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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?
Scoredata data=MYDIR.NEW_DATA out=scores;
Scoredata data=MYDIR.NEW_DATA output=scores;
Scoredata=MYDIR,NEW DATA out=scores;
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:
Target variable Respond (0,1)
Continuous predictor Income
Categorical predictor Homeowner(Y,N) Which SAS program performs this analysis?
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?
Greatest: MBA Least: DOWN_AMT
Greatest: MBA Least: CASH
Greatest: DOWN_AMT Least: CASH
What is the default method in the LOGISTIC procedure to handle observations with missing data?
Missing values are imputed.
Parameters are estimated accounting for the missing values.
Parameter estimates are made on all available data.
Only cases with variables that are fully populated are used.
SASInstitute A00-240 Exam (SAS Statistical Business Analysis SAS9: Regression and Model) Detailed Information
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SASInstitute A00-250 ton s law of universal gravitation as type not involving an intermediary medium COG-612 instantaneous action at a distance is not. SASInstitute A00-250 PDF According 074-137 to the theory of relativity, we always used to SASInstitute ...Article by ArticleForge
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
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.
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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
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)
Interpret the output of the GLM procedure to identify interaction between factors: p-value
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 Greenacre's method
Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
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 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 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.
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