标题：商务统计：决策与分析
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