Richard A. Johnson

Dean W. Wichern

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.

Applied Multivariate Statistical Analysis (Classic Version)

John E. Hanke

For undergraduate and graduate courses in Business Forecasting. Written in a simple, straightforward style, Business Forecasting 9/e presents basic statistical techniques using practical business examples to teach students how to predict long-term forecasts.

Business Forecasting

KEY BENEFIT: The ninth edition of Business Forecasting presents basic statistical techniques that are useful for preparing individual business forecasts and long-range plans. Written in a simple, straightforward style and making extensive use of practical business examples, the book includes many cases that provide readers with the necessary link between theoretical concepts and their real-world applications. Readers should have a basic knowledge of statistics and be familiar with computer applications such as word processing and spreadsheets. KEY TOPICS: The book first presents background material such as the nature of forecasting and a quick review of basic statistical concepts; proceeds with the exploration of data patterns and choosing a forecasting technique; covers averaging the smoothing techniques and time series decomposition; emphasizes causal forecasting techniques such as correlation, regression, and multiple regression analysis; and concludes with judgmental forecasting and forecast adjustments. MARKET: Useful as a reference for students and professionals with job titles including: forecasting manager, marketing manager, production manager, and analyst.

Dean W. Wichern

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