I. Framework for Statistical Thinking

A. Context of Statistical Thinking: The Decision Environment

B. Conceptual Framework Model and Scientific Method

II. Basic Sample Statistics and STATGRAPHICS

A. STATGRAPHICS Software Fundamentals

B. Basic Sample Statistics: Numeric and Graphical Portrayals

III. Statistical Inference and the Framework

A. Inference Cycle and Probability Models: Normal and Non-normal Distribution Fitting with Application to Process Capability Analysis

B. Repeated Sampling and Sampling Distributions

1. Distribution of Sample Statistics (mean, median, s)

2. Common Sampling Distributions (z, t, c², F)

C. Statistical Uncertainty: Concepts and Measures

1. Sources of Uncertainty

2. Statistical Uncertainty

3. Unbiased Point Estimation

4. Statistical Interval Estimation

5. Hypothesis Testing (comparing means and sigma's)

6. Decisions and Errors

7. Statistical Significance, Power and Sample Size

8. When Normality Assumptions Fail

D. Paradigms of the Framework: Benchmarking, Process Improvement, Process Comparison, and Product/Process Validation Decisions

A. Introduction and Terminology: Statistical Framework

B. Categorical Factors with Continuous Response Variables

1. Analysis of Variance Model

2. Model Estimation and Validation

3. Hypothesis Testing

4. Multiple Comparison Inferences

5. Model Deductions, Predictions and Decisions

6. Visualizing Differences

C. Continuous Factors with Continuous Response Variables

1. Linear Regression Model

2. Model Estimation and Validation

3. Hypothesis Testing

4. Model Deductions, Predictions and Decisions

5. Non-linear Model Forms

6. Multiple Regression Concepts

D. Associations Among Variables

1. Continuous Variables: Correlation

2. Categorical Variables: Contingency Tables

_________________________________________

V. Statistical Process Control (optional 5th day)

A. General SPC Concepts

1. Quality Concepts

2. Process Variation vs Product Specifications

3. Process Stability vs Process Capability

B. Process Monitoring and Control

1. Nature and Objectives of Process Monitoring

2. Statistical Basis of Control Chart Design

3. Control Charts for Variables

4. Control Charts for Attributes

5. Guidelines for Implementing Control Charts

C. Process Capability Analysis

1. Process Variation and Product Specifications

2. Process Performance

3. Fitting Statistical Models to Process Data

4. Estimating Process Yield

5. Estimating the "Natural Tolerance Limits" of a Process

6. Process Capability and Process Performance Indices (Normal and Non-Normal Data)

7. Gauge Capability (R&R) Studies

D. Industry Specific Charts (overview of applicable charts)

1. Cumulative Sum (CuSum) Control Charts

2. Exponentially Weighted Moving Average (EWMA) Charts

3. Multivariate Process Monitoring

4. Autocorrelated Processes (if appropriate for students' needs)