I. Statistical Thinking
in Industrial Decision-Making
A. Framework for Statistical
Thinking
B. Phases of Empirical Model
Building
C. Objectives and Typical
Applications of Statistical Modeling
II. STATGRAPHICS Fundamentals
III. Fitting Distributions
to One-Variable Data
A. Discrete vs Continuous
Distributions
B. Some Important Models
C. Graphical Representations
D. Tests of Normality and
Goodness of Fit
IV. Simple Regression
A. Straight-line Regression
Model
B. Transformations to a
Straight Line
C. Least Squares Estimators
and Their Properties
D. Inference for the Slope
and Intercept
E. Model Adequacy
Checking and Validation
F. Predictions from the
Model
V. Multiple Linear Regression
A. Linear Regression Models
B. Least Squares Estimators
and Their Properties
C. Hypothesis Tests and
Confidence Intervals for Regression Coefficients
D. Model Adequacy Checking
and Validation
E. Predictions from the
Model
F. Variable Selection Procedures
G. Polynomial Regression
H. Ridge Regression
VI. General Linear Model
A. Models with Categorical
and Quantitative Factors
B. Nested and Crossed Factors
C. Analysis of Covariance
and Separate Slopes
D. Split Plot and Repeated
Measures Designs
E. Multivariate Analysis
of Variance (MANOVA)
VII. Logistic Regression
A. Regression Models for
Binary Data
B. Multiple Logistic Regression
Model
C. Model-Building Strategies
and Methods
D. Model Adequacy Checking
VIII. Nonlinear Regression
A. Models with No Linear
Transform
B. Fitting Nonlinear Models
C. Hypothesis Tests and
Confidence Intervals for Model Parameters
D. Plotting the Fitted Model
E. Model Adequacy Checking
IX. Time Series Analysis,
Forecasting and Control
A. Descriptive Methods
B. Box-Jenkins Methodology
C. Forecasting
D. Design of Feedforward
and Feedback Control Schemes