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SASInstitute A00-240 : SAS Statistical Business Analysis SAS9: Regression and Model Exam

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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 trial 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 Practice Questions

skills and Practices concerning protected family cleansing and Disinfection for COVID-19 Prevention | A00-240 test Braindumps and Free PDF

summary and introductionIntroduction

A fresh report described a pointy enhance in calls to poison facilities concerning exposures to cleaners and disinfectants considering that the onset of the coronavirus disorder 2019 (COVID-19) pandemic.[1] besides the fact that children, records describing cleansing and disinfection practices inside family settings in the united states are constrained, in particular concerning these practices intended to steer clear of transmission of SARS-CoV-2, the virus that causes COVID-19. To provide contextual and behavioral perception into the reported raise in poison center calls and to inform timely and imperative prevention strategies, an decide-in internet panel survey of 502 U.S. adults become performed in may additionally 2020 to characterize advantage and practices regarding family unit cleansing and disinfection all through the COVID-19 pandemic. potential gaps have been recognized in several areas, together with protected training of cleaning and disinfectant options, use of advised own shielding gadget when the usage of cleaners and disinfectants, and protected storage of hand sanitizers, cleaners, and disinfectants. Thirty-9 percent of respondents reported accomplishing nonrecommended high-chance practices with the intent of preventing SARS-CoV-2 transmission, akin to washing food products with bleach, applying household cleansing or disinfectant products to bare skin, and intentionally inhaling or ingesting these items. Respondents who engaged in excessive-risk practices greater commonly stated an hostile health effect that they believed changed into as a result of the usage of cleaners or disinfectants than did those that didn't document carrying out these practices. Public messaging should continue to emphasise proof-primarily based, secure practices equivalent to hand hygiene and suggested cleaning and disinfection of excessive-contact surfaces to avoid transmission of SARS-CoV-2 in household settings.[2] Messaging should still additionally emphasize avoidance of high-risk practices equivalent to dangerous coaching of cleaning and disinfectant solutions, use of bleach on meals products, application of family unit cleansing and disinfectant items to epidermis, and inhalation or ingestion of cleaners and disinfectants.

Survey questions have been administered with the aid of Porter Novelli Public capabilities and ENGINE Insights on may additionally four, 2020, through PN View: 360,* a speedy turnaround survey that may also be used to supply insights into talents and practices of focused audiences. This decide-in internet panel survey changed into administered to 502 U.S. adults aged ≥18 years using the Lucid platform;[3] panel contributors who had now not taken a survey in the old 20 waves of survey administration have been eligible to take part. Quota sampling and statistical weighting were employed to make the panel representative of the U.S. inhabitants via gender, age, location, race/ethnicity, and training. Respondents have been recommended that their answers had been getting used for market analysis and could refuse to reply any query at any time. No for my part deciding upon suggestions turned into covered within the facts file offered to CDC.†

Survey questions asked about standard advantage, attitudes, and practices involving use of family unit cleaners and disinfectants§ and about specific tips concerning cleaning and disinfection innovations for prevention of SARS-CoV-2 transmission. Weighted response frequencies had been calculated the usage of SAS statistical application (version 9.4; SAS Institute). as a result of respondents had been recruited from an decide-in panel rather than via probability sampling, no inferential statistical checks have been performed.¶ changes had been mentioned when a difference of ≥5 percent points was found between any estimates being compared.

The median age of respondents turned into 46 years (latitude = 18–86 years), and fifty two% of respondents have been female. average, sixty three% of respondents have been non-Hispanic white, 16% had been Hispanic (any race), 12% have been non-Hispanic black, and 8% have been multiracial or of other race/ethnicity. Respondents represented all U.S. Census areas,** with 38% from the South, 24% from the West, 21% from the Midwest, and 18% from the Northeast.

