A Report on Factors Affecting Student Performance (Statistics Project Sample)

A Report on Factors Affecting Student Performance
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A Report on Factors Affecting Student Performance
This report studied two factors that may affect students’ performance in Math. For a long time I have noticed with curiosity that most of the students in my class do not attain the mean mark in Math. For a long time, I have believed that the more you spend time working out Mathematical problem, the more comfortable you become with them, hence, the better your score. However, some of my friends I randomly surveyed also cited distance from home to one’s school may also be a factor that determines if one will have good or poor scores in Math. They explained that those who are far from school get fatigued from ‘long’ distance travel which reduces their concentration during Math study both in school and at home. I set out to investigate the effect of these two variables; study time (in minutes) in a day and distance (in miles) from home to school, on the end of the term Math score. Math score is the dependent variable while the other two variables; study time (in minutes) in a day and distance (in miles) from home to school are independent variable. I randomly collected data from 30 students in my class which I used to make inferences and conclusions in this report. I designed a form that contained 30 names of randomly selected students in my class and requested them to fill in their time of study and approximate distance from their respective homes to school. With their permission, I requested our Math professor to provide me with their recent Math scores. I entered the data I obtained into STATA software and found that the mean of their score was 68.633 with a standard deviation of 17.135. This means that on average, each student in my class scored 68.633% which is above the average score of 50%. The standard deviation shows us how far the student scores are spread from the mean. The students who took part in this study also indicated that they study for 167.67 minutes on average in a day. This is approximately two hours and 48 minutes a day. Lastly, they indicated that their homes are 6.83 miles far from school on average. The regression equation used was;Student Score= Constant + β1*Study time (minutes) + β2*Distance (miles)
β1 is the coefficient of study time and β2 is the coefficient of distance.
05182235Figure 1: Regression Analysis outputFigure 1: Regression Analysis outputleft34861500I expect that study time will have a positive effect on student score because I have always believed that the more you spend time working out Mathematical problem, the more comfortable you become with them, hence, the higher your score. On the hand, I expect distance to have a negative effect on student score because those who are far from school are expected to get fatigued from ‘long’ distance travel which reduces their concentration during Math study both in school and at home and in return reduce their scores.
The figure 1 above is the output of the regression analysis done in STATA. The results concur with my first hypothesis that study time will have a positive effect on the student scores. The coefficient of study time is 0.13556 which means that for every minute increase in time of study, the score of the student increases by 0.13556. However, these results do not concur with my second hypothesis that distance affects student score negatively. On the contrary, for every one mile increase in distance, the student score increases by 1.059. If we consider a 1% level of significance, we would say that study is not statistically significant at 1% level of significance because its p-value is less than 0.01. Distance is statistically significant because its p-value, 0.036, is larger than the level of significance. The correlation coefficient, R-Squared, of 0.9081 shows that 90.81% of the total variation in the score of the students has been explained by the study time and the distance from home to school.
left491426500Multicollinearity is a disturbance which results from independent variables in a model becoming highly inter-correlated. I this case, I used...