Examine the distribution of sociodemographic and other important variables in your data set using frequency

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

PART1: This will address question related with your research project based on what you have learned from module 4 to module 8.

1. Examine the distribution of sociodemographic and other important variables in your data set using frequency/percent or mean values. Present your results in a table and describe/discuss that in text format. This table and text will make up the first part of the results section when you write a scientific paper. (3pts)

2. Test if there is a two by two relationship between your outcome variables and exposure variables using chi square, t test, correlation or simple regression analysis. Present your results in a table and describe the results in text. This table and text will usually make up the second part of the results section when you write a scientific paper. (3pts)

3. List potential moderators and confounders that you may need to examine in your research project? Justify your selection. (2pts)

Answer the 14 question below in part B and part C using the Hw2 data set. SAS codes and output are required. The SAS dataset “Hw2” on canvas gives the systolic blood pressure (SBP), body size (QUET), age (AGE), Gender (GENDER) (0=female, 1=male) and smoking history (SMK = 0 if a nonsmoker, SMK = 1 if a current or previous smoker) for a hypothetical sample of 32 white males over 40 years old from the town of Angina. (QUET stands for “quetelet index,” a measure of size defined by QUET = 100 (weight/height2).

PART B: This part address contents of module 5 to 8 (3 pts each)

1. Determine the least-squares estimates of the slope (𝛽1) and intercept (𝛽0) for the Straight-line regression of SBP (Y) on QUET (X).

2. Test the null hypothesis of no relationship between blood pressure and body size {zero slope (𝛽1)}. 

3. Based on your test in part (a)(2), would you conclude that blood pressure increases as body size increases? Explain your answer.

4. Obtain a 95% confidence interval for 𝛽1. Interpret your result.

5. Would you reject the null hypothesis H0: 𝛽1 = 0 in question part (b)(2) based on the confidence interval that you calculated in part (b)(4)? Explain.

6. Test whether the SBP–QUET relationship is moderated by GENDER, controlling for smoking status.

7. Test whether the SBP–QUET relationship is confounded by GENDER, controlling for smoking status.

PART C: This part address contents of module 5 to 8 (3 pts each)

1. Repeat questions (1), (2), (3), (5), (6) and (7) in part (B) for the regression of QUET (Y) on AGE (X).

Attachments:

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