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A00-240 - SAS Statistical Business Analysis SAS9: Regression and Model - Dump Information

Vendor : SASInstitute
Exam Code : A00-240
Exam Name : SAS Statistical Business Analysis SAS9: Regression and Model
Questions and Answers : 65 Q & A
Updated On : July 21, 2017
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A00-240 Questions and Answers

A00-240


Answer: D


QUESTION: 62

The question will ask you to provide a missing statement. Given the following SAS program:


Which SAS statement will complete the program to correctly score the data set NEW_DATA?


  1. Scoredata data=MYDIR.NEW_DATA out=scores;

  2. Scoredata data=MYDIR.NEW_DATA output=scores;

  3. Scoredata=HYDIR.NEU_DATA output=scores;

  4. Scoredata=MYDIR,NEW DATA out=scores;


Answer: D


QUESTION: 63

A marketing manager attempts to determine those customers most likely to purchase additional products as the result of a nation-wide marketing campaign. The manager possesses a historical dataset (CAMPAIGN) of a similar campaign from last year.

It has the following characteristics:


  1. Option A

  2. Option B

  3. Option C

  4. Option D


Answer: A


QUESTION: 64

Given the following output from the LOGISTIC procedure:


Which variables, among those that are statistically significant at an alpha of 0.05, have the greatest and least relative importance on the fitted model?


  1. Greatest: MBA Least: DOWN_AMT

  2. Greatest: MBA Least: CASH

  3. Greatest: DOWN_AMT Least: CASH


Answer: C


QUESTION: 65

What is the default method in the LOGISTIC procedure to handle observations with missing data?


  1. Missing values are imputed.

  2. Parameters are estimated accounting for the missing values.

  3. Parameter estimates are made on all available data.

  4. Only cases with variables that are fully populated are used.


Answer: D


SASInstitute A00-240 Exam (SAS Statistical Business Analysis SAS9: Regression and Model) Detailed Information

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    SAS Statistical Business Analysis SAS9: Regression and Model

    SAS Statistical Business Analysis Using SAS 9-Regression and Modeling.">A00-240.v5-2.2016-02-06.1e.65q.exam 3.49 Mb Feb 09, 2016 SAS Statistical Business Analysis Using SAS 9-Regression and Modeling.">A00-240.v5-2.2015-11-17.1e.65q.exam 3.41 Mb Nov 20, 2015Article by ArticleForge

    A. 2 b. 0 c. 6 d. 4 answer: a question no: 132 the

    Unformatted text preview: A. 2 B. 0 C. 6 D. 4 Answer: A QUESTION NO: 132 The SAS data set EMPLOYEE_INFO is listed below: SAS Institute A00-201: Practice Exam "Pass Any Exam. Any Time." - .actualtestsm 76 A c t u a l T e s t s . c o m The following SAS program is submitted: proc sort data = employee_info; <insert BY statement here> run; Which one of the following BY statements completes the program and sorts the data sequentially by ascending expense values within each ascending IDNUMBER value? A. by IDNumber Expenses; B. by Expenses IDNumber; C. by ascending IDNumber ascending Expenses; D. by ascending (IDNumber Expenses); Answer: A QUESTION NO: 133 The following SAS program is submitted and reads 100 records from a raw data file: data work.total; infile 'file-specification' end = eof; input name $ salary; totsal + salary; <insert IF statement here> run; Which one of the following IF statements writes the last observation to the output data set? A. if end = 1; B. if eof = 1; C. if end = 0; D. if eof = 0; Answer: B SAS Institute A00-201: Practice Exam "Pass Any Exam. Any Time." - .actualtestsm 77 A c t u a l T e s t s . c o m QUESTION NO: 134 The following SAS program is submitted: <insert ODS statement here> proc means data = sasuser.shoes; where product in ('Sandal' , 'Slipper' , 'Boot'); run; Which one of the following ODS statements completes the program and sends the report to an HTML file? A. ods html = 'sales.html'; B. ods file html = 'sales.html'; C. ods file = 'sales.html'; D. ods html file = 'sales.html'; Answer: D QUESTION NO: 135 The following SAS program is submitted: data _null_; set old (keep = prod sales1 sales2); file 'file-specification'; put sales1 sales2; run; Which one of the following default delimiters separates the fields in the raw data file created? A. (space) B. : (colon) C. , (comma) D. ; (semicolon) Answer: A QUESTION NO: 136 The following SAS program is submitted: SAS Institute A00-201: Practice Exam "Pass Any Exam. Any Time." - .actualtestsm 78 A c t u a l T e s t s . c o m proc means data = sasuser.houses std mean max; var sqfeet; run; Which one of the following is needed to display the standard deviation with only two decimal places? A. Add the option FORMAT = 7.2 option to the MEANS procedure statement. B. Add the option MAXDEC = 2 to the MEANS procedure statement. C. Add the statement MAXDEC = 7.2; in the MEANS procedure step. D. Add the statement FORMAT STD 7.2; in the MEANS procedure step. Answer: B QUESTION NO: 137 The SAS data set EMPLOYEE_INFO is listed below: The following SAS program is submitted: proc sort data = employee_info; <insert BY statement here> run; Which one of the following BY statements completes the program and sorts the data sequentially by descending expense values within each descending IDNUMBER value? A. by IDNumber descending Expenses descending; B. by descending IDNumber descending Expenses; C. by (IDNumber Expenses) descending; D. by descending IDNumber Expenses; Answer: B QUESTION NO: 138 SAS Institute A00-201: Practice Exam "Pass Any Exam. Any Time." - .actualtestsm 79 A c t u a l T e s t s . c o m The SAS data set SASUSER.HOUSES contains a variable PRICE which has been assigned a permanent label of "Asking Price"....View Full Document

