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

This is a Free Course provided to Business Analytics and Data Science Courses enrolled students.

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.Learn how to

• 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 should attend

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

Before attending this course, you should:

• Have completed the equivalent of an undergraduate course in statistics covering p-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 Outline

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 collinearity.

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.

Latest Posts

##### Introduction to Statistical Concepts
Select the fields to be shown. Others will be hidden. Drag and drop to rearrange the order.
• Image
• SKU
• Rating
• Price
• Stock
• Availability