Business Statistics, Global Edition

Höfundar: Norean R. Sharpe; Norean D. Sharpe; Richard D. De Veaux; Paul F. Velleman (Útgáfa: 4)
Business Statistics, Global Edition

Kaup valmöguleikar

Business Statistics narrows the gap between theory and practice by focusing on relevant statistical methods, thus empowering business students to make good, data-driven decisions. Using the latest GAISE (Guidelines for Assessment and Instruction in Statistics Education) report, which included extensive revisions to reflect both the evolution of technology and new wisdom on statistics education, this edition brings a modern edge to teaching business statistics.

This includes a focus on the report’s key recommendations: teaching statistical thinking, focusing on conceptual understanding, integrating real data with a context and a purpose, fostering active learning, using technology to explore concepts and analyse data, and using assessments to improve and evaluate student learning. By presenting statistics in the context of real-world businesses and by emphasising analysis and understanding over computation, this book helps students be more analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.

The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook.

Nánar um bókina

Útgefandi
Pearson International Content
ISBN
9781292269375
Print ISBN
9781292269313
Format
Page Fidelity (PDF)
Útgáfa
4
Höfundar
Norean R. Sharpe; Norean D. Sharpe; Richard D. De Veaux; Paul F. Velleman
Tungumál
English
Útgefið
2020-11-03
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Title Page
  • Copyright
  • Dedication
  • Meet the Authors
  • Contents
  • Preface
  • Index of Applications
  • Part I: Exploring and Collecting Data
  • Chapter 1: Data and Decisions (H&M)
  • 1.1 Data
  • 1.2 The Role of Data in Decision Making
  • 1.3 Variable Types
  • 1.4 Data Sources: Where, How, and When
  • Ethics in Action
  • Chapter 1: From Learning to Earning
  • Tech Support: Entering Data
  • Brief Case: Credit Card Bank
  • Chapter 2: Visualizing and Describing Categorical Data (Dalia Research)
  • 2.1 Summarizing a Categorical Variable
  • 2.2 Displaying a Categorical Variable
  • 2.3 Exploring Relationships Between Two Categorical Variables: Contingency Tables
  • 2.4 Segmented Bar Charts and Mosaic Plots
  • 2.5 Three Categorical Variables
  • 2.6 Simpson’s Paradox
  • Ethics in Action
  • Chapter 2: From Learning to Earning
  • Tech Support: Displaying Categorical Data
  • Brief Case: Credit Card Bank
  • Chapter 3: Describing, Displaying, and Visualizing Quantitative Data (AIG)
  • 3.1 Visualizing Quantitative Variables
  • 3.2 Shape
  • 3.3 Center
  • 3.4 Spread of the Distribution
  • 3.5 Shape, Center, and Spread—A Summary
  • 3.6 Standardizing Variables
  • 3.7 Five-Number Summary and Boxplots
  • 3.8 Comparing Groups
  • 3.9 Identifying Outliers
  • 3.10 Time Series Plots
  • *3.11 Transforming Skewed Data
  • Ethics in Action
  • Chapter 3: From Learning to Earning
  • Tech Support: Displaying and Summarizing Quantitative Variables
  • Brief Case: Detecting the Housing Bubble
  • Chapter 4: Correlation and Linear Regression (Zillow.com)
  • 4.1 Looking at Scatterplots
  • 4.2 Assigning Roles to Variables in Scatterplots
  • 4.3 Understanding Correlation
  • 4.4 Lurking Variables and Causation
  • 4.5 The Linear Model
  • 4.6 Correlation and the Line
  • 4.7 Regression to the Mean
  • 4.8 Checking the Model
  • 4.9 Variation in the Model and R2
  • 4.10 Reality Check: Is the Regression Reasonable?
  • 4.11 Nonlinear Relationships
  • *4.12 Multiple Regression—A Glimpse Ahead
  • Ethics in Action
  • Chapter 4: From Learning to Earning
  • Tech Support: Correlation and Regression
  • Brief Case: Fuel Efficiency, Cost of Living, and Mutual Funds
  • Case Study: Paralyzed Veterans of America
  • Part II: Modeling with Probability
