MARC details
| 000 -LEADER |
|---|
| fixed length control field |
12983nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20210127122035.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
210116b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780321925831 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
VITAP |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23rd |
| Classification number |
519.5 SHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
10460 |
| Personal name |
Sharpe, Norean R. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Business Statistics / |
| Statement of responsibility, etc. |
Norean R. Sharpe, Richard D. De Veaux and Paul F. Velleman |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Boston, MA, USA |
| Name of publisher, distributor, etc. |
Pearson Education Inc. |
| Date of publication, distribution, etc. |
2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxvii, 846p. : ill. ; A-1 to A-75; I-1 to I-17; 28cm |
| 500 ## - GENERAL NOTE |
|---|
| General note |
It includes appendixes and index pages |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Business Statistics, Third Edition, by Sharpe, De Veaux, and Velleman, narrows the gap between theory and practice—relevant statistical methods empower business students to make effective, data-informed decisions. With their unique blend of teaching, consulting, and entrepreneurial experiences, this dynamic author team brings a modern edge to teaching statistics to business students. Focusing on statistics in the context of real business issues, with an emphasis on analysis and understanding over computation, the text helps students be analytical, prepares them to make better business decisions, and shows them how to effectively communicate results. <br/><br/>Table of Contents:<br/><br/>Preface<br/><br/>Index of Applications<br/><br/>1. Data and Decisions (E-Commerce)<br/><br/>1.1 Data and Decisions<br/><br/>1.2 Variable Types<br/><br/>1.3 Data Sources: Where, How, and When<br/><br/> Ethics in Action<br/><br/> Technology Help: Data on the Computer<br/><br/> Brief Case: Credit Card Bank<br/><br/>2. Displaying and Describing Categorical Data (Keen, Inc.)<br/><br/>2.1 Summarizing a Categorical Variable<br/><br/>2.2 Displaying a Categorical Variable <br/><br/>2.3 Exploring Two Categorical Variables: Contingency Tables <br/><br/>2.4 Segmented Bar Charts and Mosaic Plots <br/><br/>2.5 Simpson's Paradox<br/><br/> Ethics in Action<br/><br/> Technology Help: Displaying Categorical Data on the Computer<br/><br/> Brief Case: Credit Card Bank<br/><br/>3. Displaying and Describing Quantitative Data (AIG)<br/><br/>3.1 Displaying Quantitative Variables <br/><br/>3.2 Shape <br/><br/>3.3 Center <br/><br/>3.4 Spread of the Distribution<br/><br/>3.5 Shape, Center, and Spread–A Summary<br/><br/>3.6 Standardizing Variables <br/><br/>3.7 Five-Number Summary and Boxplots <br/><br/>3.8 Comparing Groups,<br/><br/>3.9 Identifying Outliers,<br/><br/>3.10 Time Series Plots<br/><br/>3.11 Transforming Skewed Data<br/><br/> Ethics in Action<br/><br/> Technology Help: Displaying and Summarizing Quantitative Variables<br/><br/> Brief Cases: Detecting the Housing Bubble and Socio-Economic Data on States<br/><br/>4. Correlation and Linear Regression (Amazon.com)<br/><br/>4.1 Looking at Scatterplots<br/><br/>4.2 Assigning Roles to Variables in Scatterplots<br/><br/>4.3 Understanding Correlation<br/><br/>4.4 Lurking Variables and Causation<br/><br/>4.5 The Linear Model<br/><br/>4.6 Correlation and the Line<br/><br/>4.7 Regression to the Mean<br/><br/>4.8 Checking the Model<br/><br/>4.9 Variation in the Model and R2<br/><br/>4.10 Reality Check: Is the Regression Reasonable?