Business Statistics with MML/MSL Student Access Code Card (for Ad Hoc Valuepacks)

Business Statistics with MML/MSL Student Access Code Card (for Ad Hoc Valuepacks)

Norean Sharpe

Richard De Veaux

Paul Velleman

ISBN 9780321728111
Godina Izdanja 2011
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Business Statistics, Second Edition, helps students gain the statistical tools and develop the understanding they’ll need to make informed business decisions using data. The dynamic approach conquers the modern challenges of teaching business statistics by making it relevant, emphasizing analysis and understanding over simple computation, preparing students to be more analytical, make better business decisions, and effectively communicating results.


This text features a wealth of real data applications, with coverage of current issues, such as ethics and data mining. It draws students in using a conversational writing style and delivers content with a fresh, exciting approach that reflects the authors’ blend of teaching, consulting, and entrepreneurial experiences. Learning tools such as the Plan/Do/Report guided examples prepare students to tackle any business problem they will encounter as a future business leader.


This book follows the GAISE Guidelines, emphasizing real data and real-world interpretations of analyses.

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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
Ethics in Action
Technology Help
Brief Cases: Credit Card Bank

3. Surveys and Sampling
3.1 Three Ideas of Sampling
3.2 Populations and Parameters
3.3 Other Sample Designs
3.4 The Valid Survey
3.5 How to Sample Badly
Ethics in Action
Technology Help: Random Sampling
Brief Cases: Market Survey Research
The GfK Roper Reports Worldwide Survey

4. Displaying and Describing Categorical Data
4.1 Summarizing a Categorical Variable
4.2 Displaying a Categorical Variable
4.3 Exploring Two Categorical Variables: Contingency Tables
Ethics in Action
Technology Help: Displaying Categorical Data on the Computer
Brief Cases: KEEN

5. Displaying and Describing Quantitative Data
5.1 Displaying Quantitative Variables
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
5.11 Transforming Skewed Data
Ethics in Action
Technology Help: Displaying and Summarizing
Quantitative Variables
Brief Cases Hotel Occupancy Rates
Value and Growth Stock Returns

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?
6.11 Non-linear Relationships
Ethics in Action
Technology Help: Correlation and Regression
Brief Cases: Fuel Efficiency
The U.S. Economy and Home Depot Stock Prices
Cost of Living
Mutual Funds
Case Study: Paralyzed Veterans of America


7. Randomness and Probability
Credit Reports and the Fair Isaacs Corporation
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
Brief Case: Market Segmentation

8. Random Variables and Probability Models
Metropolitan Life Insurance Company
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 Distributions
Ethics in Action
Brief Case: Investment Options

9. The Normal Distribution
9.1 The Standard Deviation as a Ruler
9.2 The Normal Distribution
9.3 Normal Probability Plots
9.4 The Distribution of Sums of Normals
9.5 The Normal Approximation for the Binomial
9.6 Other Continuous Random Variables
Ethics In Action
Brief Cases: The CAPE10
Technology Help: Making Normal Probability Plots

10. Sampling Distributions
Marketing Credit Cards: The MBNA Story
10.1 The Distribution of Sample Proportions
10.2 Sampling Distribution for Proportions
10.3 The Central Limit Theorem
10.4 The Sampling Distribution of the Mean
10.5 How Sampling Distribution Models Work
Ethics in Action
Brief Cases Real Estate Simulation
Part 1: Proportions
Case Study: Investigating the Central Limit Theorem


11. Confidence Intervals for Proportions
The Gallup Organization
11.1 A Confidence Interval
11.2 Margin of Error: Certainty vs. Precision
11.3 Assumptions and Conditions
11.4 Choosing the Sample Size
11.5 A Confidence Interval for Small Samples
Ethics in Action
Technology Help: Confidence Intervals for Proportions
Brief Cases: Investment
Forecasting Demand

12. Confidence Intervals for Means
Guinness & Co.
12.1 The Sampling Distribution for the Mean
12.2 A Confidence Interval for Means
12.3 Assumptions and Conditions
12.4 Cautions About Interpreting Confidence Intervals
12.5 Sample Size
12.6 Degrees of Freedom - Why (n-1)?
Ethics in Action
Technology Help: Inference for Means
Brief Cases: Real Estate
Donor Profiles

13. Testing Hypotheses
Dow Jones Industrial Average
13.1 Hypotheses
13.2 A Trial as a Hypothesis Test
13.3 P-values
13.4 The Reasoning of Hypothesis Testing
13.5 Alternative Hypotheses
13.6 Testing Hypothesis about Means - the One
13.7 Alpha Levels and Significance
13.8 Critical Values
13.9 Confidence Intervals and Hypothesis Tests
13.10 Two Types of Errors
13.11 Power
Ethics in Action
Technology Help
Brief Cases: Metal Production
Loyalty Program

14. Comparing Two Groups
Visa Global Organization
14.1 Comparing Two Means
14.2 The Two-Sample t-Test
14.3 Assumptions and Conditions
14.4 A Confidence Interval for the Difference Between Two Means
14.5 The Pooled t-Test
14.6 Tukey's Quick Test
14.7 Paired Data
14.8 The Paired t-Test
Ethics in Action
Technology Help: Two-Sample Methods
Brief Cases: Real Estate
Consumer Spending Patterns (Data Analysis)

