Duration: Hours

Training Mode: Online


Statistical Bussiness Analyst :  In this course is designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis. It covers analysis of variance, linear and logistic regression, preparing inputs for predictive models, and measuring model performance. Suitable for Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables; as well as modelers and analysts who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries.

Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

a). Generate Descriptive Statistics and explore data with graphs

b). Perform analysis of Variance and apply multiple Comparison techniques

c). Perform linear Regression and assess the Assumptions

d). Use Regression Model Selection techniques to aid in the choice of Predictor Variables in multiple regression

e). Use Diagnostic Statistics to assess statistical assumptions and identify potential outliers in multiple regression

f). Use chi – square statistics to detect associations among Categorical Variables

g). Fit a multiple Logistic Regression model.

Predictive Modeling Using Logistic Regression

a). Use Logistic Regression to model an Individual’s behavior as a function of known inputs

b). Create effect plots and odds ratio plots using ODS Statistical Graphics

c). Handle missing data values

d). Tackle Multicollinearity in your Predictors

e). Assess model performance and compare models.

Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

a). Prerequisite Basic Concepts

b). Introduction to Statistics

c). Tests and Analysis of Variance

d). Linear Regression

e). Linear Regression Diagnostics

f). Categorical Data Analysis

Predictive Modeling Using Logistic Regression

a). Predictive Modeling

b). Fitting the Model

c). Preparing the Input Variables

d). Classifier Performance


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