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

Basic Statistics & Distributions

Data Types
Populations & Samples
Measures of Central Tendency and Variation
Probability Distributions
The Normal Distribution

Graphical Analysis

Pareto Charts
Run Charts
Boxplots
Histograms
Scatter Plots

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 Variances

Hypothesis Testing: Two Processes

Hypothesis Testing for Two Variances
Hypothesis Testing for Two Means
Hypothesis Testing for Two Proportions

Hypothesis Testing: Multiple Processes

Using ANOVA for Comparing Means
Testing ANOVA Assumptions
Performing ANOVA using Software
Tukey’s Method for Multiple Comparisons

Relating Two Variables

Scatter Plots
Correlation Coefficient

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

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

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