# Exam Performance Analysis

1275 Words 6 Pages
Introduction

This report considers whether gender, quiz result, lecture and SGT attendance affect university students’ exam results. The effectiveness of learning platforms must be evaluated so students reach their full potential. To achieve this, a sample of previous BSB123 students’ results and attendance will be summarised, analysed and interpreted.

Data

2.1 Research
Stanford University found that on average, males outperform females on mathematics tests (Niederle & Vesterlund, 2010), which would be expected in the statistics course BSB123. The majority of the BSB123 sample (56.8%) was female, which may affect exam results. Additionally, quiz results are positively correlated with exam results (Wambuguh & Yonn-Brown, 2013) and students
t-test of Male and Female Exam Results

Equal variances were assumed as male and female variances are similar. The null hypothesis, that the difference between male and female results was 0, was not rejected (0.50 is greater than 0.05 at 5% significance) for the alternative hypothesis, that the difference between male and female results would be greater than 0. Therefore, there is insufficient evidence that males outperform females on exams.

The effect of lecture attendance on exam results was also examined using a two-sample t-test.

Table 5. t-test of Exam Results with High and Low Lecture Attendance

At 5% significance, the highlighted one-tail p-value of 0.002 is smaller than 0.05. Hence, the null hypothesis that those who have high lecture attendance achieve the same exam results as those who do not is rejected for the alternative hypothesis that high lecture attendance increases exam results. Hence, using this t-test, there is sufficient evidence that higher lecture attendance increases exam score.

Model

4.1 Interpretation
The regression output in table 3 can be expressed as an equation to model exam results.

y_(exam result)=2.39267+1.48505〖 x〗_1+0.98494 x_2+2.81056 x_3+0.54784〖 x〗_4 where: x_1= quiz