  • Chapter 5: Randomness and Probability (Credit Reports, the Fair Isaacs Corporation, and Equifax)
  • 5.1 Random Phenomena and Probability
  • 5.2 The Nonexistent Law of Averages
  • 5.3 Different Types of Probability
  • 5.4 Probability Rules
  • 5.5 Joint Probability and Contingency Tables
  • 5.6 Conditional Probability and the General Multiplication Rule
  • 5.7 Constructing Contingency Tables
  • 5.8 Probability Trees
  • *5.9 Reversing the Conditioning: Bayes’ Rule
  • Ethics in Action
  • Chapter 5: From Learning to Earning
  • Tech Support: Generating Random Numbers
  • Brief Case: Global Markets
  • Chapter 6: Random Variables and Probability Models (Metropolitan Life Insurance Company)
  • 6.1 Expected Value of a Random Variable
  • 6.2 Standard Deviation of a Random Variable
  • 6.3 Properties of Expected Values and Variances
  • 6.4 Bernoulli Trials
  • 6.5 Discrete Probability Models
  • Ethics in Action
  • Chapter 6: From Learning to Earning
  • Tech Support: Random Variables and Probability Models
  • Brief Case: Investment Options
  • Chapter 7: The Normal and Other Continuous Distributions (The NYSE)
  • 7.1 The Standard Deviation as a Ruler
  • 7.2 The Normal Distribution
  • 7.3 Normal Probability Plots
  • 7.4 The Distribution of Sums of Normals
  • 7.5 The Normal Approximation for the Binomial
  • 7.6 Other Continuous Random Variables
  • Ethics in Action
  • Chapter 7: From Learning to Earning
  • Tech Support: Probability Calculations and Plots
  • Brief Case: Price/Earnings and Stock Value
  • Part III: Gathering Data
  • Chapter 8: Data Sources: Observational Studies and Surveys (Roper Polls)
  • 8.1 Observational Studies and Found Data
  • 8.2 Sample Surveys
  • 8.3 Populations and Parameters
  • 8.4 Common Sampling Designs
  • 8.5 The Valid Survey
  • 8.6 How to Sample Badly
  • Ethics in Action
  • Chapter 8: From Learning to Earning
  • Tech Support
  • Brief Case: Market Survey Research and The GfK Roper Reports Worldwide Survey
  • Chapter 9: Data Sources: Experiments (Capital One)
  • 9.1 Randomized, Comparative Experiments
  • 9.2 The Four Principles of Experimental Design
  • 9.3 Experimental Designs
  • 9.4 Issues in Experimental Design
  • 9.5 Displaying Data from Designed Experiments
  • Ethics in Action
  • Chapter 9: From Learning to Earning
  • Brief Case: Design a Multifactor Experiment
  • Part IV: Inference for Decision Making
  • Chapter 10: Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards:
  • 10.1 The Distribution of Sample Proportions
  • 10.2 A Confidence Interval for a Proportion
  • 10.3 Margin of Error: Certainty vs. Precision
  • 10.4 Choosing the Sample Size
  • Ethics in Action
  • Chapter 10: From Learning to Earning
  • Tech Support: Confidence Intervals for Proportions
  • Brief Case: Has Gold Lost its Luster? and Forecasting Demand
  • Case Study: Real Estate Simulation
  • Chapter 11: Confidence Intervals for Means (Guinness & Co.)
  • 11.1 The Central Limit Theorem
  • 11.2 The Sampling Distribution of the Mean
  • 11.3 How Sampling Distribution Models Work
  • 11.4 Gosset and the t-Distribution
  • 11.5 A Confidence Interval for Means
  • 11.6 Assumptions and Conditions
  • 11.7 Visualizing Confidence Intervals for the Mean
  • Ethics in Action
  • Chapter 11: From Learning to Earning
  • Tech Support: Confidence Intervals for Means
  • Brief Case: Real Estate and Donor Profiles
  • Chapter 12: Testing Hypotheses (Casting Ingots)
  • 12.1 Hypotheses
  • 12.2 P-Values
  • 12.3 The Reasoning of Hypothesis Testing
  • 12.4 A Hypothesis Test for the Mean
  • 12.5 Intervals and Tests
  • 12.6 P-Values and Decisions: What to Tell About a Hypothesis Test
  • Ethics in Action
  • Chapter 12: From Learning to Earning
  • Tech Support: Hypothesis Tests
  • Brief Case: Real Estate and Donor Profiles
  • Chapter 13: More About Tests and Intervals (Traveler’s Insurance)
  • 13.1 How to Think About P-Values
