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Use NHANS2 data to create descriptive summary for the following variables.

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

 

1. Descriptive Statistics

 

a) Use NHANS2 data to create descriptive summary for the following variables. For the continuous data, create a table that reports variable name, variable description, number of observations, mean, standard deviation, minimum, and maximum. For the categorical data, create a table that reports variable name, variable description, and category frequency and percentage of the total.

 

To receive full credit, make sure that your tables are professionally and aesthetically formatted and presented. You may consult journal article publications or research reports to see how they are done.

 

age, agegrp, bmi, bpdiast, bpsystol, diabetes, heartatk, height, hlthstat, houssiz, race, rural, sex, tcresult, tgresult, weight

 

b) For the variables bmi, bpdiast, and bpsystol create histograms that also have the normal curve overlayed. Graphically (histogram) and numerically test (e.g. Shapiro-Wilk Test) if these variables are normally distributed. Discuss what you learn from these histograms and tests.

 

2. Continuous Data Analysis

 

Using NHANS2 data, conduct hypothesis testing to answer the following research questions. In order to receive full credit, for each question below, you must explain which test you will use and why and provide a detailed explanation of your conclusions.

 

In these examples, given the large sample size, you should assume that the variables are approximately normally distributed.

 

a) Is the average bmi in the sample 25?

b) Is the average bmi for men and women equal?.

c) Is the average bmi for individuals of various races (black, white, other) equal?

 

d) Is the average bpsystol in the sample 131?

e) Is the average bpsystol for men and women equal?.

f) Is the average bpsystol for individuals of various races (black, white, other) equal?

 

g) Test if the mean serum cholesterol levels in the sample are different by gender, race, and their interaction.

 

 

3. Categorical Data Analysis

 

a) In our sample, race is represented by three categories (1=white, 2=black, 3=other). The US Census Bureau reports1 that in 2019, 76.3% of the US population were White, 13.4% were African American, and the rest were of other races. Using data from NHANS2, test whether the observed percentages for the race categorical variable are significantly different from expected percentages reported by the Census Bureau. Explain in detail which test you would use and what your conclusions are.

 

b) Use body mass index (BMI) variable in the NHANS2 data to create a categorical BMI variable with 4 categories that correspond to the standard BMI category ranges (underweight, normal, overweight, obese), which can be found here: https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html (look under “How is BMI interpreted for adults?”). Name the new categorical variable bmi2.

 

Note: in creating this new variable, make sure you code the ranges in a way that includes all values within the cutoff points. In our data, the variable bmi has 4 decimal digits but BMI cutoff points have a single digit. Therefore, if your first category is, say, “less than 7.5”and the 2nd category is 7.5-10.5, then if you only use single decimal digit value in coding in Stata (e.g. coding values between 0 and 7.4 as category 1), then 7.4 will be within the range of the 1st category. Since the 2nd category starts with 7.5, the value of 7.41 or 7.4999 will not be in the 1st category where it belongs. Therefore, if you code ranges using less than 4 decimal values, it will omit many values that have 4 decimals. To avoid this, make sure your value ranges are specified with 4 decimal points (e.g. 7.4999). Also, you should always check what your new variable looks like. When tabulating the new variable, if done correctly, it must only have 4 categories. You can use various methods to accomplish this in Stata. The easiest is to use recode command.

 

Finally, make sure all categories have meaningful labels, such as underweight, normal, overweight, and obese. Using recode command will help with this too. Please refer to the class example I demonstrated and/or Stata manual. Once done, display your new variable in a well formatted and professionally looking frequency table and a vertical bar graph. Hint: for the latter, you can create a histogram which displays percentages on the vertical axis.

 

Clarification on the Fisher’s Exact test. This test is used when the expected (not observed) cell value is less than 5. To calculate the expected values, you can use “, expected “ option with tabulate twoway command in Stata. For example: tab sex race, expected

 

Using the NHANS2 dataset, answer research questions (c) to (f) below. Explain in detail which test you would use and what your conclusions are.

c) Do men and women have the same risk of diabetes?

d) Is the risk of a heart attack the same for individuals of all races?

e) Is the risk of a heart attack the same for individuals of different BMI levels?

f) Is there a relationship between being diabetic and suffering from a heart attack?

 

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