SAS Institute A00-240 Online Practice Questions and Exam Preparation

SAS Institute A00-240 Online Questions & Answers

• Question 81:

  What is the default method in the LOGISTIC procedure to handle observations with missing data?

  A. Missing values are imputed.
  B. Parameters are estimated accounting for the missing values.
  C. Parameter estimates are made on all available data.
  D. Only cases with variables that are fully populated are used.
  D. Only cases with variables that are fully populated are used.

• Question 82:

  Refer to the exhibit: An analyst examined logistic regression models for predicting whether a customer would make a purchase. The ROC curve displayed summarizes the models. Using the selected model and the analyst's decision rule, 25% of the customers who did not make a purchase are incorrectly classified as purchasers.

  

  What can be concluded from the graph?

  A. About 25% of the customers who did make a purchase are correctly classified as making a purchase.
  B. About 50% of the customers who did make a purchase are correctly classified as making a purchase.
  C. About 85% of the customers who did make a purchase are correctly classified as making a purchase.
  D. About 95% of the customers who did make a purchase are correctly classified as making a purchase.
  C. About 85% of the customers who did make a purchase are correctly classified as making a purchase.

• Question 83:

  Refer to the ROC curve:

  

  As you move along the curve, what changes?

  A. The priors in the population
  B. The true negative rate in the population
  C. The proportion of events in the training data
  D. The probability cutoff for scoring
  D. The probability cutoff for scoring

• Question 84:

  Fill in the Blank

  A linear model has the following characteristics:

  1.

  A dependent variable (y)

  2.

  One continuous variable (x1)

  3.

  One categorical (с1 with 3 levels) predictor variable and an interaction term (с1 by x1)

  How many parameters, including the intercept, will be estimated for this model?

  Enter your numeric answer in the space below.

  7

• Question 85:

  What is a benefit to performing data cleansing (imputation, transformations, etc.) on data after partitioning the data for honest assessment as opposed to performing the data cleansing prior to partitioning the data?

  A. It makes inference on the model possible.
  B. It is computationally easier and requires less time.
  C. It omits the training (and test) data sets from the benefits of the cleansing methods.
  D. It allows for the determination of the effectiveness of the cleansing method.
  D. It allows for the determination of the effectiveness of the cleansing method.

• Question 86:

  A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model. What will be the result?

  A. An increase in R-Square
  B. A decrease in R-Square
  C. A decrease in Mean Square Error
  D. No change in R-Square
  A. An increase in R-Square

• Question 87:

  Suppose training data are oversampled in the event group to make the number of events and non-events roughly equal. A logistic regression is run and the probabilities are output to a data set NEW and given the variable name PE. A decision rule considered is, "Classify data as an event if probability is greater than 0.5." Also the data set NEW contains a variable TG that indicates whether there is an event (1=Event, 0= No event).

  The following SAS program was used.

  

  What does this program calculate?

  A. Depth
  B. Sensitivity
  C. Specificity
  D. Positive predictive value
  B. Sensitivity

• Question 88:

  Which statistic is based on the maximum vertical distance between the primary event EDF and the secondary event EDF?

  A. KS
  B. SBC
  C. Max EDF
  D. Brier Score
  A. KS

• Question 89:

  Given the following LOGISTIC procedure:

  

  What is the difference between the datasets OUTFILEJ and OUTFILE_2?

  A. OUTFILE_1 contains the final parameter estimates while OUTFILE_2 contains the newly scored probabilities.
  B. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored probabilities
  C. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored logits.
  D. OUTFILEJ contains the final parameter estimates and Wald Chi-Square values while OUTFILE_2 contains the newly scored probabilities.
  A. OUTFILE_1 contains the final parameter estimates while OUTFILE_2 contains the newly scored probabilities.

• Question 90:

  Screening for non-linearity in binary logistic regression can be achieved by visualizing:

  A. A scatter plot of binary response versus a predictor variable.
  B. A trend plot of empirical logit versus a predictor variable.
  C. A logistic regression plot of predicted probability values versus a predictor variable.
  D. A box plot of the odds ratio values versus a predictor variable.
  B. A trend plot of empirical logit versus a predictor variable.