but Y and ε are dependent.

Analysis of variance consists of calculation that provides

information from the levels of variability with in regression

model and form a basis for tests of significance of regression

line for reach on conclusion about the information contained in

the overall significance of the model.

3.5.4 Hypothesis testing for ANOVA

1. H0:β0=β1=---------=βk vs H1:β1≠β2≠----≠βk

2. α levels of significance i.e Fα(k-1,n-k)

3. Test Statistics i.e Fcal=MSRMSE

4. Decision reject the null hypothesis if, Fcal ≥Fα(k-1,n-

k),otherwise accept the null hypothesis.

5. Conclusion at least β’s statistically significance, if we

reject the null hypothesis, otherwise β’s are statistically

not significance.

6. Construct the ANOVA table

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ANOVA Table for Multiple Regression ModelModel Sum of

SquaresDegree of freedom

Mean Sum of Square

Ftab

Regression

SSR k-1 MSR=SSRK−1Fcal=MSRMSE

Error SSE n-k MSE= SSEn−kTotal SST N-1Where SSR=Summation square of regression

SSE=Summation square of residual

Β’s=Slope’s of explanatory variables

k-1 and n-k are degree freedom of SSR and SSE respectively

3.6 Model Diagnosis (Adequacy Checking)

Goodness of fit of a linear regression model attempts to get at

the perhaps surprisingly tricky issue of how well a model fits a

given set of data or how well it will predict a future set of

observations and if independent variable is explaining the

dependent variable in manner way is called the model is adequate.

There are different assumption used as model adequacy checking

linearity and normality assumption.

1. Normal Probability Plot of the Standardized residuals: - is a

plot of the ordered and standardized residuals versus the so-

called normal scores.

14

2. Scatter plots of the standardized residual against each of

the predictor variables uncorrelated with each of the

predictor variables.

3. Scatter plot of the standardize residual versus the fitted

values. Under the standard assumption, the standardize

residuals are also uncorrelated with the fitted

values ,therefore this plot should also be a random scatter

of points. In simple regression the plot of standardize

residuals against x and against the fitted values are

identical.

4. Index plot of the standardized residuals. In this diagnostic

plot we display the standardized residuals versus the

observations.

15

4. Results and Discussion

The first part of this section deals with descriptive statistics

plus bivariate analysis, the second part deals with multiple

linear regressions and the section ends with discussion.

For this study, the data of academic achievement of senior

Biology Department students of Wolaita Sodo University is used.

The data of size 169 were obtained from grade report of students.

In this paper 5% level of significance was used to investigate

the significance of the variables.

4.1 Descriptive Statistics and Bivariate Analysis

Table 4.1 (a)Min.

mean sd Q1 Q2 Q3 Max.

n

19 21.68047

1.411571

20 22 23 24 169

Table 4.1 (b) Sex

Chi-square

Female Count (%)

MaleCount (%)

TotalCount (%)

Value (p-value)

df

Typhoid

yes 65(52%) 60(48%) 125(74%)

22.149(0.000)

1

no 5(11.4%)

39(88.6%)

44(26%)

Diahria

yes 53(44.9%)

65(55.1%)

118(69.8%)

1.969(0.161)

1

no 17(33.3%)

34(66.7%)

51(30.2%)

16

Commoncold

yes 65(49.6%)

66(50.4%)

131(77.5%)

16.138(0.000)

1

no 5(13.2%)

33(86.8%)

38(22.5%)

Tension

yes 15(30.6%)

34(69.4%)

49(29%) 3.332(0.068)

1

no 55(45.8%)

65(54.2%)

120(71%)

Gastric

yes 50(52.1%)

46(47.9%)

96(56.8%)

10.415(0.001)

1

no 20(27.4%)

53(72.6%)

73(43.2%)

Malaria

yes 50(56.8%)

38(43.2%)

88(52.1%)

17.941(0.000)

1

no 20(24.7%)

61(75.3%)

81(47.9%)

