1. Using your data file, create two scatterplots with lines of best fit:

1. aerobic fitness (afit) and BMI
2. aerobic fitness and percent body fat (pft)

2. Using your data file, evaluate whether BMI or percent body fat can be used to predict aerobic fitness, and determine whether age affects these predictions. Justify your answers using statistical evidence.

1. How accurately can you predict aerobic fitness from BMI?
2. How accurately can you predict aerobic fitness from percent body fat?
3. Which model is better for predicting aerobic fitness, BMI or percent body fat?
4. Create two more models by adding age; you are using BMI and age, and percent body fat and age, respectively, to predict aerobic fitness. After accounting for BMI or percent body fat, is age a significant factor in predicting aerobic fitness?
5. Of these four models, which is the best for predicting aerobic fitness? What evidence do you have that it is the best model, and what is the equation for predicting aerobic fitness using that model?

3. Study design is critical to be able to use experiments to answer research questions.

1. Give an example of a one-tailed research hypothesis in kinesiology or health.
2. Give an example of a two-tailed research hypothesis in kinesiology or health.
3. Define type I error and type II error.
4. What is statistical power and why would you be interested in it when designing a study?

4. The data in your data file are from a study in which subjects were randomly assigned to either a six-month occupational fitness program or to a control group who performed no exercise. The measures were collected at the end of the study. The variable "group" in the data file identifies the exercise subjects (group = 1) and the control subjects (group = 0). Using your data file, test the following three hypotheses: subjects in the fitness program had 1) higher aerobic fitness, 2) lower percent body fat, and 3) lower BMI.

1. What are the independent variables and dependent variables for each hypothesis?
2. Are these hypotheses one-tailed or two-tailed? What are the corresponding null hypotheses?
3. What are the means and standard deviations for each dependent variable within each group?
4. Did subjects in the fitness group have higher aerobic fitness, lower percent body fat, and lower BMI? What is your evidence?

5. The sums of squares are computed differently in repeated measures ANOVA than they are in simple ANOVA.

1. What are the three sources of variation for each sum of squares in simple ANOVA and the four sources of variation in repeated measures ANOVA?
2. Both simple ANOVA and repeated measures ANOVA use the F statistic, which is a comparison of two mean squares (variances). What two mean squares are compared in simple ANOVA? What two mean squares are compared in repeated measures ANOVA?
3. What is the inference associated with a statistically significant F value in repeated measures ANOVA?
4. Why is the repeated measures method more appropriate if observations (scores) are not independent?

Price: $22.33

Solution: The downloadable solution consists of 10 pages, 1233 words and 10 charts.
Deliverable: Word Document