Hack into SAS Statistical Business Analysis Certification

COURSE ABSTRACT

This is an exam-based prep "course", and the exam questions are specifically tailored for the certification: latest questions, and many case studies for coding practice! Each of the item is fully covered with several questions and any blind spot is avoided.

You will have about 4-6 practice exams, in which you will have about 20 multiple choice questions. You will be fully prepared once you go through these practice exams.

Save time and money by taking these practice exams. Let's hack into the certification exam!

The certificate requires the knowledge and experience in:

• Analysis of variance.

• Linear and logistic regression.

• Preparing inputs for predictive models.

• Measuring model performance.

The proportion of each item is as follows:

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

This exam 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 exam ID A00-240; required when registering with Pearson VUE.