The mean age of respondents, table 4.1 (a), is 21.68with standarddeviation 1.41 ranging from 19 to 24. 25% of students were less than 20 years old the median age is 22 and75% of the patient’s age were below 23.As we can see, table 4.1(b), 52%, 44.9%, 49.6%, 30.6%, 52.1% and 56.8 percents of female students are suffered from typoid,diahria,commoncold,tension,gastric and malaria respectively and 48%,55.1%,50.4%,69.4%,47.9%,and 43.2 percents ofmale students are suffered by typhoid, diarrhea, common cold, tension, gastric and malaria respectively . Generally, in this study, Out of 169 students considered, male students are more affected by disease than female students when categorized by sex.Furthermore, from chi-square of test result typhoid, common cold,gastric, tension and malaria are significantly associated with sex, but diahria and tension have no association with sex.

4.2 Result of Multiple Linear Regressions

Here multiple linear regression is used to see the relationship between dependent variable and independent variables. .

17

> RegModel1 <- lm (gpa~age+commoncold+diahria+gastric+malaria+sex+tension+typoid, data=Dataset)

> Summary (RegModel1)

Call:lm (formula = gpa ~ age + common cold + diahria + gastric + malaria + sex + tension + typhoid, data = Dataset)

Residuals: Min 1Q Median 3Q Max -0.66092 -0.17373 -0.01613 0.16667 0.74900

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.085027 0.338267 9.120 3.04e-16 ***Age 0.008905 0.015796 0.564 0.57373 Common cold -0.155788 0.070284 -2.217 0.02806 * Diahria -0.123912 0.051825 -2.3910.01797 * Gastric -0.123129 0.047084 -2.615 0.00977 ** Malaria -0.267957 0.047526 -5.6387.60e-08 ***Sex 0.153507 0.049072 3.128 0.00209 ** Tension -0.091807 0.045568 -2.015 0.04561 * Typhoid -0.363288 0.069905 -5.1976.10e-07 ***---Signif. Codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2634 on 160 degrees of freedom

18

Multiple R-squared: 0.7094, Adjusted R-squared: 0.6949 F-statistic: 48.82 on 8 and 160 DF, p-value: < 2.2e-16

From the above output common cold, diahria, gastric, malaria, sex, tension, and typhoid were found to have significant effect on academic performance of students.

4.3 Variable Selection

Variable selection can be done either by computer algorithm or manually by discarding the variable with largest p-value, greaterthan the specified level of significant. In this study the selection procedure is done by backward selection computer algorithm using R version 3.0.3 (2014-03-06).

> Stepwise (RegModel1, direction='backward', criterion='AIC')

Direction: backwardCriterion: AIC

Start: AIC=-442.13gpa ~ age + common cold + diahria + gastric + malaria + sex + tension + typhoid

df Sum of Sq RSSAIC- age 1 0.02205 11.125 -443.79<none> 11.103 -442.13- tension 1 0.28169 11.385 -439.89- Common cold 1 0.34095 11.444 -439.02

19

- Diahria 1 0.39671 11.500 -438.20- Gastric 1 0.47458 11.578 -437.06- sex 1 0.67909 11.782 -434.10- Typhoid 1 1.87419 12.977 -417.77- Malaria 1 2.20597 13.309 -413.50

Step: AIC=-443.79gpa ~ common cold + diahria + gastric + malaria + sex + tension +typhoid

df Sum of Sq RSS AIC<none> 11.125 -443.79- tension 1 0.27045 11.396 -441.73- Common cold 1 0.35675 11.482 -440.46- Diahria 1 0.41405 11.595 -438.81- sex 1 0.87715 12.002 -432.97- Typhoid 1 1.85219 12.978 -419.77- Malaria 1 2.22030 13.346 -415.04

Call:lm(formula = gpa ~ common cold + diahria + gastric + malaria + sex + tension + typhoid, data = Dataset)

Coefficients:

20

(Intercept) common cold diahria gastric malaria sex tension typhoid 3.27240 -0.15887 -0.12620 -0.12238 -0.26872 0.16326 -0.08964 -0.35882

