APPENDIX

Dependent variable: T= Best 5K Time (MINUTES)

Independent variables

M= Miles Run per Week G= Gender (1 if identified themselves as Male and 0 if not)

HSA= High School Level Athlete Y= Years of competitive Running

A= Age when they ran the Best TimeWU= Warm-up Completed (1 if they warmed up at all and 0 if not)

TW= Type of warm-up completed (1 if dynamic stretching, 0 if not)

CT=Cool down type (1 if it is a run and 0 if they used another cool down method)

STR= Strength Training Completed (1 if core/abs and 0 if not)

8. A couple of years ago, a group of students designed a project that would allow them to predict the best time taken to run the 5K. The description of the variables is given in the APPENDIX. a) (2) Generate the correlation between T and all the variables using EXCEL. Copy and paste the results here. b) . (2) Generate the regression of T on all the independent variables in the data using EXCEL. Copy and paste the results here. This is MODEL I. c) (3) Interpret the estimated correlation coefficient between miles run per week and the best 5K time. d) (7) Test the claim that there is no correlation between the best time and the miles run against the claim that miles run' and the best time for the 5K move inversely (as one goes up, the other falls). Use a 5% level of significance. Ho: Ha: Decision Rule (Draw the distribution, show the critical values, and shade the rejection region) Test statistic: (Show the calculated value formula and what you are using to generate the value) Decision : Do not reject Ho Reject Ho (Circle) Conclusion in the context of this problem: e) (2) Evaluate the explanatory power of MODEL 1. f) (2) Interpret the estimated coefficient of HSA (1 if the runner was a high school athlete when they achieved their best time and 0 if not) in MODEL