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SAS-ST131 | Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

SAS-ST131

-

3 Day(s)

Instructor-Led Training

from SGD $3080*

Course Schedules

19 Feb 2024 - 21 Feb 2024

Enroll/ Enquire Now

*Course Pricing Subjected to Terms & Conditions.

Course Overview

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.

A more advanced treatment of ANOVA and regression occurs in the Statistics 2: ANOVA and Regression course. A more advanced treatment of logistic regression occurs in the Categorical Data Analysis Using Logistic Regression course and the Predictive Modeling Using Logistic Regression course.

Who Should Enrol?

Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables

Course Outcome

• Generate descriptive statistics and explore data with graphs

• Perform analysis of variance and apply multiple comparison techniques

• Perform linear regression and assess the assumptions

• Use regression model selection techniques to aid in the choice of predictor variables in multiple regression

• Use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression

• Use chi-square statistics to detect associations among categorical variables

• Fit a multiple logistic regression model

• Score new data using developed models.

LEARNING PATHWAY

Module 1: Course Overview and Review of Concepts

Module 2: ANOVA and Regression

Module 3: More Complex Linear Models

Module 4: Model Building and Effect Selection

Module 5: Model Post-Fitting for Inference

Module 6: Model Building and Scoring for Prediction

Module 7: Categorical Data Analysis

FULL COURSE OUTLINE 👉

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