This midterm is intended to test your knowledge (analytical skills) about regression methods and its application in public health research (practical problem-solving skills), in the following areas: Simple Linear Regression, Multiple Linear Regression, and Simple Logistic Regression.
Please be thorough but succinct and clear in writing. For each question, I prefer you show all evidence in your write-up (make sure to precisely refer to your evidence where necessary. NOTE: Do not copy and paste the SAS / SPSS / STATA / R output. If necessary, create tables from the software output to show your results). IF YOU COPY & PASTE THE SOFTWARE OUPUT, YOU WILL MISS HALF OF THE POINTS ASSIGNED TO THAT PARTICULAR QUESTION.
Make sure the tables you provide are appropriately titled and variables meaningfully labeled (don’t just copy and paste the variable names, output, from the software you use: YOU WILL MISS POINTS). Provide the SAS/SPSS/STATA program in Appendix.
IN THIS MIDTERM, I LOOK FOR EVIDENCE OF SCIENTIFIC WRITING IN THE CONTEXT OF (AND RELAVENT TO) THE RESEARCH STUDY. (FOCUS SHOULD BE TO MEANIGFULLY CONVEY THE RESULTS, WHICH YOU SUPPORT WITH APPROPRIATE STATISTICAL ANALYSES).
I require you to turn in your MIDTERM EXAM typed in a word document. MIDTERM EXAM TUNRED IN PAST DUE DATE OR HANDWRITTEN MIDTERM EXAM WILL NOT BE ACCEPTED FOR GRADE.
WORD OF CAUTION:
Students are expected and will be held responsible to abide by the honor code for the midterm exam.
In particular, you are cautioned not to show or discuss the midterm with anyone (this includes a tutor, collaborator, another instructor, fellow students, and students who took the midterm in the past) aside from the instructor of this course.
Should you have any questions while working on the midterm, please contact the instructor via e-mail or in person (make sure to schedule an appointment). I prefer you contact sooner than later. Considering the time allotted to work on the midterm, MIDTERMS TURNED IN AFTER THE DUE DATE WILL NOT BE CONSIDERED FOR GRADE.
NOTE: KEY for the midterm will not be provided after you receive your grade. You are highly encouraged to talk to the instructor to learn where you did well and where you could have done better.
1. Use the HERS Trial Data to conduct the following analyses:
(a) Provide the estimated regression equation for estimating (predicting) the average Total Cholesterol (variable: tchol) with the following predictors in the model: Hormone therapy (HT), Age of a female (Age), Body Mass Index of a female (BMI), Ethnic background (Nonwhite), Diabetes status (Diabetes), Tryglycerides (TG), Use of Statin drugs (Statins), Exercise behavior (Exercise), Smoking status (Smoking), and Systolic Blood Pressure (SBP). Please refer to the codebook for unit of measurement on each of these variables.
[ 2 points ]
(b) Considering the estimated regression model in part (a), is Body Mass Index a statistically significant predictor of Total Cholesterol? Interpret. [ 2 points ]
(c) Provide the estimated regression equation for estimating (predicting) the average Total Cholesterol (variables: tchol) with the following predictor in the model: Body Mass Index of a female (BMI). [ 2 points ]
(d) Considering the estimated regression model in part(c), is Body Mass Index a statistically significant predictor of Total Cholesterol? Interpret. [ 2 points ]
(e) Does your conclusion (interpretation) in part (b) agree with your conclusion (interpretation) in part (d), about the effect of Body Mass Index on Total Cholesterol? Explain in scientific detail why you agree OR disagree and support your reasoning with appropriate results (statistical evidence). [ 5 points ]
2. Consider the following simple linear regression model
Y 0 1X
, where Y is blood
glucose levels and X is weight. Describe 2 different ways in which one may test the null
hypothesis that
H0 : 1 0. [ 3 points ]
3. For conducting the analyses described below, use the data based on a 5% sample of all births
occurring in Philadelphia in 1990. The dataset is ‘BWeights’ provided in SAS format.
The codebook for the variables in the dataset ‘BirthWeights’ is below:
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