Regression Analysis and Hypothesis Testing

Overview

Participant Profile: Employees involved in advanced analysis, interested in applying statistical tools to determine if new data represents significant change or just normal noise

Duration: 2-8 hours

Course Size: 10-15 participants

Course Description: Students will learn advanced techniques for determining process change signals and comparing like processes. The course teaches students how to perform regressions and various hypothesis testing analyses, interpret the results, optimize the analysis to fit the underlying data, quantify relationships between variables, and predict future outcomes. Course topics include linear regression, normal theory, Ordinary Least Squares, t-tests, ANOVA and ANOM techniques, Chi-Square, Test for Equal Variance, multiple linear regression, residual analysis, Type I/II errors, and prediction intervals.

Learning Objectives: Upon satisfactory completion, participants will:

- Setup and run regressions to quantify relationships between variables
- Optimize and forecast using regression techniques
- Utilize the appropriate hypothesis test to statistically prove process change