- A. Framework for Statistical
Thinking

B. Phases of Empirical Model Building

C. Objectives and Typical Applications of Statistical Modeling

**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

- 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

- 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

- 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)

- A. Regression Models for
Binary Data

B. Multiple Logistic Regression Model

C. Model-Building Strategies and Methods

D. Model Adequacy Checking

- 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