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SAS_Institute

A00-240 · Question #9

A marketing manager attempts to determine those customers most likely to purchase additional products as the result of a nation-wide marketing campaign. The manager possesses a historical dataset (CAM

The correct answer is A. proc logistic data=MYDIR.CAMPAIGN descending; class Homeowner; model Respond = Income Homeowner; run;. Option A is correct because it includes both the class Homeowner; statement - required to tell SAS that Homeowner is a categorical (nominal) variable rather than continuous - and the descending option, which instructs PROC LOGISTIC to model the probability of Respond=1 (a purchas

Logistic Regression

Question

A marketing manager attempts to determine those customers most likely to purchase additional products as the result of a nation-wide marketing campaign. The manager possesses a historical dataset (CAMPAIGN) of a similar campaign from last year. It has the following characteristics:
  • Target variable Respond (0, 1)
  • Continuous predictor Income
  • Categorical predictor Homeowner(Y, N) Which SAS program performs this analysis?

Options

  • Aproc logistic data=MYDIR.CAMPAIGN descending; class Homeowner; model Respond = Income Homeowner; run;
  • Bproc logistic data = MYDIR.CAMPAIGN descending; by Homeowner; model Respond = Income Homeowner; run;
  • Cproc logistic data = MYDIR.CAMPAIGN descending; model Respond = Income Homeowner; run;
  • Dproc logistic data = MYDIR.CAMPAIGN descending; class Homeowner; model Respond = Income Homeowner; run;

How the community answered

(25 responses)
  • A
    80% (20)
  • B
    12% (3)
  • C
    4% (1)
  • D
    4% (1)

Explanation

Option A is correct because it includes both the class Homeowner; statement - required to tell SAS that Homeowner is a categorical (nominal) variable rather than continuous - and the descending option, which instructs PROC LOGISTIC to model the probability of Respond=1 (a purchase) instead of the default Respond=0.

Option B is wrong because by Homeowner; runs separate logistic models for each level of Homeowner (Y and N), rather than including it as a predictor variable in a single model - this does not answer the manager's question.

Option C is wrong because it omits the class statement; without it, SAS treats Homeowner as a numeric/continuous variable, which is inappropriate for a Y/N categorical predictor and will produce misleading results.

Option D appears structurally identical to A in this rendering, but in the original exam context likely contains a subtle error (e.g., a different dataset path, missing library reference, or omitted descending) - always read option details character by character on exams.

Memory tip: Think "Categorical needs Class" - any time a predictor has character/text values (like Y/N), you must declare it with a class statement, and pair descending with binary targets whenever you want to model the event (1) rather than the non-event (0).

Topics

#PROC LOGISTIC#Categorical variables#Binary classification#Model specification

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