<br/><br/>4.11 Nonlinear Relationships<br/><br/> Ethics in Action<br/><br/> Technology Help: Correlation and Regression<br/><br/> Brief Cases: Fuel Efficiency, Cost of Living, and Mutual Funds<br/><br/> Case Study I: Paralyzed Veterans of America<br/><br/>5. Randomness and Probability (Credit Reports and the Fair Isaacs Corporation)<br/><br/>5.1 Random Phenomena and Probability<br/><br/>5.2 The Nonexistent Law of Averages<br/><br/>5.3 Different Types of Probability<br/><br/>5.4 Probability Rules<br/><br/>5.5 Joint Probability and Contingency Tables<br/><br/>5.6 Conditional Probability<br/><br/>5.7 Constructing Contingency Tables<br/><br/>5.8 Probability Trees<br/><br/>5.9 Reversing the Conditioning: Bayes’ Rule<br/><br/> Ethics in Action<br/><br/> Technology Help: Generating Random Numbers<br/><br/> Brief Case<br/><br/>6. Random Variables and Probability Models (Metropolitan Life Insurance Company)<br/><br/>6.1 Expected Value of a Random Variable<br/><br/>6.2 Standard Deviation of a Random Variable<br/><br/>6.3 Properties of Expected Values and Variances<br/><br/>6.4 Bernoulli Trials<br/><br/>6.5 Discrete Probability Models<br/><br/> Ethics in Action<br/><br/> Technology Help: Random Variables and Probability Models<br/><br/> Brief Case: Investment Options<br/><br/>7. The Normal and other Continuous Distributions (The NYSE)<br/><br/>7.1 The Standard Deviation as a Ruler<br/><br/>7.2 The Normal Distribution<br/><br/>7.3 Normal Probability Plots<br/><br/>7.4 The Distribution of Sums of Normals<br/><br/>7.5 The Normal Approximation for the Binomial<br/><br/>7.6 The Other Continuous Random Variables<br/><br/> Ethics in Action<br/><br/> Technology Help: Probability Calculations and Plots<br/><br/> Brief Case<br/><br/>8. Surveys and Sampling (Roper Polls)<br/><br/>8.1 Three Ideas of Sampling<br/><br/>8.2 Populations and Parameters<br/><br/>8.3 Common Sampling Designs<br/><br/>8.4 The Valid Survey<br/><br/>8.5 How to Sample Badly<br/><br/> Ethics in Action<br/><br/> Technology Help: Random Sampling<br/><br/> Brief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey<br/><br/>9. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)<br/><br/>9.1 The Distribution of Sample Proportions<br/><br/>9.2 A Confidence Interval<br/><br/>9.3 Margin of Error: Certainty vs. Precision<br/><br/>9.4 Choosing and Sample Size<br/><br/> Ethics in Action<br/><br/> Technology Help: Confidence Intervals for Proportions<br/><br/> Brief Case: Real Estate Simulation<br/><br/>Case Study II<br/><br/>10. Testing Hypotheses about Proportions (Dow Jones Industrial Average)<br/><br/>10.1 Hypotheses<br/><br/>10.2 A Trial as a Hypothesis Test<br/><br/>10.3 P-Values<br/><br/>10.4 The Reasoning of Hypothesis Testing<br/><br/>10.5 Alternative Hypotheses<br/><br/>10.6 p-Values and Decisions: What to Tell About a Hypothesis Test<br/><br/> Ethics in Action<br/><br/> Technology Help: Hypothesis Tests<br/><br/> Brief Cases: Metal Production and Loyalty Program<br/><br/>11. Confidence Intervals and Hypothesis Tests for Means (Guinness & Co.)<br/><br/>11.1 The Central Limit Theorem<br/><br/>11.2 The Sampling Distribution of the Mean<br/><br/>11.3 How Sampling Distribution Models Work<br/><br/>11.4 Gossett and the tͼ/i>-Distribution<br/><br/>11.5 A Confidence Interval for Means<br/><br/>11.6 Assumptions and Conditions<br/><br/>11.7 Testing Hypothesis about Means–the One-Sample t-Test<br/><br/> Ethics in Action<br/><br/> Technology Help: Inference for Means<br/><br/> Brief Cases: Real Estate and Donor Profiles<br/><br/>12. More About Tests and Intervals (Traveler’s Insurance)<br/><br/>12.1 How to Think About