| 词条 | 商务统计：决策与分析 |
| 释义 | 图书信息书 名: 商务统计:决策与分析 作 者：斯泰恩 出版社： 机械工业出版社 出版时间： 2011年6月1日 ISBN: 9787111342007 开本： 16开 定价: 119.00元 内容简介现在商业竞争日益激烈，有效做出商务决策变得至关重要。《商务统计:决策与分析(英文版)》从实际的商业问题出发，详细阐述如何利用数据进行信息决策，并将统计概念与实际问题联系起来，告诉读者如何寻找模式从数据建立统计模型，以及如何提供调查结果。书中涵盖了应用统计学在当代商务经济领域中几乎所有的重要应用，并且统计软件（包括Excel、Mirlitab等）的使用贯穿全书。 作者简介斯泰恩，Robert A.Stine，于普林斯顿大学获得博士学位。自1983年以来他一直在宾夕法尼亚大学沃顿商学院讲授商务统计学课程。在任教期间，他获得了多项教学奖，包括MBA核心教学奖、David W.Hauck优秀教学奖。他的研究领域包括计算机软件、时间序列分析和预测、与模型识别和选择相关的一般问题等。 福斯特，Dean P.Foster，于马里兰大学获得博士学位。他曾在芝加哥大学任教，自1992年以来任教于宾夕法尼亚大学沃顿商学院。他讲授的课程有商务统计初步、概率论与马尔可夫链、统计计算和高等统计学等。其研究领域包括随机过程的统计推断、博弈论、机器学习和变量选择。 图书目录preface iii index of applications xvii part onevariation 1introduction 1.1what is statistics? 1.2previews 1.3how to use this book92data 2.1data tables 2.2categorical and numerical data 2.3recoding and aggregation 2.4time series 2.5further attributes of data chapter summary 3describing categorical data 3.1looking at data 3.2charts of categorical data 3.3the area principle 3.4mode and median chapter summary 4describing numerical data 4.1summaries of numerical variables 4.2histograms and the distribution of numerical data 4.3boxplot 4.4shape of a distribution 4.5epilog chapter summary 5association between categorical variables 5.1contingency tables 5.2lurking variables and simpson’s paradox 5.3strength of association chapter summary 6association between quantitative variables 6.1scatterplots 6.2association in scatterplots 6.3measuring association 6.4summarizing association with a line 6.5spurious correlation chapter summary statistics in action casefinancial time series statistics in action caseexecutive compensation arttwo probability 7probability 7.1from data to probability 7.2rules for probability 7.3independent events chapter summary 8conditional probability 8.1from tables to probabilities 8.2dependent events 8.3organizing probabilities 8.4order in conditional probabilities chapter summary 9random variables 9.1random variables 9.2properties of random variables 9.3properties of expected values 9.4comparing random variables chapter summary 10association between random variables 10.1portfolios and random variables 10.2joint probability distribution 10.3sums of random variables 10.4dependence between random variables 10.5iid random variables 10.6weighted sums chapter summary 11probability models for counts 11.1random variables for counts 11.2binomial model 11.3properties of binomial random variables 11.4poisson model chapter summary 12the normal probability model 12.1normal random variable 12.2the normal model 12.3percentiles 12.4de partures from normality chapter summary statistics in action casemanaging financial risk statistics in action casemodeling sampling variation art three inference 13samples and surveys 13.1two surprising properties of sampling 13.2variation 13.3alternative sampling methods 13.4checklist for surveys chapter summary 14sampling variation and quality 14.1sampling distribution of the mean 14.2control limits 14.3using a control chart 14.4control charts for variation chapter summary 15confidence intervals 15.1ranges for parameters 15.2confidence interval for the mean 15.3interpreting confidence intervals 15.4manipulating confidence intervals 15.5margin of error chapter summary 16statistical tests 16.1concepts of statistical tests 16.2testing the proportion 16.3testing the mean 16.4other properties of tests chapter summary 17alternative approaches to inference 17.1a confidence interval for the median 17.2transformations 7.3prediction intervals 17.4proportions based on small samples chapter summary 18comparison 18.1data for comparisons 18.2two-sample t-test 18.3confidence interval for the difference 18.4other comparisons chapter summary statistics in action caserare events statistics in action casetesting association part four regression models 19linear patterns 19.1fitting a line to data 19.2interpreting the fitted line 19.3properties of residuals 19.4explaining variation 19.5conditions for simple regression chapter summary 20curved patterns 20.1detecting nonlinear patterns 20.2transformations 20.3reciprocal transformation 20.4logarithm transformation chapter summary 21the simple regression model 21.1the simple regression model 21.2conditions for the simple regression model 21.3inference in regression 21.4prediction intervals chapter summary 22regression diagnostics 22.1problem 1:changing variation 22.2problem 2: leveraged outliers 22.3problem 3:dependent errors and time series chapter summary 23multiple regression 23.1the multiple regression model 23.2interpreting multiple regression 23.3checking conditions 23.4inference in multiple regression 23.5steps in fitting a multiple regression chapter summary 24building regression models 24.1identifying explanatory variables 24.2collinearity 24.3removing explanatory variables chapter summary 25categorical explanatory variables 25.1two-sample comparisons 25.2analysis of covariance 25.3checking conditions 25.4interactions and inference 25.5regression with several groups chapter summary 26analysis of variance 26.1comparing several groups 26.2inference in anova regression models 26.3multiple comparisons 26.4groups of different size chapter summary 27time series 27.1decomposing a time series 27.2regression models 27.3checking the model chapter summary statistics in action caseanalyzing experiments statistics in action caseautomated modeling appendix: tables answers photo acknowledgments index |
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