Correlate the following variables: Group quads gluts abdoms grip arms injury. a. Provide a printout with the correlations (1 point) b. Which of these variables seems to be good candidates for explaining individual differences in injuries (i.e., the overall index of injury). (1 point) c. Provide printout of the scatterplot between arm strength and the injury variable. Be sure to include the regression line in this plot for credit. (1 point)
The questions pertain to using regression analysis. a. Run a regression analysis using the injury index score (injury variable) as the DV and the self-defense training group as the independent variable. Be sure to include the part correlation for credit. Provide a printout of this output. (1 point) b. Now, include the arm strength variable as an additional predictor. Is arm strength a significant predictor of injury index while controlling for the effects of the training groups? Why or why not? (1 point) c. How much variance in the injury outcome variable is explained by arm strength while controlling for the effects of group? Show calculations using the part correlation for credit. (1 point) d. Is this additional variance in the outcome that is explained by arm strength significantly different from zero? Justify your answer. (1 point) e. Now, run a regression analysis with group quads gluts abdoms grip arms as the predictors and the injury index score as the dependent variable. Please show your output for credit. (1 point) f. Which parts of the body appear to uniquely predict injury regardless of which training group the participants were in? Please justify your answer? (1 point) g. Please interpret the effect sizes for each of the predictors in 2f that were significant in terms of the rules of thumb for small, medium, and large. (1 point)
Suppose you wanted to conduct a regression analysis that involves 5 predictors. You want the power to be .80, the type one error rate at .05, and you are assuming the effect size will just make the medium cutoff. Please provide the results from a power analysis using GPower. Show this screen in your homework for credit. (1 point) a. How many participants will you need to test the effects of any one predictor with a power of .80 based on the printout from GPower? b. How many participants will you need to test the overall effects (i.e., total R2) for all predictors with a power of .80 based on the printout from G*Power?
