1. [7 points]

Refer to the dataset "cyclosporine.xlsx".

This dataset presents data from 17 participants, 7 of whom were given cyclosporine, an immunosuppressant, as part of an organ transplant, and 10 of whom were given a different drug. Researchers were interested in the impacts of long-term cyclosporine use on renal function. To help answer this question, researchers measured the serum creatinine levels present in participants given cyclosporine and given another drug.

Variables in this data include:

IDnum: Participant ID (1 to 17)

cyclosporine: Indicator of cyclosporine use (0 for other drug, 1 for cyclosporine)

creatinine: Participant serum creatinine level (mg/dL)

systolic: Participant systolic blood pressure level (mmHg)

1a. [2 points]

Researchers wish to compare serum creatinine levels between cyclosporine users and non-users. To help them, explore the distributions of serum creatinine levels in the two treatment groups with two side-by-side boxplots. Give a brief written summary comparing the two boxplots.

Comments: Your graphs should be descriptive, organized and clear. Think carefully about labeling, clarity and communication. Your reader should be able to compare, at a glance, the serum creatinine distributions for the two treatment groups. Graphing is more than the mechanics of the software; you must communicate a story accurately and succinctly to address the interests of the investigators.

1b. [0.5 point]

Based on your answer to (1a), do you think it is appropriate to compare average serum creatinine levels between the two treatment groups using a two-sample t-test? If so, why? If not, which non-parametric test is more appropriate?

1c. [4.5 points]

Regardless to your answer in (1b), carry out a t-test to compare the mean serum creatinine levels between treatment groups.

Complete all the usual steps of hypothesis testing such as:

• State the null and alternative hypothesis in words and symbols.
• State and check any assumptions required for your proposed test. Explain briefly.
• State and calculate the test-statistic and pertinent results such as the means and standard deviations for each group.
• State the degrees of freedom, if appropriate.
• Calculate the p-value.
• Interpret the results (p-value interpretation and decision/conclusion, i.e.,What (if anything) can we conclude about serum creatinine levels in the two treatment groups?)

If you use software, state the command you used and include a portion of the output. If you used a table, state that.