What’s wrong with this program?

Using data set CLINICAL from Problem 4.4, create a new SAS data set (CI-IANGE) with one observation per subject with the difference in WEIGHT between the first and last visit. Include in this data set the number of days between the first and last visit. Do not include any patients who have only one visit.

Write a program similar to Problem 4.5, except that we want to include all the data for each patient, excluding any patient who has had only one visit. Instead of having PROC MEANS create printed output, use it to create a SAS data set (PAT_MEAN) containing the mean for each patient (use the AUTO NAME

You want to randomly assign 48 subjects into two groups (Placebo and Drug). Write a DATA step to do this in such a way as to ensure there are exactly 24 subjects in each group. Can you think of how to do this so that in each group of eight subjects, there are exactly four

The same data as in problem 8.3 are to be analyzed. However, they are arranged so that the four ratings from each judge are on one tine. Thus, columns 1-3 are for the judge ID, column 4 is the rating for New York, column 5 for New Orleans, column 6 for Chicago, and column 7

Using the data set from problem 9.8, conduct a logistic regression predicting outcome (case or control) using SMOKING,ASBESTOS. and SES. Use a CLASS statement to create two dummy variables for SES, setting the reference level to ‘2-Mediurn’. (HINT: CLASS SES (PARAM=REF REF=’2-Medium’); PROC LOGISTIC uses the lower value formatted value of the outcome variable (if

Add the necessary statements to your logistic regression in problem 9.8 to produce an ROC curve (sensitivity versus 1 – specificity). Use the OUTROC MODEL option to create a data set so you can plot the curve. Use PROC GPLOT (or PROC PLOT) to generate this curve.

Repeat problem 9.8, except this time create your own dummy variables (i.e., do not use a CLASS statement). Make the reference level for SES ‘2-Medium’ as before. Problem 9.8 Run the following program to create a SAS data set called SMOKING. Outcome values of 1 represent cases (those with lung disease) and outcome values of

Accident data, similar to Section G, are presented here. This time. we recorded accidents that occurred in the past year, based on the presence of a drinking problem and whether the driver had one or more accidents in the previous year. Run a forward stepwise logistic regression on these data, and write the resulting logistic

Run a factor analysis on the questionnaire data in Chapter 3, Section B. Use only the variables PRES, ARMS, and CITIES. Request two factors, VARIMAX rotation method, and set the PRIORS estimate of conununality to SMC. Include the option to generate a scree plot.