Business statistics : a first course
著者
書誌事項
Business statistics : a first course
Addison Wesley, c2011
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注記
Includes index
内容説明・目次
内容説明
Professors Norean Sharpe (Georgetown University), Dick De Veaux (Williams College), and Paul Velleman (Cornell University) have taught at the finest business schools and draw on their consulting experience at leading companies to show readers how statistical thinking is vital to modern decision making. Managers make better business decisions when they understand statistics, and Business Statistics gives readers the statistical tools and understanding to take them from the classroom to the boardroom. Hundreds of examples are based on current events and timely business topics. Short, accessible chapters allow for flexible coverage of important topics, and the conversational writing style maintains readers' interest and improves understanding.
目次
PART I EXPLORING AND COLLECTING DATA
1. Statistics and Variation
1.1 So, What Is Statistics?
1.2 How Will This Book Help?
2. Data
2.1 What Are Data?
2.2 Variable Types
2.3 Data Sources-Where, How, and When
3. Surveys and Sampling
3.1 Three Ideas of Sampling
3.2 A Census-Does It Make Sense?
3.3 Populations and Parameters
3.4 Simple Random Sample (SRS)
3.5 Other Sample Designs
3.6 Defining the Population
3.7 The Valid Survey
4. Displaying and Describing Categorical Data
4.1 The Three Rules of Data Analysis
4.2 Frequency Tables
4.3 Charts
4.4 Contingency Tables
5. Displaying and Describing Quantitative Data
5.1 Displaying Distributions
5.2 Shape
5.3 Center
5.4 Spread of the Distribution
5.5 Shape, Center, and Spread-A Summary
5.6 Five-Number Summary and Boxplots
5.7 Comparing Groups
5.8 Identifying Outliers
5.9 Standardizing
*5.10 Time Series Plots
Transforming Skewed Data-On CD-ROM
6. Correlation and Linear Regression
6.1 Looking at Scatterplots
6.2 Assigning Roles to Variables in Scatterplots
6.3 Understanding Correlation
6.4 Lurking Variables and Causation
6.5 The Linear Model
6.6 Correlation and the Line
6.7 Regression to the Mean
6.8 Checking the Model
6.9 Variation in the Model and R2
6.10 Reality Check: Is the Regression Reasonable?
Straightening Scatterplots-On CD-ROM
PART II UNDERSTANDING DATA AND DISTRIBUTIONS
7. Randomness and Probability
7.1 Random Phenomena and Probability
7.2 The Nonexistent Law of Averages
7.3 Different Types of Probability
7.4 Probability Rules
7.5 Joint Probability and Contingency Tables
7.6 Conditional Probability
7.7 Constructing Contingency Tables
7.8 Probability Trees
*7.9 Reversing the Conditioning: Bayes's Rule
8. Random Variables and Probability Models
8.1 Expected Value of a Random Variable
8.2 Standard Deviation of a Random Variable
8.3 Properties of Expected Values and Variances
8.4 Discrete Probability Models
8.5 Continuous Random Variables
9. Sampling Distributions and Confidence Intervals forProportions
9.1 Simulations
9.2 The Sampling Distribution for Proportions
9.3 Assumptions and Conditions
9.4 The Central Limit Theorem-The Fundamental Theorem of Statistics
9.5 A Confidence Interval
9.6 Margin of Error: Certainty vs. Precision
9.7 Critical Values
9.8 Assumptions and Conditions
9.9 Choosing the Sample Size
A Confidence Interval for Small Samples-On CD-ROM
10. Testing Hypotheses about Proportions
10.1 Hypotheses
10.2 A Trial as a Hypothesis Test
10.3 P-Values
10.4 The Reasoning of Hypothesis Testing
10.5 Alternative Hypotheses
10.6 Alpha Levels and Significance
10.7 Critical Values
10.8 Confidence Intervals and Hypothesis Tests
10.9 Two Types of Errors
*10.10 Power
11. Confidence Intervals and Hypothesis Tests for Means
11.1 The Sampling Distribution for Means
11.2 How Sampling Distribution Models Work
11.3 Gossett and the t-Distribution
11.4 A Confidence Interval for Means
11.5 Assumptions and Conditions
11.6 Cautions About Interpreting Confidence Intervals
11.7 One-Sample t-Test
11.8 Sample Size
*11.9 Degrees of Freedom-Why n - 1
12. Comparing Two Groups
12.1 Comparing Two Means
12.2 The Two-Sample t-Test
12.3 Assumptions and Conditions
12.4 A Confidence Interval for the Difference Between Two Means
*12.5 The Pooled t-Test
*12.6 Tukey's Quick Test
12.7 Paired Data
12.8 The Paired t-Test
13. Inference for Counts: Chi-Square Tests
13.1 Goodness-of-Fit Tests
13.2 Interpreting Chi-Square Values
13.3 Examining the Residuals
13.4 The Chi-Square Test of Homogeneity
13.5 Comparing Two Proportions
13.6 Chi-Square Test of Independence
PART III BUILDING MODELS FOR DECISION MAKING
14. Inference for Regression
14.1 The Population and the Sample
14.2 Assumptions and Conditions
14.3 Regression Inference
14.4 Standard Errors for Predicted Values
14.5 Using Confidence and Prediction Intervals
14.6 Extrapolation and Prediction
14.7 Unusual and Extraordinary Observations
*14.8 Working with Summary Values
*14.9 Linearity
Re-expressing data-On CD-ROM
The Ladder of Powers*-On CD-ROM
15. Multiple Regression
15.1 The Multiple Regression Model
15.2 Interpreting Multiple Regression Coefficients
15.3 Assumptions and Conditions for the Multiple Regression Model
15.4 Testing the Multiple Regression Model
15.5 Adjusted R2 and the F-statistic
The Logistic Regression Model-On CD-ROM
Indicator Variables-On CD-ROM
Adjusting for Different Slopes- Interaction Terms-On CD-ROM
Collinearity-On CD-ROM
16. Introduction to Data Mining
16.1 Direct Marketing
16.2 The Data
16.3 The Goals of Data Mining
16.4 Data Mining Myths
16.5 Successful Data Mining
16.6 Data Mining Problems
16.7 Data Mining Algorithms
16.8 The Data Mining Process
16.9 Summary
*Indicates an optional topic
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