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# Introduction to Statistics Using SAS9.2®: ANOVA, Linear Regression & Logistic Regression- ST092

This three-day course is an excellent follow-on to the SAS Programming 1: Essentials course for anyone using, or wanting to use, statistics in a business environment. It covers a range of introductory statistical topics including the best way to describe your data, statistical inference, analysis of variance, simple and multiple linear regression, categorical data analysis, and an introduction to binary logistic regression.

For a more in-depth learning of logistic regression please see the Categorical Data Analysis using Logistic Regression course.

## Duration

 3 days - Classroom

## Learn how to

• construct graphs to explore, summarise and interpret data,
• assess the precision of your statistics,
• examine ways of testing business questions,
• examine relationships between variables,
• produce predictions of continuous target variables by fitting simple and multiple linear regression models explain why categorised data are treated differently,
• know what to do if your target variable is binary (e.g. Yes/No, Default/Repay)

## Who should attend

Anyone who would like to learn or know more about using statistical tests within their work environment. This course is not industry specific and emphasis will be placed on using SAS to carry out data investigation, analyses and the interpretation of the results to answer business problems.

## Prerequisites

Before attending this course, you should be able to execute SAS programs, create SAS data sets and understand SAS Procedures. This knowledge can be gained by attending the SAS Programming 1: Essentials course. No prior knowledge of statistics is necessary.

## Course Contents

### Business Problems and Statistical Solutions:

• Understanding the purpose of statistics
• Calculating some simple summary statistics
• Interpreting output from the UNIVARIATE procedure.
• Examining the variability of data, why can we never be sure?

• Introduction to terminology for testing questions
• What is a t-test and when is it used?
• How can I obtain and interpret a p-value?
• Interpreting output from the TTEST procedures
• What is analysis of variance (ANOVA)

### Linear Regression

• Exploring the relationship between two continuous variables
• Measuring a linear relationship using correlation
• Interpreting the output from the CORR procedure
• Understanding the misuses of correlation.
• Using Simple Linear Regression.
• Is our target variable related to more than one variable?
• Can we get better predictions by using more variables (Multiple Linear Regression)?
• How can we select the 'best' variables?

### Logistic Regression

• Examining categorised data with the FREQ procedure
• Examining and testing for an association between two variables
• Calculating and interpreting the chi-square test for association
• Why do we need to do something different?
• What is logistic regression and how does it work?
• How can I interpret the results from the LOGISTIC procedure?
• What is an odds ratio and why is it useful?

• ODS Statistical Graphics
• Paired T-test
• Fishers exact p-values
• Non-parametric ANOVA
• Partial Leverage plots
• Interaction Plots.