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

Correct Answer : Get Lastest Questions and Answer :
The standard error of the estimate is a measure of the accuracy of predictions. The regression line is the line that minimizes the sum of squared deviations of prediction (also called the sum of squares error), and the standard error of the estimate is the square root of the average squared deviation.





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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The standard error of the estimate is a measure of the accuracy of predictions. The regression line is the line that minimizes the sum of squared deviations of prediction (also called the sum of squares error), and the standard error of the estimate is the square root of the average squared deviation.




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

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The REG procedure is one of many regression procedures in the SAS System. It is a general-purpose procedure for regression, while other SAS regression procedures provide more specialized applications.
Other SAS/STAT procedures that perform at least one type of regression analysis are the CATMOD, GENMOD, GLM, LOGISTIC, MIXED, NLIN, ORTHOREG, PROBIT, RSREG, and TRANSREG procedures
PROC REG provides the following capabilities:
" multiple MODEL statements
" nine model-selection methods
" interactive changes both in the model and the data used to fit the model
" linear equality restrictions on parameters
" tests of linear hypotheses and multivariate hypotheses
" collinearity diagnostics
" predicted values, residuals, studentized residuals, confidence limits, and influence statistics
" correlation or crossproduct input
" requested statistics available for output through output data sets
" ODS Graphics is now available. For more information, see the section ODS Graphics. These plots are available in addition to the line printer and the traditional graphics currently available in PROC REG.

The PROC REG statement is required. If you want to fit a model to the data, you must also use a MODEL statement. If you want to use only the PROC REG options, you do not need a MODEL statement, but you must use a VAR statement. If you do not use a MODEL statement, then the COVOUT and OUTEST= options are not available.
MODEL Statement
After the keyword MODEL, the dependent (response) variables are specified, followed by an equal sign and the regressor variables. Variables specified in the MODEL statement must be numeric variables in the data set being analyzed. For example, if you want to specify a quadratic term for variable X1 in the model, you cannot use X1*X1 in the MODEL statement but must create a new variable (for example, X1SQUARE=X1*X1) in a DATA step and use this new variable in the MODEL statement. The label in the MODEL statement is optional.

Refer Study notes as well.



Related Questions

Question : Refer to the exhibit.
Based on the control plot, which conclusion is
justified regarding the means of the response?

 : Refer to the exhibit.
1. All groups are significantly different from each other.
2. 2XL is significantly different from all other groups
3. Only XL and 2XL are not significantly different from each other.
4. No groups are significantly different from each other.


Question : Customers were surveyed to assess their intent to purchase a product. An analyst divided the customers
into groups defined by the company's pre-assigned market segments and tested for difference in the customers'
average intent to purchase. The following is the output from the GLM procedure:
What percentage of customers' intent to purchase is explained by market segment?
 : Customers were surveyed to assess their intent to purchase a product. An analyst divided the customers
1. less than 0.01%
2. 35%
3. 65%
4. 76%



Question : Refer to the exhibit:
The box plot was used to analyze daily sales data following three different ad campaigns.
The business analyst concludes that one of the assumptions of ANOVA was violated.
Which assumption has been violated and why?
 : Refer to the exhibit:
1. Normality, because Prob > F less than .0001.
2. title Normality, because the interquartile ranges are different in different ad campaigns.
3. Constant variance, because Prob > F less than .0001.
4. Constant variance, because the interquartile ranges are different in different ad campaigns




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?
 : Refer to the exhibit.
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?
: An analyst compares the mean salaries of men and women working at a company.
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?
 : Given the following GLM procedure output:
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.