質問 1:Which characteristic of Studentized residuals indicate potential outliers?
A. Only studentized residuals less than two and greater than negative two
B. Only studentized residuals greater than negative two
C. Only studentized residuals greater than two
D. Only studentized residuals less than negative two and greater than two
正解:C
質問 2:Given the following LOGISTIC procedure:
What is the difference between the datasets OUTFILEJ and OUTFILE_2?
A. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored probabilities
B. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored logits.
C. OUTFILEJ contains the final parameter estimates and Wald Chi-Square values while OUTFILE_2 contains the newly scored probabilities.
D. OUTFILE_1 contains the final parameter estimates while OUTFILE_2 contains the newly scored probabilities.
正解:D
質問 3:Refer to the exhibit.
Based on the control plot, which conclusion is justified regarding the means of the response?
A. All groups are significantly different from each other.
B. No groups are significantly different from each other.
C. Only XL and 2XL are not significantly different from each other.
D. 2XL is significantly different from all other groups.
正解:C
質問 4:Drag the adjustment formulas for oversamping from the left and place them into the correct location in the confusion matrix shown on the right.
正解:
質問 5:A confusion matrix is created for data that were oversampled due to a rare target.
What values are not affected by this oversampling?
A. Sensitivity and Specificity
B. Specificity and PV-
C. Sensitivity and PV+
D. PV+ and PV-
正解:A
質問 6:A linear model has the following characteristics:
* A dependent variable (y)
* One continuous variable (x1)
* One categorical (1 with 3 levels) predictor variable and an interaction term (1 by x1) How many parameters, including the intercept, will be estimated for this model?
正解:
Enter your numeric answer in the space below.
7
質問 7:Refer to the REG procedure output:
Calculate the coefficient of determination, R-Square.
正解:
Enter your numeric answer in the space below. Round to 4 decimal places (example: n.nnnn).
0.5671
質問 8:When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?
A. The sample means from the validation data set are applied to the training and test data sets.
B. The sample means from each partition of the data are applied to their own partition.
C. The sample means from the training data set are applied to the validation and test data sets.
D. The sample means from the test data set are applied to the training and validation data sets.
正解:C
質問 9:Refer to the exhibit:
An analyst examined logistic regression models for predicting whether a customer would make a purchase.
The ROC curve displayed summarizes the models. Using the selected model and the analyst's decision rule,
25% of the customers who did not make a purchase are incorrectly classified as purchasers.
What can be concluded from the graph?
A. About 50% of the customers who did make a purchase are correctly classified as making a purchase.
B. About 25% of the customers who did make a purchase are correctly classified as making a purchase.
C. About 85% of the customers who did make a purchase are correctly classified as making a purchase.
D. About 95% of the customers who did make a purchase are correctly classified as making a purchase.
正解:C
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SASInstitute A00-240 認定試験の出題範囲:
トピック | 出題範囲 |
---|
トピック 1 | - Screen variables for non-linearity using empirical logit plots
- Score new data sets using the LOGISTIC and PLM procedures
|
トピック 2 | - Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
- Improve the predictive power of categorical inputs
|
トピック 3 | - Screen variables for irrelevance and non-linear association using the CORR procedure
- Perform logistic regression with the LOGISTIC procedure
|
トピック 4 | - Identify the potential challenges when preparing input data for a model
- Detect and analyze interactions between factors
|
トピック 5 | - Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
- Use the REG or GLMSELECT procedure to perform model selection
|
トピック 6 | - Analyze differences between population means using the GLM and TTEST procedures
- Assess classifier performance using the confusion matrix
|
参照:https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html
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