The result above shows common cold, diarrhea, gastric, malaria,sex, tension, and typhoidshould be included in the model and hence, the best model is:

>Regmodel2<-lm ( gpa ~ common cold + diahria + gastric + malaria + sex + tension + typoid,data = Dataset)> Summary (Regmodel2)

Call:lm (formula = gpa ~ common cold + diahria + gastric + malaria + sex + tension + typoid, data = Dataset)

Residuals: Min 1Q Median 3Q Max -0.67233 -0.17741 -0.02328 0.16787 0.76188

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.27240 0.06271 52.179 2e-16 *** Common cold -0.15887 0.06992 -2.272 0.024400 * Diahria -0.12620 0.05156 -2.448 0.015445 * Gastric -0.12238 0.04696 -2.606 0.010029 * Malaria -0.26872 0.04741 -5.668 6.51e-08 *** Sex 0.16326 0.04582 3.563 0.000483 *** Tension -0.08964 0.04531 -1.978 0.049597 *

21

Typhoid -0.35882 0.06931 -5.177 6.64e-07 ***---Signif. Codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2629 on 161 degrees of freedomMultiple R-squared: 0.7088, Adjusted R-squared: 0.6962

F-statistic: 55.99 on 7 and 161 DF, p-value: < 2.2e-16From the above output the value of R-square is equal to

0.7088.this implies that 70.88% of the response variables is

explained by the explanatory variables. The adjusted R-squared is

equal to 0.6962which is greater than 50% and this indicates that

the model is well fitted as can also be best explained under

section 4.4 below.

The output for the best fitted model indicates that common cold,

diarrhea, gastric, malaria, sex, tension, and typhoid

significantly affect the academic achievement of students.

4.4 Model Adequacy Checking

After finding results, the overall adequacy of the model should

be checked. There are several alternative methods to check the

adequacy of the fitted model. Among many alternative methods, we

used the following very common plots to assess the model.

22

Residuals versus fitted plot above indicate, the standardized

residuals are uncorrelated with the fitted values; as this plot

is a random scatter of points.

The normal Q-Q plot resembles a (nearly) straight line with an

intercept of zero and a slope of one (these are the mean and the

standard deviation of the standardized residuals, respectively)

implying normality assumption is satisfied.

Moreover, Scale location and residual versus leverage plots on

the figure above show that there is no powerful outlier and

influential value in the data respectively.

23

Therefore, from the plot above, we can generalize that the model

fits the data well.

4.5 Discussion

Based on seeming under recognized evidence over all finding is

that there is reason to believe health does have an impact on the

academic achievements of students (miller,2009) and (Constante

c.c(2002).Health learners) . In recent year University has

placed strong focus on the academic achievements gab works cross

traditional jurisdiction to determine how, when and where to

effectively and efficiently provided the conditional or learner

and students have the best chance to succeed when they are

healthy. As many researchers or writers provided information

absence of chronic condition such as malaria, common cold, and

typhoid are the major factors that affect academic achievements

of students in the University. Based on the rises assumption of

our study results also similar to one i.e. malaria, common cold,

and typhoid are the major factors that affect academic

achievements of students in the University.

24

25

5 Conclusions and Recommendation

5.1 Conclusions

The following conclusions were made based on the results and

discussion.

Analyses of multiple regressions indicate that malaria, typhoid,

common cold, diahria and gastric are the most prominent or the

most determinant factor that affect the academic achievements of

students. It implies that health related problems and CGPA of the

students are indirectly relationship, when the impact of health

related problems increased as the academic achievement of the

students decrease and the impact of health related problem

decreased as the academic achievement of student increase. In

generally health plays a great role on the academic achievement

of students.

5.2 Recommendation

Based on the final finding or results the following

recommendation were forwarded Since malaria, common cold,

tension, typhoid ,diahria and sex were have an impact on academic

achievement of students, the concerned body should have focus on

these factors to increase CGPA of students. Both government and

the university had better to work to avoid this problem. .

26

References

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