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

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Exam Number : A00-240
Exam 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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abilities and Practices concerning safe household cleansing and Disinfection for COVID-19 Prevention | A00-240 real questions and Study Guide

summary and creationIntroduction

A fresh report described a pointy enhance in calls to poison centers regarding exposures to cleaners and disinfectants since the onset of the coronavirus ailment 2019 (COVID-19) pandemic.[1] youngsters, statistics describing cleansing and disinfection practices inside family settings within the u.s. are restricted, primarily regarding those practices meant to keep away from transmission of SARS-CoV-2, the virus that causes COVID-19. To provide contextual and behavioral insight into the pronounced boost in poison middle calls and to notify timely and relevant prevention suggestions, an decide-in internet panel survey of 502 U.S. adults was conducted in may also 2020 to characterize expertise and practices concerning family cleansing and disinfection during the COVID-19 pandemic. skills gaps have been recognized in several areas, together with safe practise of cleaning and disinfectant solutions, use of informed personal protective gadget when the usage of cleaners and disinfectants, and safe storage of hand sanitizers, cleaners, and disinfectants. Thirty-9 % of respondents stated carrying out nonrecommended excessive-risk practices with the intent of fighting SARS-CoV-2 transmission, corresponding to washing food items with bleach, applying family unit cleansing or disinfectant items to naked dermis, and deliberately inhaling or ingesting these items. Respondents who engaged in high-possibility practices extra generally said an adverse fitness effect that they believed turned into as a result using cleaners or disinfectants than did people that did not file engaging in these practices. Public messaging should continue to emphasise evidence-based mostly, safe practices akin to hand hygiene and informed cleansing and disinfection of high-contact surfaces to evade transmission of SARS-CoV-2 in family unit settings.[2] Messaging should also emphasize avoidance of excessive-chance practices equivalent to dangerous instruction of cleaning and disinfectant solutions, use of bleach on food products, utility of household cleansing and disinfectant items to dermis, and inhalation or ingestion of cleaners and disinfectants.

Survey questions have been administered via Porter Novelli Public capabilities and ENGINE Insights on may also four, 2020, via PN View: 360,* a speedy turnaround survey that may also be used to provide insights into talents and practices of targeted audiences. This choose-in cyber web panel survey turned into administered to 502 U.S. adults aged ≥18 years the use of the Lucid platform;[3] panel participants who had now not taken a survey within the outdated 20 waves of survey administration had been eligible to participate. Quota sampling and statistical weighting have been employed to make the panel representative of the U.S. population via gender, age, location, race/ethnicity, and training. Respondents have been suggested that their answers had been getting used for market research and could refuse to answer any question at any time. No personally picking advice was included in the statistics file provided to CDC.†

Survey questions asked about regularly occurring skills, attitudes, and practices involving use of family unit cleaners and disinfectants§ and about particular assistance related to cleaning and disinfection suggestions for prevention of SARS-CoV-2 transmission. Weighted response frequencies were calculated using SAS statistical utility (version 9.4; SAS Institute). because respondents had been recruited from an choose-in panel instead of by likelihood sampling, no inferential statistical checks have been performed.¶ transformations had been mentioned when a change of ≥5 percentage aspects changed into found between any estimates being in comparison.

The median age of respondents turned into 46 years (latitude = 18–86 years), and 52% of respondents were feminine. standard, 63% of respondents have been non-Hispanic white, sixteen% had been Hispanic (any race), 12% had been non-Hispanic black, and 8% had been multiracial or of other race/ethnicity. Respondents represented all U.S. Census regions,** with 38% from the South, 24% from the West, 21% from the Midwest, and 18% from the Northeast.