P-Values<br/><br/>12.2 Alpha Levels and Significance<br/><br/>12.3 Critical Values<br/><br/>12.4 Confidence Intervals and Hypothesis Tests<br/><br/>12.5 Two Types of Errors<br/><br/>12.6 Power<br/><br/> Ethics in Action<br/><br/> Technology Help: Hypothesis Tests<br/><br/> Brief Case<br/><br/>13. Comparing Two Means (Visa Global Organization)<br/><br/>13.1 Comparing Two Means<br/><br/>13.2 The Two-Sample t-Test<br/><br/>13.3 Assumptions and Conditions<br/><br/>13.4 A Confidence Interval for the Difference Between Two Means<br/><br/>13.5 The Pooled t-Test<br/><br/>13.6 Paired Data<br/><br/>13.7 Paired Methods<br/><br/>Ethics in Action<br/><br/> Technology Help: Two-Sample Methods<br/><br/> Technology Help: Paired t<br/><br/> Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis)<br/><br/>14. Inference for Counts: Chi-Square Tests (SAC Capital)<br/><br/>14.1 Goodness-of-Fit Tests<br/><br/>14.2 Interpreting Chi-Square Values<br/><br/>14.3 Examining the Residuals<br/><br/>14.4 The Chi-Square Test of Homogeneity<br/><br/>14.5 Comparing Two Proportions<br/><br/>14.6 Chi-Square Test of Independence<br/><br/> Ethics in Action<br/><br/> Technology Help: Chi-Square<br/><br/> Brief Cases: Health Insurance and Loyalty Program<br/><br/> <br/><br/> Case Study III: Investment Strategy Segmentation<br/><br/>15. Inference for Regression (Nambé Mills)<br/><br/>15.1 A Hypothesis Test and Confidence Interval for the Slope<br/><br/>15.2 Assumptions and Conditions<br/><br/>15.3 Standard Errors for Predicted Values<br/><br/>15.4 Using Confidence and Prediction Intervals<br/><br/> Ethics in Action<br/><br/> Technology Help: Regression Analysis<br/><br/> Brief Cases: Frozen Pizza and Global Warming?<br/><br/>16. Understanding Residuals (Kellogg’s)<br/><br/>16.1 Examining Residuals for Groups<br/><br/>16.2 Extrapolation and Prediction<br/><br/>16.3 Unusual and Extraordinary Observations<br/><br/>16.4 Working with Summary Values<br/><br/>16.5 Autocorrelation<br/><br/>16.6 Transforming (Re-expressing) Data<br/><br/>16.7 The Ladder of Powers<br/><br/> Ethics in Action<br/><br/> Technology Help: Examining Residuals<br/><br/> Brief Cases: Gross Domestic Product and Energy Sources<br/><br/>17. Multiple Regression (Zillow.com)<br/><br/>17.1 The Multiple Regression Model<br/><br/>17.2 Interpreting Multiple Regression Coefficients<br/><br/>17.3 Assumptions and Conditions for the Multiple Regression Model<br/><br/>17.4 Testing the Multiple Regression Model<br/><br/>17.5 Adjusted R2 and the F-statistic<br/><br/>17.6 The Logistic Regression Model<br/><br/> Ethics in Action<br/><br/> Technology Help: Regression Analysis<br/><br/> Brief Case: Golf Success<br/><br/>18. Building Multiple Regression Models (Bolliger and Mabillard)<br/><br/>18.1 Indicator (or Dummy) Variables<br/><br/>18.2 Adjusting for Different Slopes–Interaction Terms<br/><br/>18.3 Multiple Regression Diagnostics<br/><br/>18.4 Building Regression Models<br/><br/>18.5 Collinearity<br/><br/>18.6 Quadratic Terms<br/><br/> Ethics in Action<br/><br/> Technology Help: Building Multiple Regression Models<br/><br/> Brief Case<br/><br/>19. Time Series Analysis (Whole Food Market)<br/><br/>19.1 What Is a Time Series?<br/><br/>19.2 Components of a Time Series<br/><br/>19.3 Smoothing Methods<br/><br/>19.4 Summarizing Forecast Error<br/><br/>19.5 Autoregressive Models<br/><br/>19.6 Multiples Regression-based Models<br/><br/>19.7 Choosing a Time Series Forecasting Method<br/><br/>19.8 Interpreting Time Series Models: The Whole Foods Data Revisited<br/><br/> Ethics in Action<br/><br/> Technology Help<br/><br/> Brief Cases: Intel Corporation and Tiffany & Co.