Crush the Statistical Business Analysis Exam in 2024

Statistical Business Analysis

Passing the A00-240 SAS Statistical Business Analysis exam requires preparation with up-to-date practical test material from the SAS Certified Statistical Business Analyst. A00-240 SAS Statistical Business Analysis exam questions assure you pass the exam. If you want to pass the A00-240 SAS Statistical Business Analysis exam and acquire high-paying jobs in the IT industry, you need to study from actual A00-240 SAS Statistical Business Analysis exam dumps.

These updated test dumps are suitable for finishing up preparing for the A00-240 SAS Statistical Business Analysis exam in a few weeks. Many students fail the A00-240 SAS exam owing to a lack of current nine exam practice test material.

Understanding how A00-240 skills translate into real-world scenarios is vital. Through case studies and industry-specific examples, readers will gain insights into the practical applications of statistical business analysis in diverse fields.

Continue reading and exploring to learn about the A00-240 SAS Statistical Business Analysis exam certifications.

What is the A00-240 SAS Statistical Business Analysis Exam?

Statistical Business Analysis

The A00-240 SAS Statistical Business Analysis exam is an accreditation examination developed for SAS professionals who wish to demonstrate their knowledge of utilizing the SAS program for statistical business analysis. The A00-240 SAS Statistical Business Analysis exam covers statistical regression modeling, hypothesis testing, and analyzing data so that you can make educated business decisions.

The A00-240, also called the SAS Statistical Business Analysis Using SAS 9: Regression & Modeling, allows the SAS Institute to examine various topics, as do all tests. That implies that understanding the bulk of the A00-240 SAS Statistical Business Analysis exam curriculum is needed because they test arbitrarily on the numerous topics that are accessible. Be aware.

This A00-240 SAS exam is a vital certification for SAS professionals who want to demonstrate their proficiency in utilizing SAS for statistical business analysis. The exam includes essential areas such as regression analysis, statistical modeling, and hypothesis testing, and those who pass will get the A00-240 SAS Statistical Business Analysis exam certifications.

A00-240 SAS Statistical Business Analysis Exam Details

Statistical Business Analysis

Here are the exam details for the SAS Statistical Business Analysis A00-240 exam you must know:

  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9
  • Exam Code: A00-240
  • Number of Questions: 60 MCQs and short answer questions
  • Exam Duration: 120 minutes
  • Passing Score: 68%
  • Exam Cost: $180 USD
  • Exam Vendor: Pearson VUE

A00-240 SAS Statistical Business Analysis Exam Course Outline

Here is the course outline for the SAS Statistical Business Analysis exam you must know before taking the actual exam:

  1. ANOVA – 10%

  • Check the assumptions of ANOVA.
  • Explain the theorem of central limit and when it should be implemented.
  • Analyze continuous variable distribution using histograms, box-whiskers, and Q-Q plots.
  • Describe how skewness affects the normal distribution.
  • Specify H0, H1, Type I/II errors, statistical power, and p-value.
  • Explain the impact of the number of samples on p-value and power.
  • Interpret hypothesis test findings.
  • Understanding histograms and conventional probability charts
  • Analyze data using histograms, box-whisker plots, and Q-Q plots.
  • Identify potential flaws in the data
  • Ensure independent observations for each experiment.
  • Check that the mistakes in the experiment are regularly distributed.
  • Apply the UNIVARIATE technique to analyze residuals.
  • For a particular experiment, ensure that all groups have equal reaction variance.
  • Use the Statistical Business Analysis HOVTEST parameter of the MEANS declaration in PROC GLM to assess response variance.
  • Using the GLM and TTEST techniques, compare population means

  • Employ the GLM Procedure for ANOVA.
  • Use GLM results to evaluate the null hypothesis.
  • Analyze the statistical results of the GLM technique
  • Interpret the visual output of the GLM method.
  • Apply the TTEST procedure to compare means.
  • To determine the impact of the treatment, use the ANOVA post hoc test

  • Use the LSMEANS clause in GLM or PLM procedures for pairwise comparisons.
  • Use the PDIFF option in the LSMEANS statement.
  • Use the ADJUST option in the LSMEANS statements (TUKEY and DUNNETT).
  • Interpret diagrams for pairwise comparisons.
  • Interpret control plots for assessing pairwise comparisons.
  • Compare and contrast paired T-tests, Tukey, and Dunnett comparison methods.
  • Identify and evaluate interactions between elements.