members had constrained talents of safe instruction of cleansing and disinfectant options (figure 1). universal, 23% replied that only room temperature water should still be used for instruction of dilute bleach solutions, 35% that bleach should now not be mixed with vinegar, and 58% that bleach should still no longer be combined with ammonia. In comparison, a much better percentage of respondents had advantage about use of counseled own shielding device: 64% responded that eye insurance policy changed into suggested to be used of some cleaners and disinfectants, and seventy one% responded that gloves were advised for use. in a similar way, sixty eight% spoke back that handwashing changed into informed after the use of cleaners and disinfectants and 73% that enough air flow turned into counseled when the usage of these items. related to protected storage of cleaners, disinfectants, and hand sanitizers, seventy nine% of respondents referred to that cleaners and disinfectants should be kept out of the attain of infants, and 54% that hand sanitizers may still be stored out of the reach of little ones.

determine 1.

capabilities about secure use of cleaners and disinfectants*,† based on responses to an opt-in cyber web panel survey§ (N = 502 respondents) — united states, may also 2020*in accordance with the query "Which of right here have you heard is right about the use of family cleansing items (such as bleach or Lysol)?"; response alternate options mirrored CDC ideas for protected cleaning and disinfection. https://www.cdc.gov/coronavirus/2019-ncov/stay away from-getting-ailing/disinfecting-your-domestic.html.†In survey questions, the term "cleaning" talked about the use of a cleaner or disinfectant on surfaces or objects. Questions concerning storage of hand sanitizers had been blanketed with questions concerning storage of cleaners and disinfectants.§Survey administered by using Porter Novelli Public capabilities through PN View: 360; respondents might opt for varied responses to the question (all response alternate options shown). preference of the response "none of these" become exclusive (i.e., respondents could not select this response alternative moreover other responses).

Respondents mentioned undertaking quite a number practices all over the outdated month with the intent of combating SARS-CoV-2 transmission (determine 2). Sixty % of respondents reported extra universal home cleaning or disinfection in comparison with that in previous months. Thirty-nine % mentioned intentionally conducting at the least one excessive-possibility follow now not suggested by way of CDC for prevention of SARS-CoV-2 transmission,[2] together with application of bleach to food objects (e.g., vegatables and fruits) (19%); use of family unit cleaning and disinfectant products on fingers or epidermis (18%); misting the physique with a cleansing or disinfectant spray (10%); inhalation of vapors from family unit cleaners or disinfectants (6%); and drinking or gargling diluted bleach options, soapy water, and other cleaning and disinfectant options (4% each).

figure 2.

cleaning and disinfection practices within the previous month with the intent of fighting SARS-CoV-2 infection,*,† in keeping with responses to an decide-in web panel survey§ (N = 502 respondents) — united states, might also 2020*according to the query "in the past month, which of here cleansing behaviors have you ever or a family unit member engaged in to stay away from coronavirus?"†In survey questions, the time period "cleansing" stated the use of a cleaner or disinfectant on surfaces or objects.§Survey administered with the aid of Porter Novelli Public functions via PN View: 360; respondents may select assorted responses to the question (9 of 11 viable response alternate options proven). option of the response "none of those" became unique (i.e., respondents could not choose this response option apart from different responses).

One quarter (25%) of respondents suggested as a minimum one hostile fitness impact throughout the previous month that they believed had resulted from the usage of cleaners or disinfectants, including nose or sinus inflammation (11%); dermis inflammation (eight%); eye irritation (8%); dizziness, lightheadedness, or headache (eight%); upset abdominal or nausea (6%); or breathing complications (6%). Respondents who pronounced conducting at the least one high-chance apply more commonly mentioned an opposed fitness effect than did people that did not file conducting such practices (39% versus sixteen%).

about half (51%) of respondents strongly agreed and 31% slightly agreed that they knew how to clear and disinfect their home safely. similarly, 42% strongly agreed and 35% somewhat agreed that they knew how to clean and disinfect their home to avoid SARS-CoV-2 transmission. When asked who their most relied on sources of SARS-CoV-2-related cleaning and disinfection information had been, the right three responses had been CDC (sixty five%), state or native health departments (49%), and docs, nurses, or scientific providers (forty eight%).




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