Introduction and Review of Basic Statistics
An Introduction to Forecasting
Forecasting and Data
Forecasting Methods
Errors in Forecasting
Choosing a Forescasting Technique
An Overview of Quantitative Forecasting Techniques
Basic Statistical Concepts
Populations
Probability
Random Samples and Sample Statistics
Continuous Probability Distributions
The Normal Probability Distribution
The t-Distribution, the F-Distribution, the Chi-Square Distribution
Confidence Intervals for a Population Mean
Hypothesis Testing for a Population Mean
Exercises
Regression Analysis
Simple Linear Regression
The Simple Linear Regression Model
The Least Squares Point Estimates
Point Estimates and Point Predictions
Model Assumptions and the Standard Error
Testing the Significance of the Slope and y Intercept
Confidence and Prediction Intervals
Simple Coefficients of Determination and Correlation
An F Test for the Model
Exercises
Multiple Linear Regression
The Linear Regression Model
The Least Squares Estimates, and Point Estimation and Prediction
The Mean Square Error and the Standard Error
Model Utility: R2, Adjusted R2, and the Overall F Test
Testing the Significance of an Independent Variable
Confidence and Prediction Intervals
The Quadratic Regression Model
Interaction
Using Dummy Variables to Model Qualitative Independent Variables
The Partial F Test: Testing the Significance of a Portion of a Regression Model
Exercises
Model Building and Residual Analysis
Model Building and the Effects of Multicollinearity
Residual Analysis in Simple Regression
Residual Analysis in Multiple Regression
Diagnostics for Detecting Outlying and Influential Observations
Exercises
Time Series Regression, Decomposition Methods, and Exponential Smoothing
Time Series Regression
Modeling Trend by Using Polynomial Functions
Detecting Autocorrelation
Types of Seasonal Variation
Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions
Growth Curves
Handling First-Order Autocorrelation
Exercises
Decomposition Methods
Multiplicative Decomposition
Additive Decomposition
The X-12-ARIMA Seasonal Adjustment Method
Exercises
Exponential Smoothing
Simple Exponential Smoothing
Tracking Signals
Holts Trend Corrected Exponential Smoothing
Holt-Winters Methods
Damped Trends and Other Exponential Smoothing Methods
Models for Exponential Smoothing and Prediction Intervals
Exercises
The Box-Jenkins Methodology
Nonseasonal Box-Jenkins Modeling and Their Tentative Identification
Stationary and Nonstationary Time Series
The Sample Autocorrelation and Partial Autocorrelation Functions: The SAC and SPAC
An Introduction to Nonseasonal Modeling and Forecasting
Tentative Identification of Nonseasonal Box-Jenkins Models
Exercises
Estimation, Diagnostic Checking, and Forecasting for Nonseasonal Box-Jenkins Models
Estimation
Diagnostic Checking
Forecasting
A Case Study
Box-Jenkins Implementation of Exponential Smoothing
Exercises
Box-Jenkins Seasonal Modeling
Transforming a Seasonal Time Series into a Stationary Time Series
Three Examples of Seasonal Modeling and Forecasting
Box-Jenkins Error Term Models in Time Series Regression
Exercises
Advanced Box-Jenkins Modeling
The General Seasonal Model and Guidelines for Tentative Identificatino
Intervention Models
A Procedure for Building a Transfer Function Model
Exercises
Statistical Tables
Matrix Algebra for Regression Calculations
Matrices and Vectors
The Transpose of a Matrix
Sums and Differences of Matrices
Matrix Multiplication
The Identity Matrix
Linear Dependence and Linear Independence
The Inverse of a Matrix
The Least Squares Point Esimates
The Unexplained Variation and Explained Variation
The Standard Error of the Estimate b
The Distance Value
Using Squared Terms
Using Interaction Terms
Using Dummy Variable
The Standard Error of the Estimate of a Linear Combination of Regression Parameters
Exercises
References.