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    SAS Global Certification program

    Required Exam Candidates who earn this credential will have earned a passing score on the SAS Statistical Business Analysis Using SAS 9: Regression and Modeling exam. This exam is administered by SAS and Pearson VUE.
  • 60 scored multiple-choice and short-answer questions (must achieve score of 68% correct to pass)
  • In addition to the 60 scored items, there may be up to 5 unscored items.
  • 2 hours to complete exam
  • Use exam ID ">A00-240; required when registering with Pearson VUE.
  • Expand All Collapse All Exam topics include: ANOVA - 10% Verify the assumptions of ANOVA
  • Explain the central limit theorem and when it must be applied
  • Examine the distribution of continuous variables (histogram, box-whisker, Q-Q plots)
  • Describe the effect of skewness on the normal distribution
  • Define H0, H1, Type III error, statistical power, p-value
  • Describe the effect of sample size on p-value and power
  • Interpret the results of hypothesis testing
  • Interpret histograms and normal probability charts
  • Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
  • Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
  • For a given experiment, verify that the observations are independent
  • For a given experiment, verify the errors are normally distributed
  • Use the UNIVARIATE procedure to examine residuals
  • For a given experiment, verify all groups have equal response variance
  • Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance
  • Analyze differences between population means using the GLM and TTEST procedures
  • Use the GLM Procedure to perform ANOVA
  • CLASS statement
  • MODEL statement
  • MEANS statement
  • OUTPUT statement
  • Evaluate the null hypothesis using the output of the GLM procedure
  • Interpret the statistical output of the GLM procedure (variance derived from MSE, F value, p-value R**2, Levene's test)
  • Interpret the graphical output of the GLM procedure
  • Use the TTEST Procedure to compare means
  • Perform ANOVA post hoc test to evaluate treatment effect
  • Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
  • Use PDIFF option of LSMEANS statement
  • Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
  • Interpret diffograms to evaluate pairwise comparisons
  • Interpret control plots to evaluate pairwise comparisons
  • CompareContrast use of pairwise T-Tests, Tukey and Dunnett comparison methods
  • Detect and analyze interactions between factors
  • Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
  • LSMEANS with SLICE=option (Also using PROC PLM)
  • ODS SELECT
  • Interpret the output of the GLM procedure to identify interaction between factors: p-value
  • F Value
  • R Squared
  • TYPE I SS
  • TYPE III SS
  • Linear Regression - 20% Fit a multiple linear regression model using the REG and GLM procedures
  • Use the REG procedure to fit a multiple linear regression model
  • Use the GLM procedure to fit a multiple linear regression model
  • Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
  • Interpret REG or GLM procedure output for a multiple linear regression model: convert models to algebraic expressions
  • Convert models to algebraic expressions
  • Identify missing degrees of freedom
  • Identify variance due to modelerror, and total variance
  • Calculate a missing F value
  • Identify variable with largest impact to model
  • For output from two models, identify which model is better
  • Identify how much of the variation in the dependent variable is explained by the model
  • Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)
  • Use the REG or GLMSELECT procedure to perform model selection
  • Use the SELECTION option of the model statement in the GLMSELECT procedure
  • Compare the different model selection methods (STEPWISE, FORWARD, BACKWARD)
  • Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
  • Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
  • Assign names to models in the REG procedure (multiple model statements)
  • Assess the validity of a given regression model through the use of diagnostic and residual analysis
  • Explain the assumptions for linear regression
  • From a set of residuals plots, asses which assumption about the error terms has been violated
  • Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
  • Explain options for handling influential observations
  • Identify collinearity problems by examining REG procedure output
  • Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)
  • Logistic Regression - 25% Perform logistic regression with the LOGISTIC procedure
  • Identify experiments that require analysis via logistic regression
  • Identify logistic regression assumptions
  • logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
  • Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)
  • Optimize model performance through input selection
  • Use the LOGISTIC procedure to fit a multiple logistic regression model
  • LOGISTIC procedure SELECTION=SCORE option
  • Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure
  • Interpret the output of the LOGISTIC procedure
  • Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
  • Testing Global Null Hypothesis table
  • Type 3 Analysis of Effects table
  • Analysis of Maximum Likelihood Estimates table
  • Association of Predicted Probabilities and Observed Responses
  • Score new data sets using the LOGISTIC and PLM procedures
  • Use the SCORE statement in the PLM procedure to score new cases
  • Use the CODE statement in PROC LOGISTIC to score new data
  • Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
  • Use the INMODELOUTMODEL options in PROC LOGISTIC
  • Explain how to score new data when you have developed a model from a biased sample
  • Prepare Inputs for Predictive Model Performance - 20% Identify the potential challenges when preparing input data for a model
  • Identify problems that missing values can cause in creating predictive models and scoring new data sets
  • Identify limitations of Complete Case Analysis
  • Explain problems caused by categorical variables with numerous levels
  • Discuss the problem of redundant variables
  • Discuss the problem of irrelevant and redundant variables
  • Discuss the non-linearities and the problems they create in predictive models
  • Discuss outliers and the problems they create in predictive models
  • Describe quasi-complete separation
  • Discuss the effect of interactions
  • Determine when it is necessary to oversample data
  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
  • Use ARRAYs to create missing indicators
  • Use ARRAYS, LOOP, IF, and explicit OUTPUT statements
  • Improve the predictive power of categorical inputs
  • Reduce the number of levels of a categorical variable
  • Explain thresholding
  • Explain Greenacre's method
  • Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
  • METHOD=WARD option
  • FREQ, VAR, ID statement
  • Use of ODS output to create an output data set
  • Convert categorical variables to continuous using smooth weight of evidence
  • Screen variables for irrelevance and non-linear association using the CORR procedure
  • Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
  • Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
  • Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations
  • Screen variables for non-linearity using empirical logit plots
  • Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
  • Interpret RANK procedure output
  • Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
  • Create empirical logit plots with the GPLOT procedure
  • Interpret empirical logit plots
  • Measure Model Performance - 25% Apply the principles of honest assessment to model performance measurement
  • Explain techniques to honestly assess classifier performance
  • Explain overfitting
  • Explain differences between validation and test data
  • Identify the impact of performing data preparation before data is split
  • Assess classifier performance using the confusion matrix
  • Explain the confusion matrix
  • Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
  • Explain the effect of oversampling on the confusion matrix
  • Adjust the confusion matrix for oversampling
  • Model selection and validation using training and validation data
  • Divide data into training and validation data sets using the SURVEYSELECT procedure
  • Discuss the subset selection methods available in PROC LOGISTIC
  • Discuss methods to determine interactions (forward selection, with bar and notation)
  • Create interaction plot with the results from PROC LOGISTIC
  • Select the model with fit statistics (BIC, AIC, KS, Brier score)
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Explain and interpret charts (ROC, Lift, Gains)
  • Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
  • Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
  • Explain the concept of depth as it relates to the gains chart
  • Establish effective decision cut-off values for scoring
  • Illustrate a decision rule that maximizes the expected profit
  • Explain the profit matrix and how to use it to estimate the profit per scored customer
  • Calculate decision cutoffs using Bayes rule, given a profit matrix
  • Determine optimum cutoff values from profit plots
  • Given a profit matrix, and model results, determine the model with the highest average profit
  • Note: All 22 main objectives will be tested on every exam. The 126 expanded objectives are provided for additional explanation and define the entire domain that could be tested. Experience is a critical component to becoming a SAS Certified Professional. These resources are designed to help you prepare. Training: Get training from SAS experts. To view training options in your country, choose your location above. Practice: More information: the SAS Global Certification Program at certificationsasm or 800-727-0025. Registration Options: Pearson VUE to register online. Candidates may not register directly at a Pearson VUE testing facility. A minimum of 24 hours is required for registration for returning candidates. First-time candidates require additional time.

