# Statistics, Hypothesis Testing, & Regression

Course Overview

This course teaches participants the fundamental concepts and methods needed to organize and analyze data and make objective decisions based on the data. Participants will also learn to compare groups in order to determine whether they are statistically similar or not. Additionally, the course instructs in the development of mathematical models to predict outcomes and understand key factors affecting our processes. Knowledge of basic algebra is helpful. Computer software is utilized although an understanding of underlying concepts and methods is stressed.

Seminar Content

1. Basic Statistics & Distributions
• Data Types
• Populations & Samples
• Measures of Central Tendency and Variation
• Probability Distributions
• The Normal Distribution
2. Graphical Analysis
• Pareto Charts
• Run Charts
• Boxplots
• Histograms
• Scatter Plots
3. Hypothesis Testing: A Single Process
• Hypothesis Testing for Means
• Test Statistics & Critical Values
• Type I and Type II Errors
• P-values
• Confidence Intervals for the Estimates
• Hypothesis Testing for Paired Data
• Hypothesis Testing for a Proportion
• Hypothesis Testing for a Variance
4. Hypothesis Testing: Two Processes
• Hypothesis Testing for Two Variances
• Hypothesis Testing for Two Means
• Hypothesis Testing for Two Proportions
5. Hypothesis Testing: Multiple Processes
• Using ANOVA for Comparing Means
• Testing ANOVA Assumptions
• Performing ANOVA using Software
• Tukey’s Method for Multiple Comparisons
6. Power & Sample Size for Hypothesis Tests
• Type II Error Probability/Power
• Factors Affecting Power
• Computing Sample Sizes for Desired Power
7. Relating Two Variables
• Scatter Plots
• Correlation Coefficient
8. Linear Models with a Single Factor
• Model Building Concepts
• Simple Linear Regression
• R Squared
• Residual Analysis
• Hypothesis Testing for the Model
• Making Predictions
• Confidence and Prediction Intervals
9. Linear Models with Multiple Factors
• Multiple Regression
• Testing for Significant Explanatory Variables
• Model Performance
• Model Building Strategies and Tools
• Confidence and Prediction Intervals
• Multi-collinearity
• Influential Data Points