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
1
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
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.
iv
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
vi
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
3
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
6
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).
7
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
9
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.
10
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
11
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
12
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
13
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
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
[1]. Austin & Lee Bathe (2004) sted. Available online at:
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