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Exam Number : A00-240
Exam Name : SAS Statistical Business Analysis SAS9: Regression and Model
Vendor Name : SASInstitute
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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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Cardiovascular and Cerebrovascular routine in sufferers treated for Human Immunodeficiency Virus infection | A00-240 real questions and PDF Dumps

study background

Our examine changed into conducted under the auspices of the Oversight Committee for the evaluation of the Metabolic issues of totally energetic Antiretroviral therapy, convened by the european company for the assessment of Medicinal products, however ultimate choices involving this analysis and report rested with the authors. The study changed into accepted via the Committee on Human syllabus on the college of California, San Diego, and the VA San Diego Healthcare equipment, which didn't require informed consent.

Sources of statistics

Our supply of records became the best Enhancement Database for HIV (QED-HIV), which they developed using anonymous suggestions obtained from the master Veteran checklist national Database and VA country wide patient Care (utilization) statistics bases, and the Immunology Case Registry of the VA AIDS carrier.19 This registry is shaped in the course of the “sweeping” of records on patients with HIV from native registries at each and every VA facility into a critical information base, which carries no patient identifiers apart from an encrypted quantity used to hyperlink and “unduplicate” facts that seem in additional than one native registry. Coding schemes finished by way of off-web site folks no longer affiliated with their study community have been used to augment the information on vital popularity by means of the addition of suggestions from VA loss of life-advantage claims, Social safety facts, and (through 1999) the country wide demise Index. They categorized the cause of loss of life with the aid of scanning the particular person explanations purchasable from the country wide dying Index. They calculated using medicinal drugs with the aid of estimating the number of days lined by way of each and every prescription.

normal, they consider that the information they used describe care received by means of these veterans well, because the VA information systems are enormously uniform, and because services are finished and usually free, together with direct provision of all antiretroviral drugs permitted by way of the food and Drug Administration. evidence of the completeness of the statistics contains the records that fundamental diagnoses had been available for 96 to ninety nine percent of admissions and that under 5 % of deaths have been discovered most effective in non-VA sources.

consequences

We file on five effects: admission for heart problems, admission for cardiovascular or cerebrovascular disease, admission for or dying from cardiovascular or cerebrovascular sickness, demise from any trigger, and admission for cardiovascular or cerebrovascular sickness or dying from any cause. with a view to convert codes from the foreign Classification of illnesses, Ninth Revision (ICD-9) or Tenth Revision (ICD-10)20 to the based variables listed above, they reviewed the literature and convened a panel including senior coder-abstracters and researchers in infectious ailments, neurology, and cardiology. They covered the following ICD-9 codes for heart problems when they seemed as some of the first three listed discharge diagnoses and for cerebrovascular ailment when they seemed as one of the vital first five listed discharge diagnoses: 410, “acute myocardial infarction,” apart from with a fifth digit of 2; 411, “different acute and subacute forms of ischemic coronary heart ailment”; 413, “angina pectoris,” apart from 413.1; 414, “different types of chronic ischemic coronary heart disease,” except 414.1; 36.0 (procedure code), “removal of coronary-artery obstruction and insertion of stent(s)”; 430, “subarachnoid hemorrhage”; 433, “occlusion and stenosis of precerebral arteries”; 434, “occlusion of cerebral arteries”; 436, “acute however ill-described cerebrovascular ailment”; 437.0, “cerebral atherosclerosis”; 437.1, “other generalized ischemic cerebrovascular ailment”; 431, “intracerebral hemorrhage”; and 435, “transient cerebral ischemia.” Diagnoses of cerebrovascular disease had been included simplest in the absence of codes for HIV-related infections or drug dependence. Measures of mortality attributable to various diseases have been in accordance with the same categories as the classification of admissions; they used conversion tables translating ICD-9 codes into ICD-10 codes for the coding of deaths in 1999.

