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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When building a predictive model with logistic regression, when should you choose an appropriate link function?

A) Based on the nature of the response variable and the problem
B) After specifying the number of predictors
C) After specifying the learning rate
D) Before assigning roles to variables

2. Which equation does NOT represent a linear model? Note: bi are parameters and Xi are variables.

A) y = b0 + b1X1 + b2X1 3
B) y = b0 + b1X1 + b2X2 + b3(X1X2)
C) y = b0 + b1X1 + (b2/b1)X2
D) y = b0 + b1X1 + b2X2

3. Given a scenario where the response variable represents the number of accidents in a day, what distribution and link function might be appropriate for modeling?

A) Logistic distribution with a logit link function
B) Exponential distribution with a square root link function
C) Normal distribution with an identity link function
D) Poisson distribution with a log link function

4. How is prediction typically performed in logistic regression?

A) By estimating the probability of an event for each observation
B) By maximizing the likelihood of the response variable
C) By fitting a straight line through the data points
D) By calculating the mean of the response variable

5. What is meant by "informative missingness" in the context of linear regression?

A) Missing data that is randomly distributed across observations
B) Missing data that leads to multicollinearity among predictors
C) Missing data that provides valuable information about the outcome
D) Missing data that is unrelated to the outcome variable

Solutions: