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Test Number : A00-240
Test Name : SAS Statistical Business Analysis SAS9: Regression and Model
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A00-240 test Format | A00-240 Course Contents | A00-240 Course Outline | A00-240 test Syllabus | A00-240 test Objectives


This test is administered by SAS and Pearson VUE.
60 scored multiple-choice and short-answer questions.
(Must achieve score of 68 percent correct to pass)
In addition to the 60 scored items, there may be up to five unscored items.
Two hours to complete exam.
Use test ID A00-240; required when registering with Pearson VUE.

ANOVA - 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors

Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures

Prepare Inputs for Predictive Model Performance - 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring

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 I/II error, statistical power, p-value
=> Describe the effect of demo 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
o CLASS statement
o MODEL statement
o MEANS statement
o 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, Fvalue, 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
=> Compare/Contrast 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 model/error, 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 differentmodel 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 INMODEL/OUTMODEL 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
o METHOD=WARD option
o 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 SGPLOT 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



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SASInstitute Free PDF

Digital advertising Analytics Market sets the desk for continued growth | Hubspot, Adobe systems, Marketo, Microsoft | A00-240 test dumps and Question Bank

The MarketWatch information branch turned into now not thinking within the advent of this content.

Sep 29, 2020 (Market insight reviews) -- (New Jersey) existing study on Industrial boom of COVID-19 Outbreak-global Digital marketing Analytics Market 2019-2025. a detailed examine amassed to present latest insights about acute aspects of the COVID-19 Outbreak- Digital marketing Analytics market. The report includes diverse market predictions concerning market size, earnings, production, CAGR, Consumption, gross margin, fee, and different mammoth elements. whereas emphasizing the key riding and restraining forces for this market, the report additionally presents an entire examine of the long run tendencies and trends of the market. It also examines the function of the main market players thinking within the trade including their corporate overview, fiscal summary and SWOT evaluation.

The fundamental players coated during this document: Salesforce.Com Inc., Adobe programs, Marketo, Hewlett-Packard (HP), Microsoft, Hubspot, IBM employer, SAS Institute Inc., SAP AG, Oracle corporation, ScienceSoft & CodeBright

COVID-19 Outbreak- Digital marketing Analytics Market analyze ensures you to remain / reside suggested bigger than your competition. With Structured tables and figures inspecting the COVID-19 Outbreak- Digital marketing Analytics, the analysis document gives you a leading product, submarkets, income size and forecast to 2025. Comparatively is additionally classifies rising in addition to leaders in the trade. demo PDF (including Full TOC, desk & Figures)

This analyze also covers enterprise profiling, requirements and product photograph, revenue, market share and make contact with counsel of various regional, foreign and local companies of COVID-19 Outbreak-world Digital advertising and marketing Analytics Market. The market opposition is frequently setting up better with the upward push in scientific innovation and M&A actions within the industry. additionally, many local and regional companies are providing selected application products for numerous end-users. the new service provider candidates in the market are finding it tough to compete with the overseas vendors based on reliability, high-quality and modernism in know-how.

The titled segments and sub-portion of the market are illuminated beneath:

In-depth analysis of COVID-19 Outbreak-world Digital advertising and marketing Analytics market segments by types: , On-Premises & Cloud based

certain evaluation of COVID-19 Outbreak-global Digital marketing Analytics market segments by way of applications: IT and Telecom, BFSI, govt, Retail, Manufacturing, automobile, Retail & Others

most important Key players of the Market: Salesforce.Com Inc., Adobe techniques, Marketo, Hewlett-Packard (HP), Microsoft, Hubspot, IBM company, SAS Institute Inc., SAP AG, Oracle agency, ScienceSoft & CodeBright

read targeted Index of full research examine at @ https://www.htfmarketreport.com/studies/2805969-covid-19-outbreak-international-digital-market-3

Regional evaluation for COVID-19 Outbreak-international Digital advertising and marketing Analytics Market:North the us (united states, Canada and Mexico)Europe (Germany, France, UK, Russia and Italy)Asia-Pacific (China, Japan, Korea, India and Southeast Asia)South america (Brazil, Argentina, Colombia and many others.)core East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

moreover, the years considered for the study are as follows:historic year - 2014-2019Base year - 2019Forecast period** - 2020 to 2025 [** unless otherwise stated]

**moreover, it's going to additionally consist of the opportunities attainable in micro markets for stakeholders to invest, certain evaluation of competitive landscape and product functions of key gamers.

counsel of the COVID-19 Outbreak-world Digital advertising and marketing Analytics market file:

– certain thoughtful of COVID-19 Outbreak- Digital marketing Analytics market-specific drivers, developments, constraints, Restraints, alternatives and fundamental micro markets.– comprehensive valuation of all prospects and risk in the COVID-19 Outbreak-global Digital advertising and marketing Analytics market.– intensive study of trade recommendations for increase of the COVID-19 Outbreak- Digital advertising Analytics market-leading players.– COVID-19 Outbreak- Digital advertising Analytics market existing innovations and important strategies.– Favorable dip interior lively excessive-tech and market existing traits wonderful the Market.– Conclusive examine in regards to the growth conspiracy of COVID-19 Outbreak- Digital advertising and marketing Analytics market for forthcoming years.

What to are expecting from this document On COVID-19 Outbreak- Digital advertising Analytics Market:

1. A comprehensive summary of a few enviornment distributions and the abstract styles of ordinary items in the COVID-19 Outbreak- Digital advertising Analytics Market.2. that you would be able to repair up the growing to be databases to your industry when you have information on the can charge of the construction, cost of the items, and value of the production for the next future years.three. Thorough evaluation the destroy-in for new groups who want to enter the COVID-19 Outbreak- Digital advertising Analytics Market.four. exactly how do essentially the most essential businesses and mid-stage businesses make income within the Market?5. complete analysis on the ordinary construction within the COVID-19 Outbreak- Digital advertising and marketing Analytics Market that helps you choose the product launch and overhaul growths.

precise TOC of COVID-19 Outbreak- Digital advertising and marketing Analytics Market research file-

– COVID-19 Outbreak- Digital advertising Analytics Introduction and Market Overview– COVID-19 Outbreak- Digital marketing Analytics Market, by utility [IT and Telecom, BFSI, Government, Retail, Manufacturing, Automotive, Retail & Others]

– COVID-19 Outbreak- Digital marketing Analytics trade Chain evaluation– COVID-19 Outbreak- Digital advertising Analytics Market, by means of class [, On-Premises & Cloud Based]

– industry Manufacture, Consumption, Export, Import by way of regions (2014-2019)– industry cost ($) with the aid of place (2014-2019)

– COVID-19 Outbreak- Digital advertising and marketing Analytics Market repute and SWOT analysis by way of areas

– principal area of COVID-19 Outbreak- Digital advertising Analytics Marketi) international COVID-19 Outbreak- Digital marketing Analytics Salesii) international COVID-19 Outbreak- Digital advertising Analytics salary & market share– fundamental agencies listing– Conclusion

Thanks for studying this text; which you could also get particular person chapter shrewd section or vicinity clever file edition like North the us, Europe or Asia.

This Press free up has been written with the intention of featuring correct market assistance for you to allow their readers to make recommended strategic investment selections. if you be aware any problem with this content, please consider free to reach us on editorial@themarketpublicist.com

COMTEX_372054533/2599/2020-09-29T11:25:01

Is there an issue with this press unencumber? Contact the source provider Comtex at editorial@comtex.com. that you would be able to also contact MarketWatch consumer carrier by means of their customer center.

The MarketWatch news department turned into not concerned in the advent of this content material.


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