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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 REG procedure output:
The Intercept estimate is interpreted as:
: Refer to the REG procedure output:
1. The predicted value of the response when all the predictors are at their current values.
2. The predicted value of the response when all predictors are at their means.
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4. The predicted value of the response when all predictors are at their minimum values.

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Question : A linear model has the following characteristics:
A dependent variable (y)
Three continuous predictor variables (x1-x3)
One categorical predictor variable (c1with 3 levels)
Which SAS program fits this model?

: A linear model has the following characteristics:
1. A
2. B
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4. D

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The CLASS statement names the classification variables to be used in the model. Typical classification variables are TREATMENT, SEX, RACE, GROUP, and REPLICATION. If you specify the CLASS statement, it must appear before the MODEL statement.
By default, class levels are determined from the entire formatted values of the CLASS variables. Note that this represents a slight change from previous releases in the way in which class levels are determined. Before SAS 9, class levels were determined using no more than the first 16 characters of the formatted values. If you want to revert to this previous behavior, you can use the TRUNCATE option in the CLASS statement. In any case, you can use formats to group values into levels.
See the discussion of the FORMAT procedure in the Base SAS Procedures Guide, and the discussions of the FORMAT statement and SAS formats in SAS Language Reference: Dictionary.
The GLM procedure displays a table summarizing the CLASS variables and their levels, and you can use this to check the ordering of levels and, hence, of the corresponding parameters for main effects. If you need to check the ordering of parameters for interaction effects, use the E option in the MODEL, CONTRAST, ESTIMATE, and LSMEANSstatements. See the section Parameterization of PROC GLM Models for more information.
You can specify the following option in the CLASS statement after a slash (/):
TRUNCATE
specifies that class levels should be determined using only up to the first 16 characters of the formatted values of CLASS variables. When formatted values are longer than 16 characters, you can use this option in order to revert to the levels as determined in releases previous to SAS 9.
The GLM procedure uses the method of least squares to fit general linear models. Among the statistical methods available in PROC GLM are regression, analysis of variance, analysis of covariance, multivariate analysis of variance, and partial correlation.
PROC GLM analyzes data within the framework of general linear models. PROC GLM handles models relating one or several continuous dependent variables to one or several independent variables. The independent variables can be either classification variables, which divide the observations into discrete groups, or continuous variables. Thus, the GLM procedure can be used for many different analyses, including the following:
" simple regression
" multiple regression
" analysis of variance (ANOVA), especially for unbalanced data
" analysis of covariance
" response surface models
" weighted regression
" polynomial regression
" partial correlation
" multivariate analysis of variance (MANOVA)
" repeated measures analysis of variance
The MODEL statement names the dependent variables and independent effects. The syntax of effects is described in the section Specification of Effects. For any model effect involving classification variables (interactions as well as main effects), the number of levels cannot exceed 32,767. If no independent effects are specified, only an intercept term is fit. You can specify only one MODEL statement (in contrast to the REG procedure, for example, which allows several MODEL statements in the same PROC REG run).





Question : Which SAS program will detect collinearity in a multiple regression application?
: Which SAS program will detect collinearity in a multiple regression application?
1. A
2. B
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4. D

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Related Questions

Question : An analyst investigates Region (A, B, or C) as an input variable in a logistic regression model.
The analyst discovers that the probability of purchasing a certain item when Region = A is 1. What problem does this illustrate?
 : An analyst investigates Region (A, B, or C) as an input variable in a logistic regression model.
1. Collinearity
2. Influential observations
3. Quasi-complete separation
4. Problems that arise due to missing values


Question : Refer to the following exhibit:
What is a correct interpretation of this graph?

 : Refer to the following exhibit:
1. The association between the continuous predictor and the binary response is quadratic.
2. The association between the continuous predictor and the log-odds is quadratic.
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4. The association between the binary predictor and the log-odds is quadratic.


Question : This question will ask you to provide a missing option.
Given the following SAS program:
What option must be added to the program to obtain a
data set containing Pearson statistics?
 : This question will ask you to provide a missing option.
1. OUTPUT=estimates
2. OUTP=estimates
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4. OUTCORR=estimates


Question : A predictive model uses a data set that has several variables with missing values.
What two problems can arise with this model?

A. The model will likely be overfit.
B. There will be a high rate of collinearity among input variables.
C. Complete case analysis means that fewer observations will be used in the model building process.
D. New cases with missing values on input variables cannot be scored without extra data processing.

 : A predictive model uses a data set that has several variables with missing values.
1. A,B
2. B,C
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4. A,D


Question : Spearman statistics in the CORR procedure are useful for screening for irrelevant variables by
investigating the association between which function of the input variables?
 : Spearman statistics in the CORR procedure are useful for screening for irrelevant variables by
1. Concordant and discordant pairs of ranked observations
2. Logit link (log(p/1-p))
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4. Weighted sum of chi-square statistics for 2x2 tables


Question : A non-contributing predictor variable (Pr > |t| =.) is added to an existing multiple linear regression model. What will be the result?
 : A non-contributing predictor variable (Pr > |t| =.) is added to an existing multiple linear regression model. What will be the result?
1. An increase in R-Square
2. A decrease in R-Square
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4. No change in R-Square