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group project math 1040

group project math 1040. Group 3: Vivian Dinh, Sunny Davis, Ashley Tignor , Jessica Shake. Purpose of study.

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group project math 1040

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  1. group projectmath 1040 Group 3: Vivian Dinh, Sunny Davis, Ashley Tignor, Jessica Shake

  2. Purpose of study • Research Question: For SLCC students attending the Redwood campus, does hours of exercise per week relate to cumulative GPA? We wanted to examine whether exercise affects GPA. Ultimately we wanted to determine whether exercise was good for our intellectual health as we already know it is good for our physical health.

  3. Study Design

  4. Group 3 Data Results

  5. Hour of exercise per week graph

  6. Hours of exercise per week Our first qualitative variable is the hours of exercise per week for the SLCC students we interviewed. Listed below is the data for hours of exercise per week data in ascending order. Outliers: Q3-Q1=IQR 5.25-2=3.25 Q1-1.5(IQR)= Outlier 2-1.5(3.25)= -2.87 Q3+1.5(IQR)= Outlier 5.25+1.5(3.25)= 10.125 The outliers for this data is 15. 1st Quartile Minimum Median 15 3rd Quartile Maximum

  7. GPA

  8. GPA The second qualitative variable is the GPA of students we interviewed. Listed below is the data for GPA listed in ascending order. Outliers: Q3-Q1=IQR 3.5-2.6= 0.9 Q1-1.5(IQR)= Outlier 2.6-1.5(0.9)= 1.25 Q3+1.5(IQR)= Outlier 3.5+1.5(0.9)= 4.85 There are no outliers for this data. Minimum Median 1st Quartile Maximum 3rd Quartile

  9. Does increased hours of exercise per week result in higher cumulative GPA?

  10. Correlation Analysis

  11. Scatter Plot

  12. Parameter estimates: Simple linear regression results: Parameter estimates: Analysis of variance table for regression model:

  13. Simple linear regression results: Dependent Variable: GPAIndependent Variable: Hour of Exercise per Week y = 3.1364536 - 0.01720754 x Sample size: 100 R (correlation coefficient) = -0.0855 R-sq = 0.0073119695 Estimate of error standard deviation: 0.58115214

  14. Surprises/difficulties • Our group was working with a large sample size and found it somewhat difficult dealing with the calculations of so much data. It took quite a bit of time to do the calculations for 100 data points for each of the variables and to double check each of the results. • Our group wasn’t sure if our data would show a strong correlation between the hours of exercise per week and GPA but suspected that increased exercise might lead to doing better in school. • Our data ended up surprising us by showing a moderate negative correlation between the hours of exercise per week and GPA for the students we interviewed.

  15. Analysis Our data shows that most people we surveyed exercise very little each week, therefore the histogram for that variable is skewed to the right. Conversely, the GPA variable is distributed evenly in its histogram. The linear relationship appears to be very weak between the two variables because the correlation coefficient (-0.0855) is close to being 0. The critical value in table II at 0.05 level of significance is .195, which is greater than our R value. Therefore we believe that our data for hours of exercise per week and GPA are not correlated with each other because we are not able to reject our null hypothesis.

  16. Conclusion • In conclusion, our hypothesis is not true that the more hours a student exercises per week the better their cumulative GPA is. Our variables don’t have any correlation as expressed in our analysis. We believe this is so due to lurking variables like the age of the student, how many credits the student takes during a semester, and how many hours the student works during the week. If we had to repeat the project, we would make sure all of these lurking variables were the same in our population so we could be sure that our results accurately reflected whether exercise per week relates to cumulative GPA.

  17. Group 3 Contributors list: • Vivian Dinh – Start power point draft including graphs, edit, and revise information for final product. • Sunny Davis – Scripts for purpose study, surprise/difficulties, analysis, and conclusion. • Ashley Tignor– Data tables, and analysis tables of hours of exercise per week and GPA. • Jessica Shake – Edit data tables in power point.

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