STAT 5260: DATA ANALYSIS USING SAS HOMEWORK HELP

STAT 5260: DATA ANALYSIS USING SAS

Homework Set 3

Answer to each part of each problem should start on a fresh sheet. Order your answer sheets in the order of the problems and the parts within each problem. Please do not submit SAS code you used or any unrequested results.

            In the table on the 3^rdpage, you will find the national record for women for the track events 100, 200, 400, 800, 1,600, 3,000, meters and the marothon just prior to the 1984 Olympics, for a select set of countries.  Each line in this data set can be looked at as a profile of the corresponding country where the track record times for each distance acting as independent variables associated with the country. Please do the following and use the Answer sheets posted together with this homework to submit your naswers.

• Using this information, carry out a principal componat analysis. Please use the correlation matrix(not the covariance matrix) to carry out the PCA. You do this by NOT specifying COV as an option in the Proc PRINCOMP command. This way, every variable will play an equal role in determining the principal components. Otherwise variables with larger values will play a bigger role.
• What proportion of the variation in the data is explained by the first two principal components?
• Draw a 2-Dimensional plot using the first two principal components as the axes. Please make sure that each data point in the plot is identified by the corresponding country name. Make sure you read the data with the variable name country given to the country name.
• Please interpret the what the graphs says about the U.S., China, USSR, Kenya, and GDR (this is former East Germany).

Diabetes in Pima Indians

A population of women who were at least 21 years old, of Pima Indian heritage and living near Phoenix, Arizona, was tested for diabetes according to World Health Organization criteria. The data were collected by the US National Institute of Diabetes and Digestive and Kidney Diseases (Smith et al, 1988). This example is also contained in the UCI machine-learning database collection (Murphy & Aha, 1995).

The data files have rows containing

npreg -- number of pregnancies

glu -- plasma glucose concentration in an oral glucose tolerance test

bp -- diastolic blood pressure (mm Hg)

skin -- triceps skin fold thickness (mm)

ins -- serum insulin (micro U/ml)  - Missing from data

bmi -- body mass index (weight in kg/(height in m)^2)

ped -- diabetes pedigree function

age -- in years

type -- No / Yes  Note: Yes means the individual is diabetic, and no means they are not.

The tasks you have to perform are given below.

• Copy and paste the final SAS program you used to both estimate and model and predict the diabetic status of the individuals in the test data set.

• Provide the cross classification table for the estimation data.

• Provide the cross classification table for the test data.

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