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Statistics 2: ANOVA and Regression

Course Details
Code: ST242
Tuition (USD): $2,100.00 • Classroom (3 days)
$2,100.00 • Virtual (3 days)

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.

Skills Gained

  • fit polynomial regression models using the GLMSELECT and REG procedures
  • select models based on several statistics and automatic model selection methods using PROC GLMSELECT
  • evaluate model fit and model assumptions using the GLMSELECT, REG, GLM, GENMOD, and UNIVARIATE procedures
  • fit Poisson and negative binomial models using the GENMOD procedure, and fit gamma regression models using the GLIMMIX procedure
  • perform analysis of variance using the GLM procedure
  • write LSMESTIMATE statements in PROC GLM
  • fit ANCOVA models using PROC GLM
  • fit models with random effects using the GLIMMIX procedure
  • create a variety of statistical graphs.

Who Can Benefit

  • Data analysts and researchers with some statistical training

Prerequisites

  • Before attending this course, you should
  • have some experience creating and managing SAS data sets, which you can gain from the SAS(R) Programming I: 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 I: Introduction to ANOVA, Regression, and Logistic Regression course.

Course Details

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
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