Statistics 2: ANOVA and Regression

Course code: ST242

This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course.

1 800 EUR

2 178 EUR including VAT

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Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 2 070 EUR

Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR

	Starting date	Type	Course duration	Language	Price without VAT
	Upon request	E-learning	21 hours	en	2 070 EUR	Register

	Upon request	Upon request	21 hours	en	1 800 EUR	Register
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Target group

Data analysts and researchers with some statistical training

Course structure

Regression

building and evaluating polynomial regression models using PROC GLMSELECT and PROC REG
dealing with violations of model assumptions and multicollinearity

Analysis of Variance

performing n-way ANOVA
interpreting significant interactions
writing LSMESTIMATE statements
performing evaluation of model assumptions and remedial measures

Regression Using Indicator Variables and Analysis of Covariance

building and interpreting analysis of covariance models using the GLM procedure
least squares means from an ANCOVA model
diagnostics and remedial measures for ANCOVA models

Generalized Linear Models

using the GENMOD procedure to fit Poisson and negative binomial regression models
using the GLIMMIX procedure to fit gamma regression models

Linear Mixed Models

performing linear mixed model analysis with PROC GLIMMIX

Prerequisites

Before attending this course, you should

have some experience creating and managing SAS data sets, which you can gain from the SAS Programming 1: Essentials course

be able to fit simple and multiple linear regression models using the REG procedure

be able to analyze a one-way analysis of variance using the GLM procedure

understand the statistical concepts of normal distribution, sampling distributions, hypothesis testing, and estimation

have completed a graduate-level course in regression and analysis of variance methods or the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

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