1. Develop an equation to model the data using a regression analysis approach and explain

your calculation process in Excel.

2. Calculate the r-square statistic using Excel. Interpret the meaning of the r-square statistic in

this case.

3. Determine three conclusions that address the initial observations and are supported by the

regression analysis.

1. Develop an equation to model the data using a regression analysis approach and explain

your calculation process, using Excel.

2. Megan has a small group of three additional patients that are the same age that she wants to

examine for lesions. She knows the number of minutes of continuous exposure to direct

sunlight that each has experienced. Predict the number of lesions that each of these patients

will have based on the regression analysis that you completed in your initial data analysis:

• Patient 9-193 minutes.

Patient 10-219 minutes.

Patient 11- 84 minutes.

3. Determine three conclusions based on the correlation of the number of lesions to minutes of

sunlight exposure, using regression analysis.

1. State the null hypothesis and the alternative hypothesis for this situation.

2. At a = 0.01, can you conclude that the mean times are different? Assume that each

population of relief times is normally distributed and that the population variances are

equal. Hint: Use a one-way ANOVA to solve this problem. Be certain to show your

calculations and describe the process you used to solve this problem.

3. Determine three conclusions on the effectiveness of the medication by addressing

observations or hypotheses regarding these initial tests.

1. Provide a three-paragraph summary of the findings you learned through the analysis.

2. Provide three data-driven suggestions for further exploration.