15. Inference for Counts: Chi-Square Tests
SAC Capital
15.1 Goodness-of-Fit Tests
15.2 Interpreting Chi-Square Values
15.3 Examining the Residuals
15.4 The Chi-Square Test of Homogeneity
15.5 Comparing Two Proportions
15.6 Chi-Square Test of Independence
Ethics in Action
Technology Help: Chi-Square
Brief Cases: Health Insurance
Loyalty Program
Case Study


16. Inference for Regression
Nambé Mills
16.1 The Population and the Sample
16.2 Assumptions and Conditions
16.3 The Standard Error of the Slope
16.4 A Test for the Regression Slope
16.5 A Hypothesis Test for Correlation
16.6 Standard Errors for Predicted Values
16.7 Using Confidence and Prediction Intervals
Ethics in Action
Technology Help: Regression Analysis
Brief Cases: Frozen Pizza
Global Warming?

17. Understanding Residuals
17.1 Examining Residuals for Groups
17.2 Extrapolation and Prediction
17.3 Unusual and Extraordinary Observations
17.4 Working with Summary Values
17.5 Autocorrelation
17.6 Transforming (Re-expressing) Data
17.7 The Ladder of Powers
Ethics in Action
Technology Help
Brief Cases: Gross Domestic Product
Energy Sources

18. Multiple Regression
18.1 The Multiple Regression Model
18.2 Interpreting Multiple Regression Coefficients
18.3 Assumptions and Conditions for the Multiple Regression Model
18.4 Testing the Multiple Regression Model
18.5 Adjusted R2, and the F-statistic
18.6 The Logistic Regression Model
Ethics in Action
Technology Help: Regression Analysis
Brief Case: Golf Success

19. Building Multiple Regression Models
Bolliger and Mabillard
19.1 Indicator (or Dummy) Variables
19.2 Adjusting for Different Slopes-Interaction
19.3 Multiple Regression Diagnostics
19.4 Building Regression Models
19.5 Collinearity
19.6 Quadratic
Ethics in Action
Technology Help: Regression Analysis on the Computer
Brief Cases: Paralyzed Veterans of America

20. Time Series Analysis
Whole Foods Market®
20.1 What is a Time-Series?
20.2 Components of a Time Series
20.3 Smoothing Methods
20.4 Summarizing Forecast Error
20.5 Autoregressive Models
20.6 Multiple Regression-based Models
20.7 Choosing a Time Series Forecasting Method
20.8 Interpreting Time Series Models: The Whole Foods Data Revisited
Ethics in Action
Technology Help
Brief Cases: Intel Corporation
Tiffany & Co.
Case Study: title to come


21. Design and Analysis of Experiments and Observational Studies
Capital One
21.1 Observational Studies
21.2 Randomized, Comparative Experiments
21.3 The Four Principles of Experimental Design
21.4 Experimental Designs
21.5 Issues in Experimental Design
21.6 Analyzing a Completely Randomized Design in One Factor-The One-Way Analysis of Variance
21.7 Assumptions and Conditions for ANOVA
*21.8 Multiple Comparisons
21.9 ANOVA on Observational Data
21.10 Analysis of Multi Factor Designs
Ethics in Action
Technology Help
Brief Cases: A Multifactor Experiment

22. Quality Control
22.1 A Short History of Quality Control
22.2 Control Charts for Individual Observations (Run Charts)
22.3 Control Charts for Measurements: X and R Charts
22.4 Actions for Out of Control Processes
22.5 Control Charts for Attributes: p Charts and c Charts
22.6 Philosophies of Quality Control
Ethics in Action
Technology Help: Quality Control Charts
Brief Cases

23. Nonparametric Methods
23.1 Ranks
23.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic
23.3 Kruskal-Wallace Test
23.4 Paired Data: The Wilcoxon Signed-Rank Test
*23.5 Friedman Test for a Randomized Block Design
23.6 Kendall's Tau: Measuring Monotonicity
23.7 Spearman's Rho
23.8 When Should You Use Nonparametric Methods?
Ethics in Action
Brief Cases: Real Estate Reconsidered

24. Decision Making and Risk
Data Description, Inc.
24.1 Actions, States of Nature, and Outcomes
24.2 Payoff Tables and Decision Trees
24.3 Minimizing Loss and Maximizing Gain
24.4 The Expected Value of an Action
24.5 Expected Value with Perfect Information
24.6 Decisions Made with Sample Information
24.7 Estimating Variation
24.8 Sensitivity
24.9 Simulation
24.10 Probability Trees
24.11 Reversing the Conditioning: Bayes's Rule
24.12 More Complex Decisions
Ethics in Action
Brief Cases: Texaco-Pennzoil
Insurance Services, Revisited

25. Introduction to Data Mining
Paralyzed Veterans of America
25.1 Direct Marketing
25.2 The Data
25.3 The Goals of Data Mining
25.4 Data Mining Myths
25.5 Successful Data Mining
25.6 Data Mining Problems
25.7 Data Mining Algorithms
25.8 The Data Mining Process
25.9 Summary
Ethics in Action
Case Study

A. Answers
B. XLStat
C. Photo Acknowledgments
D. Tables and Selected Formulas
E. Index

Autor Norean Sharpe, Richard De Veaux, Paul Velleman
ISBN 9780321728111
EAN 0321728114
Godina izdanja 2011
Izdavač Pearson Education
Broj stranica 1008
Format i dimenzije 217 x 275mm, mek povez