دانلود کتاب آمار کاربردی و تحلیل داده‌های چند متغیره برای تجارت و اقتصاد

The second edition of this textbook, Applied Statistics and Multivariate Data Analysis for Business and Economics: A Modern Approach Using R, SPSS, Stata, and Excel, aims to familiarize students of business and economics and all other students of social sciences and humanities, as well as practitioners in firms with the basic principles, techniques, and applications of applied statistics and applied data analysis. Drawing on practical examples from business settings, it demonstrates the techniques of statistical testing, univariate, bivariate, and multivariate statistical analyses. The textbook covers a range of subject matter, from scaling, sampling, and data preparation to advanced analytic procedures for assessing multivariate relationships. Techniques covered include univariate analyses (e.g. measures of central tendencies, frequency tables, univariate charts, dispersion parameters), bivariate analyses (e.g. contingency tables, correlation), parametric and nonparametric tests (e.g. t-tests, Wilcoxon signed-rank test, U test, H test), and multivariate analyses (e.g. analysis of variance, linear regression, logistic regression, cluster analysis, and factor analysis). In addition, the book covers issues such as time series and indices, classical measurement theory, point estimation, and interval estimation. Each chapter concludes with a set of exercises. In this way, it addresses all of the topics typically covered in university courses on statistics and advanced applied multivariate analysis. In writing this book, I have consistently endeavoured to provide readers with an understanding of the thinking processes underlying complex methods of data analysis. I believe this approach will be particularly valuable to those who might otherwise have difficulty with the formal method of presentation used by many other textbooks in statistics. In numerous instances, I have tried to avoid unnecessary formulas, attempting instead to provide the reader with an intuitive grasp of a concept before deriving or introducing the associated mathematics. Nevertheless, a book about statistics and data analysis that omits formulas would be neither possible nor desirable. Whenever ordinary language reaches its limits, the mathematical formula has always been the best tool to express meaning. To provide further depth, I have included practice problems and solutions at the end of each chapter, which are intended to make it easier for students to pursue effective self-study. The broad availability of statistical software now makes it possible to learn and to teach statistics in new ways. Indeed, students today have access to a range of powerful computer and online applications, from Excel to various professional statistical packages. Accordingly, this textbook does not confine itself to presenting statistical methods, but also addresses the use of programmes such as R, SPSS, Stata, and Excel. To aid the learning process, datasets have been made available on the springer website, along with other supplemental materials, allowing all of the examples and practice problems to be recalculated and reviewed. I want to take this opportunity to thank all those who have collaborated in making this book possible. Well-deserved gratitude for their critical review of the manuscript and valuable suggestions goes to Uli Föhl, Wolfgang Gohout, Bernd Kuppinger, Bettina Müller, Bettina Peters, Wolfgang Schäfer (†), Lucais Sewell, and Kirsten Wüst, as well as many other unnamed individuals. Any errors or shortcomings that remain are entirely my own. Finally, this book could not have been possible without the ongoing support of my family. They deserve my very special gratitude.