SAS Certified BI Content Developer for SAS 9 and Business Analytics Questions and Answer (Dumps and Practice Questions)

Question : Suppose that we are interested in the factors that influence whether a political candidate wins an election.
The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent
on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.
Above is an example of

1. Linear Regression
2. Logistic Regression
3. Maximum likelihood estimation
4. Hierarchical linear models

Correct Answer : 2





Question : A researcher is interested in how variables, such as GRE (Graduate Record Exam scores),
GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school.
The response variable, admit/don't admit, is a binary variable.

Above is an example of


1. Linear Regression
2. Logistic Regression
3. Maximum likelihood estimation
4. Hierarchical linear models

Correct Answer : 2





Question : Which of the following is an correct example of the target variable in regression (supervised learning) ?

1. Nominal values like true, false
2. Reptile, fish, mammal, amphibian, plant, fungi
3. Infinite number of numeric values, such as 0.100, 42.001, 1000.743..
4. All 1,2 and 3

Correct Answer : 4
Explanation: We address two cases of the target variable. The first case occurs when the target variable can take only nominal values: true or false; reptile, fish, mammal, amphibian, plant, fungi. The second case of classification occurs when the target variable can take an infinite number of numeric values, such as 0.100, 42.001, 1000.743, .... This case is called egression.

Related Questions

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Question : Refer to the exhibit.
Given alpha=0.02, which conclusion is justified regarding percentage of body fat,
comparing small(S), medium (M), and large (L) wrist sizes?

1. Medium wrist size is significantly different than small wrist size.
2. Large wrist size is significantly different than medium wrist size.
3. Large wrist size is significantly different than small wrist size.
4. There is no significant difference due to wrist size.


Question : An analyst compares the mean salaries of men and women working at a company.
The SAS data set SALARY contains variables:
Gender (M or F)
Pay (dollars per year)
Which SAS programs can be used to find the p-value for comparing men's salaries with women's
salaries?

1. A,B
2. B,C
3. C,D
4. A,D


Question : Given the following GLM procedure output:
Which statement is correct at an alpha level of 0.05?

1. School*Gender should be removed because it is non-significant.
2. Gender should be removed because it is non-significant.
3. School should be removed because it is significant.
4. Gender should not be removed due to its involvement in the significant interaction.


Question : There are missing values in the input variables for a regression application.
Which SAS procedure provides a viable solution?

1. GLM
2. VARCLUS
3. STDIZE
4. CLUSTER


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

1. A scatter plot of binary response versus a predictor variable.
2. A trend plot of empirical logit versus a predictor variable.
3. A logistic regression plot of predicted probability values versus a predictor variable.
4. A box plot of the odds ratio values versus a predictor variable.


Question : Given the SAS data set TEST:
Which SAS program is NOT a correct way to create dummy variables?

1. A
2. B
3. C
4. D