Does Regular Exercise Improve GPA?

1. Does Regular Exercise Improve GPA? By Kristin Miller, Patrick Ruhr, and Amna Sultan Introduction Conclusion Results Through conversations with friends and other students, there seemed to be a common theme in their college study habits; all of these high achieving students scheduled time for exercise.  It seems counterintuitive for students to be setting aside study time for exercise and still achieving high grades, As and Bs. Our goal was to find out if student GPAs are significantly related to the total hours of exercise per week in which a student participates.  Overall, should students  incorporate exercise into their busy schedules  to boost their academic performance? The sample data showed, 50% of the GPAs ranged from 3.3-3.8 and 50% of the weekly hours of exercise reported was between 1-5.5 hours, with outliers in each category; this indicates high variability in each category. Based on the linear regression performed, we could not conclude a correlation exists between GPA and amount of time spent exercising, or between the other variables in our study. The two-sample hypothesis tests performed for each variable showed no difference in GPAs with respect to the corresponding variables, with one possible exception.  The p-value for younger students having lower GPAs than older students was 0.106, which is very close to our significance level of 0.10.  This indicates that age may be a factor in determining GPA.  For all other variables there was not sufficient evidence to determine difference in GPA. Based on the sample data, there is not sufficient evidence to support the claim that incorporating exercise into students’ schedules will boost their academic performance, measured by GPA. Even though we accounted for the prominent confounding factors, we were limited by our sample size and the population from which we sampled.  Our results may have differed if we had a larger sample and were able to include students who do not attend class. Summary Statistics and Boxplots A+ Methodology Linear Regression Analysis Data was collected from 60 randomly selected students at different times on different days at Salt Lake Community College’s Taylorsville Redwood Campus.  Each participant was asked specific questions relating to the following topics: age, most recent GPA, school status, work status, and frequency of exercise. Using StatCrunch software, the data was analyzed utilizing two methods: linear regression and two-sample hypothesis testing.  Linear regression was used to check for any correlation between GPA and each of the other variables.  For the two-sample hypothesis tests, each variable was split into two appropriate categories and the GPAs corresponding to the variables were compared.  Exercise was split into infrequent (less than 3 hours a week) and frequent (more than four hours per week).  Age was split into younger and older based upon the median age of the sample, which was 24. Two-Sample Hypothesis Tests Sources StatCrunch Software: www.statcrunch.com Data: October 2013, Salt Lake Community College Taylorsville Redwood Campus by Kristin Miller, Patrick Ruhr, and Amna Sultan.