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



Question : Refer to the following odds ratio table:
What is a correct interpretation of the estimate?
: Refer to the following odds ratio table:
1. The odds of the event are 1.142 greater for each one dollar increase in salary.
2. The odds of the event are 1.142 greater for each one thousand dollar increase in salary.
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4. The probability of the event is 1.142 greater for each one thousand dollar increase in salary.

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Let's begin with probability. Let's say that the probability of success is .8, thus
p = .8
Then the probability of failure is
q = 1 - p = .2
The odds of success are defined as
odds(success) = p/q = .8/.2 = 4,
that is, the odds of success are 4 to 1. The odds of failure would be
odds(failure) = q/p = .2/.8 = .25.
This looks a little strange but it is really saying that the odds of failure are 1 to 4. The odds of success and the odds of failure are just reciprocals of one another, i.e., 1/4 = .25 and 1/.25 = 4. Next, we will add another variable to the equation so that we can compute an odds ratio.

2. This example is adapted from Pedhazur (1997). Suppose that seven out of 10 males are admitted to an engineering school while three of 10 females are admitted. The probabilities for admitting a male are,
p = 7/10 = .7 q = 1 - .7 = .3
Here are the same probabilities for females,
p = 3/10 = .3 q = 1 - .3 = .7
Now we can use the probabilities to compute the admission odds for both males and females,
odds(male) = .7/.3 = 2.33333
odds(female) = .3/.7 = .42857
Next, we compute the odds ratio for admission,
OR = 2.3333/.42857 = 5.44
Thus, for a male, the odds of being admitted are 5.44 times as large than the odds for a female being admitted.






Question : Which method is NOT an appropriate way to score new observations with a known target in a logistic regression model?
: Which method is NOT an appropriate way to score new observations with a known target in a logistic regression model?
1. Use the SCORE statement in the LOGISTIC procedure.
2. Augment the training data set with new observations and set their responses to missing.
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4. Use the saved parameter estimates from the LOGISTIC procedure and score new observations in the SCORE procedure.

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Question : Consider scoring new observations in the SCORE procedure versus the SCORE statement in the LOGISTIC procedure.
Which statement is true?

: Consider scoring new observations in the SCORE procedure versus the SCORE statement in the LOGISTIC procedure.
1. The SCORE statement in the LOGISTIC procedure returns only predicted probabilities, whereas the SCORE procedure returns only predicted logits.
2. The SCORE statement in the LOGISTIC procedure returns only predicted logits, whereas the SCORE procedure returns only predicted probabilities.
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4. The SCORE procedure and the SCORE statement in the LOGISTIC procedure produce the same output.

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The PROC LOGISTIC statement invokes the LOGISTIC procedure and optionally identifies input and output data sets, suppresses the display of results, and controls the ordering of the response levels

The SCORE statement creates a data set that contains all the data in the DATA= data set together with posterior probabilities and, optionally, prediction confidence intervals. Fit statistics are displayed on request. If you have binary response data, the SCORE statement can be used to create a data set containing data for the ROC curve. You can specify several SCORE statements. FREQ, WEIGHT, and BY statements can be used with the SCORE statements. The SCORE statement is not available with the STRATA statement.
If a SCORE statement is specified in the same run as fitting the model, FORMAT statements should be specified after the SCORE statement in order for the formats to apply to all theDATA= and PRIOR= data sets in the SCORE statement.

The SCORE procedure multiplies the values from two SAS data sets and creates a new data set to contain the results of the multiplication. The variables in the new data set are linear combinations of the variables in the two input data sets. Typically, one of these data sets contains raw data that you want to score, and the other data set contains scoring coefficients.

Many statistical procedures output coefficients that PROC SCORE can apply to raw data to produce scores. The new score variable is formed as a linear combination of raw data and scoring coefficients. For each observation in the raw data set, PROC SCORE multiplies the value of a variable in the raw data set by the matching scoring coefficient from the data set of scoring coefficients. This multiplication process is repeated for each variable in the VAR statement. The resulting products are then summed to produce the value of the new score variable. This entire process is repeated for each observation in the raw data set. In other words, PROC SCORE cross multiplies part of one data set with another.





Related Questions

Question : The standard form of a linear regression model is:
Which statement best summarizes the assumptions placed on the errors?
 : The standard form of a linear regression model is:
1. The errors are correlated, normally distributed with constant mean and zero variance.
2. The errors are correlated, normally distributed with zero mean and constant variance.
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4. The errors are independent, normally distributed with zero mean and constant variance.


Question : In a regression line, the ________ the standard error of the estimate is, the more accurate the predictions are.
: In a regression line, the ________ the standard error of the estimate is, the more accurate the predictions are.
1. larger
2. smaller
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Question : Identify the correct SAS program for fitting a multiple linear regression model with
dependent variable (y) and four predictor variables (x1-x4).

 : Identify the correct SAS program for fitting a multiple linear regression model with
1. A
2. B
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4. D


Question : Refer to the REG procedure output:
An analyst has selected this model as a champion because it shows better model
fit than a competing model with more predictors.
Which statistic justifies this rationale?
 : Refer to the REG procedure output:
1. R-Square
2. Coeff Var
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4. Error DF


Question : The selection criterion used in the forward selection method in the REG procedure is:
 : The selection criterion used in the forward selection method in the REG procedure is:
1. Adjusted R-Square
2. SLE
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4. AIC


Question : Which SAS program will correctly use backward elimination selection criterion within the REG procedure?
 : Which SAS program will correctly use backward elimination selection criterion within the REG procedure?
1. A
2. B
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4. D