Applied Regression Modeling: A Business Approach
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculus
Regression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression analysis to make informed decisions. Applied Regression Modeling: A Business Approach offers a practical, workable introduction to regression analysis for upper-level undergraduate business students, MBA students, and business managers, including auditors, financial analysts, retailers, economists, production managers, and professionals in manufacturing firms.
The book's overall approach is strongly based on an abundant use of illustrations and graphics and uses major statistical software packages, including SPSS(r), Minitab(r), SAS(r), and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided, although Excel does not have a built-in capability to carry out all the techniques discussed.
Applied Regression Modeling: A Business Approach offers special user features, including:
* A companion Web site with all the datasets used in the book, classroom presentation slides for instructors, additional problems and ideas for organizing class time around the material in the book, and supplementary instructions for popular statistical software packages. An Instructor's Solutions Manual is also available.
* A generous selection of problems-many requiring computer work-in each chapter with fullyworked-out solutions
* Two real-life dataset applications used repeatedly in examples throughout the book to familiarize the reader with these applications and the techniques they illustrate
* A chapter containing two extended case studies to show the direct applicability of the material
* A chapter on modeling extensions illustrating more advanced regression techniques through the use of real-life examples and covering topics not normally seen in a textbook of this nature
* More than 100 figures to aid understanding of the material
Applied Regression Modeling: A Business Approach fully prepares professionals and students to apply statistical methods in their decision-making, using primarily regression analysis and modeling. To help readers understand, analyze, and interpret business data and make informed decisions in uncertain settings, many of the examples and problems use real-life data with a business focus, such as production costs, sales figures, stock prices, economic indicators, and salaries. A calculus background is not required to understand and apply the methods in the book.
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A Business Approach 1 Foundations
A Business Approach 2 Simple linear regression
A Business Approach 3 Multiple linear regression
A Business Approach 4 Regression model building I
A Business Approach 5 Regression model building II
A Business Approach 6 Case studies
A Business Approach 7 Extensions
A Business Approach Appendix A Computer software help
A Business Approach Appendix B Critical values for tdistributions
A Business Approach Appendix C Notation and formulas
A Business Approach Appendix D Mathematics refresher
A Business Approach Appendix E Brief answers to selected problems
A Business Approach Glossary
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97.5th percentile Adjusted Regression Model average calculate computer help 20 conﬁdence interval critical value data ﬁle dataset degrees of freedom dialog box Error t-stat Pr Figure ﬁnal ﬁnd ﬁrst ﬁtted line ﬂoor histogram hit OK horizontal axis hypothesis test increase indicator variables Intercept linear regression model Microsoft Excel Minitab Model Estimate Std Model MultipleR RSquared model see computer Model Summary Adjusted multiple linear regression MultipleR RSquared RSquared natural logarithm null hypothesis particular point estimate population mean prediction interval predictor effect plots predictor variables QQ-plot qualitative variable random errors regression line Regression Model MultipleR regression parameter regression standard error reject the null represents residual plots response variable RSquared RSquared Std sale price sample data sample mean scatterplot signiﬁcance level simple linear regression standard deviation standard error Statistical software output studentized residuals Summary Adjusted Regression t-distribution t-statistic tail p-value tail test test statistic transformations upper tail vertical axis