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    All notes will be collected at the end of testing and no material may be removed from the testing event.

    Score Report You will receive an immediate passfail score upon completion of your exam attempt at your testing facility. The score report will display the percentage of items in each section that you answered correctly for your exam. Please note: These section scores are calculated on a per section basis and cannot be used in determining your total score. They are provided to you for descriptive purposes only. Welcome E-mail and Certificate If you pass your exam and meet all requirements for this credential, you will receive an e-mail from SAS with instructions providing access to your certificate and logo through the Certification Records Management System. This e-mail will be sent to the e-mail address you provided to Pearson VUE at exam registration. Some individual firewalls may send this e-mail to your junk folder. Please allow at least one week from your exam date to receive your e-mail.

    Within Certification Records Management system, your certificate can be accessed on the left navigation bar under "Printable Documents." To print your certificate, your pop-up blocker should be disabled before clicking the "Print Now" button. Click on "Print Now" and your certificate will open in a new window where you can download andor print.

    Certain credentials require more than one exam to earn the credential. We encourage you to visit credentials and exams for more information.

    Public Registry of Certified Professionals A Public Registry of SAS Certified Professionals is maintained within the SAS Certification Records Management system. If you do not wish for your name to appear in the Public Registry of SAS Certified Professionals, you can choose to be excluded by updating your personal information in the SAS Certification Records Management system. Once you earn your credential, you'll enjoy these perks:



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