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Titlebook: Basic Business Statistics; A Casebook Dean P. Foster,Robert A. Stine,Richard P. Waterman Textbook 1998 Springer-Verlag New York, Inc. 1998

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發(fā)表于 2025-3-21 18:26:47 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Basic Business Statistics
期刊簡(jiǎn)稱A Casebook
影響因子2023Dean P. Foster,Robert A. Stine,Richard P. Waterman
視頻videohttp://file.papertrans.cn/181/180963/180963.mp4
發(fā)行地址Unique approach encourages the use of statistics to answer important business questions.Includes commands necessary to analyze data in the statistical packages JMP and Minitab.Large workbook format an
圖書封面Titlebook: Basic Business Statistics; A Casebook Dean P. Foster,Robert A. Stine,Richard P. Waterman Textbook 1998 Springer-Verlag New York, Inc. 1998
影響因子Preface Statistics is seldom the most eagerly anticipated course of a business student. It typically has the reputation of being a boring, complicated, and confusing mix of mathematical formulas and computers. Our goal in writing this casebook and the companion volume (Business Analysis Using Regression) was to change that impression by showing how statistics yields insights and answers interesting business questions. Rather than dwell on underlying formulas, we show how to use statistics to answer questions. Each case study begins with a business question and concludes with an answer to that question. Formulas appear only as needed to address the questions, and we focus on the insights into the problem provided by the mathematics. The mathematics serves a purpose. The material in this casebook is organized into 11 "classes" of related case studies that develop a single, key idea of statistics. The analysis of data using statistics is seldom very straightforward, and each analysis has many nuances. Part of the appeal of statistics is this richness, this blending of substantive theories and mathematics. For newcomers, however, this blend is too rich, and they are easily overwhelmed
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Der Umgang mit dem kranken Tierous types of processes. The key idea in developing these charts combines the empirical rule from Class 2 with our observation that summary measures such as the mean vary less than the original data. How much less? Today we show how variation in the average, as measured by its standard error, is rela
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https://doi.org/10.1007/978-3-540-72546-6We all sense that large samples are more informative than small ones, because of something vaguely called the “l(fā)aw of averages.” This class considers how to use the information in a sample to make a claim about some population feature. The normal distribution plays a key role.
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