內容簡介
商務統計:決策與分析
現在商業競爭日益激烈，有效做出商務決策變得至關重要。《商務統計:決策與分析(英文版)》從實際的商業問題出發，詳細闡述如何利用數據進行信息決策，並將統計概念與實際問題聯繫起來，告訴讀者如何尋找模式從數據建立統計模型，以及如何提供調查結果。書中涵蓋了套用統計學在當代商務經濟領域中幾乎所有的重要套用，並且統計軟體（包括Excel、Mirlitab等）的使用貫穿全書。
作者簡介
斯泰恩，Robert A.Stine，於普林斯頓大學獲得博士學位。自1983年以來他一直在賓夕法尼亞大學沃頓商學院講授商務統計學課程。在任教期間，他獲得了多項教學獎，包括MBA核心教學獎、David W.Hauck優秀教學獎。他的研究領域包括計算機軟體、時間序列分析和預測、與模型識別和選擇相關的一般問題等。
福斯特，Dean P.Foster，於馬里蘭大學獲得博士學位。他曾在芝加哥大學任教，自1992年以來任教於賓夕法尼亞大學沃頓商學院。他講授的課程有商務統計初步、機率論與馬爾可夫鏈、統計計算和高等統計學等。其研究領域包括隨機過程的統計推斷、博弈論、機器學習和變數選擇。
圖書目錄
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
13.1twosurprisingproperties 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 thefittedline
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: leveragedoutliers
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
