In this report I will explore whether taking a pre-course or preparation course in the run up to a normal for credit course can help students achieve better. Royce Kimmons, a professor in Psychology and technology, used a data set that included scores for a mixture of exams and exam takers, keeping in mind parameters such as social, and economic. The results used in this report are based of both the scores from the math and reading exams. In addition, other variables taken into consideration was gender and parental level of education.

The graph above highlights as a decimal, what proportion of people took the test preperation class for both males and females. The results it shows are that there is a higher number of students that had not partaken in the test preparation class. Approximately, 70% in fact did not.

As seen in this report, \(\mu_1\) refers the population mean for students who did not complete the pre course class and \(\mu_2\) refers to the population mean for students who did.

The two graphs above illustrate the scores for the students who did and did not take part in the pre-course class.The density curve of those who did not take the test is more distributed to the left whereas the curve of the students who completed the preparation test is mostly distributed to the right. These two different positions of the distributions means that the population mean differs between those who did and didn’t take the class. Those that didnt had a lower mean than those that did.

Is the preparation class an effective way to help boost potential success for further classes? To judge this effectiveness, a hypothesis test was carried out. Testing with a 0.05 level of significance, the following null hypothesis and alternative hypothesis were used

\[H_0 :\mu_1 =\mu_2\] \[H_a :\mu_1 < \mu_2 \]

The test results gave a p-value equal to 1.043e-08, close to zero and the null was rejected. The test proved that the sample mean for the students who did not take the prep course, \(\bar{x}_1\) = 64 is less than the sample mean of the students who completed the prep course, \(\bar{x}_2\) =70. At 0.05 level of significance there is enough evidence to prove that completing the prep test is effective.

To gain an even greater understanding i explore the reading score distribution. This resulted in a similar answer and distributive characteristics as the math score distribution. This shows that the grades did not just go up for math as a subject but part in the prep class does in fact help improve grades for any subject.

The graph above shows the parental level of education for students within the sample. The graph demonstrates that there are a greater number of parents under every level of education did not have their child complete the preparation course. This shows that the chances of having a student complete the test was wasn’t down to the influence of the parents level of education.

Reference:

Kimmons, Royce. “Understanding Digital Participation Divides.” Exam Scores, http://roycekimmons.com/tools/generated_data/exams.

please note that this data and infomation should not be used for research purpose.