<br/><br/> Case Study IV: Health Care Costs<br/> <br/>20. Design and Analysis of Experiments and Observational Studies (Capital One)<br/><br/>20.1 Observational Studies<br/><br/>20.2 Randomized Comparative Experiments<br/><br/>20.3 The Four Principles of Experimental Design<br/><br/>20.4 Experimental Designs<br/><br/>20.5 Issues in Experimental Design<br/><br/>20.6 Analyzing a Design in One Factor–The One-Way Analysis of Variance<br/><br/>20.7 Assumptions and Conditions for ANOVA<br/><br/>20.8 Multiple Comparisons<br/><br/>20.9 ANOVA on Observational Data<br/><br/>20.10 Analysis of Multifactor Designs<br/><br/> Ethics in Action<br/><br/> Technology Help: Analysis of Variance<br/><br/> Brief Case: Multifactor Experiment Design<br/><br/>21. Quality Control (Sony)<br/><br/>21.1 A Short History of Quality Control<br/><br/>21.2 Control Charts for Individual Observations (Run Charts)<br/><br/>21.3 Control Charts for Measurements: (x-bar) and R Charts<br/><br/>21.4 Actions for Out-of-Control Processes<br/><br/>21.5 Control Charts for Attributes: p Charts and c Charts<br/><br/>21.6 Philosophies of Quality Control<br/><br/> Ethics in Action<br/><br/> Technology Help: Quality Control Charts<br/><br/> Brief Case: Laptop Touchpad Quality<br/><br/>22. Nonparametric Methods (i4cp)<br/><br/>22.1 Ranks<br/><br/>22.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic<br/><br/>22.3 Kruskal-Wallace Test<br/><br/>22.4 Paired Data: The Wilcoxon Signed-Rank Test<br/><br/>22.5 Friedman Test for a Randomized Block Design<br/><br/>22.6 Kendall’s Tau: Measuring Monotonicity<br/><br/>22.7 Spearman’s Rho<br/><br/>22.8 When Should You Use Nonparametric Methods?<br/><br/> Ethics in Action<br/><br/> Technology Help<br/><br/> Brief Case: Real Estate Reconsidered<br/><br/>23. Decision Making and Risk (Data Description, Inc.)<br/><br/>23.1 Actions, States of Nature, and Outcomes<br/><br/>23.2 Payoff Tables and Decisions Trees<br/><br/>23.3 Minimizing Loss and Maximizing Gain<br/><br/>23.4 The Expected Value of an Action<br/><br/>23.5 Expected Value with Perfect Information<br/><br/>23.6 Decisions Made with Sample Information<br/><br/>23.7 Estimating Variation<br/><br/>23.8 Sensitivity<br/><br/>23.9 Simulation<br/><br/>23.10 More Complex Decisions<br/><br/> Ethics in Action<br/><br/> Technology Help<br/><br/> Brief Cases: Texaco-Pennzoil and Insurance Services, Revisited<br/> <br/>24. Introduction to Data Mining (Paralyzed Veterans of America)<br/><br/>24.1 The Big Data Revolution<br/><br/>24.2 Direct Marketing<br/><br/>24.3 The Goals of Data Mining<br/><br/>24.4 Data Mining Myths<br/><br/>24.5 Successful Data Mining<br/><br/>24.6 Data Mining Problems<br/><br/>24.7 Data Mining Algorithms<br/><br/>24.8 The Data Mining Process<br/><br/>24.9 Summary<br/><br/> Ethics in Action<br/><br/> Case Study V Marketing Experiment<br/><br/>Appendices<br/> <br/>A. Answers<br/><br/>B. Photo Acknowledgments<br/><br/>C. Tables and Selected Formulas<br/><br/>Index |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
10533 |
| Topical term or geographic name entry element |
Commercial Statistics; |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
10534 |
| Personal name |
De Veaux, Richard D. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
10535 |
| Personal name |
Velleman, Paul F. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://www.pearson.com/store/p/business-statistics-student-value-edition/P100001802031?tab=overview">https://www.pearson.com/store/p/business-statistics-student-value-edition/P100001802031?tab=overview</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Reference Book |
| Edition |
3rd ed. |
| Classification part |
519.5 |
| Call number suffix |
SHA |