Statistical analysis

We decided the date of first receipt of look after HIV at a VA facility from the date of registration in the Immunology Case Registry or the date of the first HIV-related laboratory look at various or health center admission. They used distinct imputation, as carried out in SAS Proc MI (SAS Institute), to impute the race for 7 % of the study population, the risk aspect for HIV for 13 percent, and the severity of illness or age for under 0.5 percent. They analyzed the five ensuing information units the use of Proc MIANALYZE (SAS Institute), which contains the uncertainty led to by the imputation. They characterised the analyze inhabitants and all covariates within the customary and imputed data sets using normal descriptive facts and described distinct distinct periods of receipt of VA look after each patient. They mixed and chosen variables to include in their fashions on the groundwork of previous abilities and correlations amongst covariates. They tallied results and use of medicinal drugs in line with year and calculated costs as events or years of drug distributed per one hundred patient-years of statement.

before performing the modeling, they rearranged the statistics sets to accommodate staggered periods of receipt of care, then applied diversifications of the programs of Therneau and Grambsch21 and analyzed tied survival times with Efron's system.22 They generated Kaplan–Meier curves for all models. They investigated the healthy of the information to the proportional-hazards assumption through evaluating the parallelism of plots of the logarithm of neighborhood-specific cumulative-hazard functions. They performed time-to-event modeling the usage of the interval of January 1, 1993 (or the date of first VA-primarily based care for HIV if it was later), via June 30, 2001, for all consequences other than loss of life because of cardiovascular or cerebrovascular disorder, which became always truncated at December 31, 1999. records had been censored at the conclusion of the examine, at the time of loss of life, or six months after care turned into final acquired at a VA facility. They chosen the six-month duration as a result of their analysis indicated that veterans receiving look after HIV had been not likely to return to the VA for capabilities after a spot of that length.

Regression models included a common neighborhood of covariates that remained consistent over time (12 months of first take care of HIV at a VA facility, race or ethnic group, intercourse, age, possibility ingredient for HIV, severity of disorder, presence or absence of a historical past of AIDS-defining diagnosis, presence or absense of a diagnosis of drug abuse, and presence or absence of outdated treatment for severe vascular disorder, diabetes, hypertension, hyperlipidemia, or smoking), as well as covariates involving use of antiretroviral medication that varied over time. These included variables for the cumulative months of exposure to each and every of the three classes of antiretroviral drugs. They additionally included variables equal to the squares and the cubes of these phrases in order to accommodate feasible alterations in dangers over time. The results acquired with cubic terms introduced little to the analysis and have been disregarded. They blanketed variables denoting any publicity to every classification of medication and set the values at 1 after the primary month of remedy in order to relevant in part for alternative with the aid of absorbing the effects of selecting to beginning therapy.

To evaluate the results of secular traits, they carried out separate stratified analyses for the periods January 1996 via December 1998 and January 1999 via June 2001. They tested for the statistical magnitude of changes over time by way of calculating z ratings for the change between the logarithms of the hazard ratios linked to publicity to therapy during the first and 2nd intervals. They document the effects of modeling as estimates of the hazard ratios for 24 months of exposure as compared with 0 months of exposure, calculated with the aid of right here components:

log(hazard ratio) = (24 × βlinear publicity term) + (576 × βquadratic publicity time period).




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https://www.4shared.com/video/IDBBT2Iqiq/SAS-Statistical-Business-Analy.html
https://www.4shared.com/office/o7UWojt3ea/SAS-Statistical-Business-Analy.html
https://files.fm/f/hze6gkv2
https://www.coursehero.com/file/69265576/SAS-Statistical-Business-Analysis-SAS9-Regression-and-Model-A00-240pdf/
http://ge.tt/86Pkfd83
https://youtu.be/P-HXRAOMHZs
http://feeds.feedburner.com/FreePass4sureA00-240QuestionBank
http://killexams.decksrusct.com/blog/uncategorized/a00-240-sas-statistical-business-analysis-sas9-regression-and-model-real-exam-questions-and-answers-by-killexams-com/
https://justpaste.it/A00-240
https://sites.google.com/view/killexams-a00-240-question-ban
https://ello.co/killexamz/post/bnsuukev76emdjzzxkjtug
https://www.clipsharelive.com/video/6350/a00-240-sas-statistical-business-analysis-sas9-regression-and-model-real-exam-questions-by-killexams-com
https://spaces.hightail.com/space/v47qz1ixkg/files/fi-7c9a4417-89eb-49cf-a979-0e76906afe68/fv-59075ac0-ab9a-408a-bc64-440e53537af4/SAS-Statistical-Business-Analysis-SAS9-Regression-and-Model-(A00-240).pdf#pageThumbnail-1



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