  • Use the GLM technique to generate reports that assess the importance of factor interactions. MODEL Statement LSMEANS with SLICE= option (also utilizing PROC PLM).
  • Interpret GLM output to find factor interactions:
  • p-value
  • F Value
  • R squared
  • Type I SS
  • Type III SS.
  1. Linear regression – 20%

  • Fit a multivariate linear regression prototypical with the REG and GLM algorithms.

  • Apply the Statistical Business Analysis REG technique to fit models with multiple linear regressions.
  • Apply the GLM approach to fit multiple linear regression models.
  • Evaluate the results of the REG, PLM, and GLM methods for multiple linear regression.
  • Interpret the REG or GLM method output for a series of linear regression models.
  • Identify the model’s ability to explain the dependent variable’s variance.
  • Conclusions based on REG, GLM, or PLM output (regarding H0, model quality, and visuals)
  • To accomplish model selection, employ the REG or GLMSELECT procedures

  • Use the SELECTION option for the model phrase in the GLMSELECT procedure.
  • Evaluate several model selection approaches (Stepwise, Forward, Backward).
  • Allow ODS graphics to display charts from the REG or GLMSELECT procedures.
  • Evaluate models based on graphical results.
  • Give model names in the REG process.
  • Diagnostic and residual evaluation are used to assess the validity of a specific regression model.

  • Explain the fundamental tenets of linear regression in .
  • Determine whether assumptions about the error terms have been violated based on a collection of residual graphs.
  • Use REG procedure and MODEL statement options to discover influential observations.
  • Provide choices for processing influential observations.
  • Examine REG procedure output for potential collinearity concerns.
  • Employ MODEL statement choices to identify collinearity issues.
  1. Logistic regression – 25%

  • Use the LOGISTIC method to perform logistic regression.

  • Identify and analyze studies using logistic regression.
  • Determine logistic regression assumptions.
  • Understand logistic regression concepts.
  • Apply the LOGISTIC method to fit a model of binary logistic regression.
  • Optimize model performance by selecting appropriate inputs.

  • Apply the LOGISTIC method to fit multiple logistic regression models.
  • Logistic procedure: SELECT=SCORE option.
  • Select types (STEPWISE, FORWARD, BACKWARD) using the logistic method.
  • Interpret the results of the LOGISTIC method.

  • Interpret the results of the LOGISTIC method for binary logistic regression.
  • Model Convergence section.
  • Testing World a Null Hypothesis Table.
  • Type 3 Analysis of Impacts Table
  • Analysis of the Maximum Probability Estimates Table
  • Relationship between predicted probabilities and actual responses.
  • Score new data sets with the LOGISTIC and PLM methods.

  • Employ the SCORE clause in the PLM process to evaluate new instances.
  • Employ the CODE statement in PROC LOGISTIC to evaluate updated data.
  • Explain when to utilize the SCORE statement compared to the CODE sentence in PROC LOGISTIC.
  • Make use of the INMODEL/OUTMODEL parameters in PROC LOGISTIC.
  • Explain how to evaluate new data after developing a model using a biased sample.
  • Prepare inputs for the predictive model.
  1. Performance – 20%

  • Identify possible issues while preparing input data for a model.

  • Identify complications that missing values might create in predicting models and rating new data sets.
  • Recognize the limits of complete case analysis.
  • Identify issues caused by category variables with several levels.
  • Address the issue of unnecessary variables.
  • Address the issue of irrelevant and duplicated variables.
  • Address nonlinearities and the issues they cause in prediction models.
  • Address outliers and the challenges they generate in prediction models
  • Define quasi-complete separation.
  • Discuss the impact of encounters.
  • Determine whether to oversample data.
  • Using loops, arrays, conditional expressions, and functions, use the DATA phase to manipulate data.

  • Use ARRAYs to generate missing indications.
  • Implement ARRAYS, LOOP, IF, and declarative OUTPUT statements.
  • Increase the prediction ability of categorical inputs.
  • Reduce categorical variables to fewer levels.
  • Explain thresholding.
  • Explain the Greenacre technique.
  • Apply Greenacre’s approach to cluster categorical variable levels using the CLUSTER function.
  • Convert classification variables continuously using the smooth weight of probability.
  • The CORR approach can screen variables for obsolescence and non-linear relationships.