contributors had limited knowledge of protected guidance of cleaning and disinfectant solutions (figure 1). standard, 23% replied that simplest room temperature water should still be used for coaching of dilute bleach solutions, 35% that bleach may still not be combined with vinegar, and 58% that bleach should still now not be mixed with ammonia. In comparison, a better percent of respondents had competencies about use of suggested very own protective device: 64% replied that eye insurance plan was suggested for use of some cleaners and disinfectants, and 71% replied that gloves had been recommended to be used. in a similar way, sixty eight% answered that handwashing changed into suggested after the use of cleaners and disinfectants and 73% that enough ventilation become advised when using these items. involving protected storage of cleaners, disinfectants, and hand sanitizers, seventy nine% of respondents mentioned that cleaners and disinfectants should still be stored out of the reach of babies, and fifty four% that hand sanitizers may still be saved out of the attain of little ones.

determine 1.

competencies about secure use of cleaners and disinfectants*,† in line with responses to an opt-in web panel survey§ (N = 502 respondents) — united states, might also 2020*in response to the question "Which of here have you heard is true about using family unit cleaning items (such as bleach or Lysol)?"; response options mirrored CDC innovations for secure cleansing and disinfection. https://www.cdc.gov/coronavirus/2019-ncov/stay away from-getting-in poor health/disinfecting-your-domestic.html.†In survey questions, the term "cleaning" said the usage of a cleaner or disinfectant on surfaces or objects. Questions involving storage of hand sanitizers had been protected with questions concerning storage of cleaners and disinfectants.§Survey administered through Porter Novelli Public features through PN View: 360; respondents could select diverse responses to the question (all response options proven). selection of the response "none of these" turned into unique (i.e., respondents could not choose this response option moreover different responses).

Respondents mentioned accomplishing quite a number practices during the old month with the intent of fighting SARS-CoV-2 transmission (figure 2). Sixty p.c of respondents suggested greater everyday domestic cleaning or disinfection in comparison with that in previous months. Thirty-9 p.c said intentionally undertaking as a minimum one high-chance observe now not informed with the aid of CDC for prevention of SARS-CoV-2 transmission,[2] including utility of bleach to food objects (e.g., vegetables and fruit) (19%); use of family unit cleaning and disinfectant products on palms or epidermis (18%); misting the physique with a cleansing or disinfectant spray (10%); inhalation of vapors from household cleaners or disinfectants (6%); and consuming or gargling diluted bleach options, soapy water, and other cleaning and disinfectant solutions (four% every).

figure 2.

cleansing and disinfection practices in the previous month with the intent of fighting SARS-CoV-2 an infection,*,† based on responses to an opt-in cyber web panel survey§ (N = 502 respondents) — u.s., may also 2020*in accordance with the question "in the past month, which of here cleansing behaviors have you ever or a household member engaged in to keep away from coronavirus?"†In survey questions, the time period "cleansing" said the use of a cleaner or disinfectant on surfaces or objects.§Survey administered with the aid of Porter Novelli Public capabilities through PN View: 360; respondents might opt for multiple responses to the question (nine of 11 viable response alternate options proven). alternative of the response "none of these" became unique (i.e., respondents couldn't choose this response choice apart from different responses).

One quarter (25%) of respondents pronounced at least one antagonistic health effect throughout the outdated month that they believed had resulted from using cleaners or disinfectants, including nostril or sinus irritation (eleven%); epidermis inflammation (8%); eye infection (8%); dizziness, lightheadedness, or headache (8%); upset abdominal or nausea (6%); or breathing problems (6%). Respondents who said conducting at least one excessive-possibility practice greater frequently said an adversarial fitness impact than did those who didn't report accomplishing such practices (39% versus 16%).

approximately half (51%) of respondents strongly agreed and 31% slightly agreed that they knew how to clean and disinfect their home safely. similarly, 42% strongly agreed and 35% a bit agreed that they knew the way to clear and disinfect their domestic to avoid SARS-CoV-2 transmission. When requested who their most depended on sources of SARS-CoV-2-linked cleansing and disinfection guidance were, the top three responses had been CDC (65%), state or local health departments (49%), and medical doctors, nurses, or scientific providers (48%).


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