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

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on tests, ANOVA, and linear regression, and includes a brief introduction to...

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$2,725 USD GSA  $2,037.58
Course Code ST142
Duration 3 days
Available Formats Classroom, Virtual
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This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on 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.

Skills Gained

  • 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.

Who Can Benefit

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

Prerequisites

  • Before attending this course, you should:
  • Have completed the equivalent of an undergraduate course in statistics covering -values, hypothesis testing, analysis of variance, and regression.
  • Be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS® Programming 1: Essentials course.

Course Details

Course Overview and Review of Concepts

  • Descriptive statistics.
  • Inferential statistics.
  • Examining data distributions.
  • Obtaining and interpreting sample statistics using the UNIVARIATE procedure.
  • Examining data distributions graphically in the UNIVARIATE and FREQ procedures.
  • Constructing confidence intervals.
  • Performing simple tests of hypothesis.
  • Performing tests of differences between two group means using PROC TTEST.

ANOVA and Regression

  • Performing one-way ANOVA with the GLM procedure.
  • Performing post-hoc multiple comparisons tests in PROC GLM.
  • Producing correlations with the CORR procedure.
  • Fitting a simple linear regression model with the REG procedure.

More Complex Linear Models

  • Performing two-way ANOVA with and without interactions.
  • Understanding the concepts of multiple regression.

Model Building and Effect Selection

  • Automated model selection techniques in PROC GLMSELECT to choose from among several candidate models.
  • Interpreting and comparison of selected models.

Model Post-Fitting for Inference

  • Examining residuals.
  • Investigating influential observations.
  • Assessing collinearit.

Model Building and Scoring for Prediction

  • Understanding the concepts of predictive modeling.
  • Understanding the importance of data partitioning.
  • Understanding the concepts of scoring.
  • Obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM.

Categorical Data Analysis

  • Producing frequency tables with the FREQ procedure.
  • Examining tests for general and linear association using the FREQ procedure.
  • Understanding exact tests.
  • Understanding the concepts of logistic regression.
  • Fitting univariate and multivariate logistic regression models using the LOGISTIC procedure.
  • Using automated model selection techniques in PROC LOGISTIC including interaction terms.
  • Obtaining predictions (scoring) for new data using PROC PLM.
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