  • Explain how Hoeffding’s D and Pearson statistics can identify irrelevant factors and non-linear relationships.
  • Generate Spearman and Hoeffding’s D statistics using the CORR technique.
  • Analyze scatter plots using Hoeffding’s D and Spearman statistics to detect irrelevant factors and non-linear correlations.
  • Empirical logit graphs are used to screen variables for nonlinearity.
  • Utilize the RANK technique to categorize continuous input variables.
  • Interpret the RANK procedure output.
  • Using the MEANS method, compute the sum and averages for goal cases and total events.
  • Use SGPLOT to generate empirical logit graphs.
  • Analyze empirical logit charts.
  1. Measure Model Performance – 25%.

  • Model performance measuring using the principles of honest assessment.

  • Describe methods for accurately evaluating classifier performance.
  • Explain overfitting.
  • Explain the distinction between validation and test data.
  • Evaluate the impact of data preparation before splitting.
  • Use the confusion matrix to evaluate classifier performance

  • Describe the confusion matrix.
  • Define the following terms: accuracy, error percentage, sensitivity, specificity, PV+, and PV.
  • Explain the impact of the oversampling on the confusion matrix.
  • Adjust the confusion matrix for oversampling.
  • Selecting a model and validation based on training and validation data
  • Divide knowledge into training and validation sets using the SURVEYSELECT technique.
  • Describe the subset selection strategies available in PROC LOGISTIC.
  • Discuss how to determine interactions.
  • Generate an interaction plot using PROC LOGISTIC data.
  • Choose a model that fits Statistical Business Analysis.
  • (BIC, AIC, KS, and Brier score).
  • Generate and analyze graphs (ROC, lift, and gains charts) for comparative analysis and selection.

  • Explain and understand charts (ROC, Lift, Gains)
  • Generate a ROC curve
  • Employ ROC and ROCCONTRAST commands to produce several models’ overlay graphics of ROC curves.
  • Explain how depth relates to the wins chart.
  • Determine practical cut-off values for scoring.
  • Create a choice rule that optimizes predicted profit.
  • Explain how to utilize the profit matrix to calculate the profit per evaluated client of .



Statistical Business Analysis

The SAS designed this certificate to examine a candidate’s knowledge and competence in the abovementioned areas through a certification exam. The SAS Statistical Business Analysis (A00-240) Certification test has a high market value because of the SAS brand associated with it.

Most exam practice test resources available online have not been updated. However, Troytec updates the genuine SAS Certified Statistical Business Analysis dumps throughout time.

Furthermore, the professionals created three forms for our SAS  exam preparation test material. These specialists have compiled a PDF file including the genuine SAS Certified Statistical Business Analysts test questions that will likely appear on the final certification exam. The A00-240 SAS Statistical Business Analysis exam dumps PDF download is compatible with all smart devices, including smartphones, tablets, laptops, and PCs.

FAQs (Frequently Asked Questions)

Who Should Take The A00-240 Certification Exam?

The A00-240 SAS Certified Statistical Business Analysis Using SAS 9 exam is intended for SAS professionals who utilize SAS/STAT software to understand and analyze complicated statistical data.


What Is The Passing Score On The SAS Certification Exam?

SAS & Pearson VUE jointly conduct this test having 40 to 45 multiple-choice and short-answer questions. It takes 135 minutes to finish the exam. The average passing score is 725 (a scoring range of 200 to 1,000 points).


Is There Any Prerequisite For Taking The A00-240 Certification?

Before taking the SAS Certified Statistical Business Analysis Using SAS 9 exam, you should have finished the equivalent of an undergrad statistics certification, which includes p-values, hypothesis testing, evaluation of variance, and regression.

Leave a Reply

Your email address will not be published. Required fields are marked *

Pass Your IT Certification Exams on FIRST TRY Using our Exam Products & accelerate your Career is Providing IT Certification Exams for over 500+ Exams.
We offer Quality Products in PDF & Test Engine format which helps our Clients pass the Exams using our Products.

© Copyright 2022 Troytec, Inc All rights reserved.

Our Newsletter

Subscribe to our newsletter to get our news & deals delivered to you.

Get in Touch