  • 13.2 Alpha Levels and Significance
  • 13.3 Critical Values
  • 13.4 Confidence Intervals and Hypothesis Tests
  • 13.5 Two Types of Errors
  • 13.6 Power
  • Ethics in Action
  • Chapter 13: From Learning to Earning
  • Brief Case: Confidence Intervals and Hypothesis Tests
  • Chapter 14: Comparing Two Means (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 Paired Data
  • 14.7 Paired t-Methods
  • Ethics in Action
  • Chapter 14: From Learning to Earning
  • Tech Support: Comparing Two Groups
  • Brief Case: Real Estate and Consumer Spending Patterns (Data Analysis)
  • Chapter 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
  • Chapter 15: From Learning to Earning
  • Tech Support: Chi-Square
  • Brief Case: Health Insurance and Loyalty Program
  • Case Study: Investment Strategy Segmentation
  • Part V: Models for Decision Making
  • Chapter 16: Inference for Regression (Nambé Mills)
  • 16.1 A Hypothesis Test and Confidence Interval for the Slope
  • 16.2 Assumptions and Conditions
  • 16.3 Standard Errors for Predicted Values
  • 16.4 Using Confidence and Prediction Intervals
  • Ethics in Action
  • Chapter 16: From Learning to Earning
  • Tech Support: Regression Analysis
  • Brief Case: Frozen Pizza and Global Warming?
  • Chapter 17: Understanding Residuals (Kellogg’s)
  • 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
  • Chapter 17: From Learning to Earning
  • Tech Support: Examining Residuals
  • Brief Case: Gross Domestic Product and Energy Sources
  • Chapter 18: Multiple Regression (Zillow.com)
  • 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
  • Chapter 18: From Learning to Earning
  • Tech Support: Regression Analysis
  • Brief Case: Golf Success
  • Chapter 19: Building Multiple Regression Models (Bolliger and Mabillard)
  • 19.1 Indicator (or Dummy) Variables
  • 19.2 Adjusting for Different Slopes—Interaction Terms
  • 19.3 Multiple Regression Diagnostics
  • 19.4 Building Regression Models
  • 19.5 Collinearity
  • 19.6 Quadratic Terms
  • Ethics in Action
  • Chapter 19: From Learning to Earning
  • Tech Support: Building Multiple Regression Models
  • Brief Case: Building Models
  • Chapter 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
  • Chapter 20: From Learning to Earning
  • Tech Support: Time Series
  • Brief Case: U.S. Trade with the European Union
  • Case Study: Health Care Costs
  • Part VI: Analytics
  • Chapter 21: Introduction to Big Data and Data Mining (Paralyzed Veterans of America)
  • 21.1 Data Mining and the Big Data Revolution
  • 21.2 The Data Mining Process
  • 21.3 Data Mining Algorithms: A Sample
  • 21.4 Models Built from Combining Other Models
  • 21.5 Comparing Models
  • 21.6 Summary
  • Ethics in Action
  • Chapter 21: From Learning to Earning
  • Part VII: Online Topics
  • Chapter 22: Quality Control (Sony)
  • 22.1 A Short History of Quality Control
  • 22.2 Control Charts for Individual Observations (Run Charts)
  • 22.3 Control Charts for Measurements: x̅ 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
  • Chapter 22: From Learning to Earning
  • Tech Support: Quality Control Charts
  • Brief Case: Laptop Touchpad Quality
  • Chapter 23: Nonparametric Methods (i4cp)
  • 23.1 Ranks
  • 23.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic
  • 23.3 Kruskal-Wallis 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
  • Chapter 23: From Learning to Earning
  • Tech Support: Nonparametric Methods
  • Brief Case: Real Estate Reconsidered
  • Chapter 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 More Complex Decisions
  • Ethics in Action
  • Chapter 24: From Learning to Earning
  • Brief Case: Texaco-Pennzoil and Insurance Services, Revisited
  • Chapter 25: Analysis of Experiments and Observational Studies
  • 25.1 Analyzing a Design in One Factor—The One-Way Analysis of Variance
  • 25.2 Assumptions and Conditions for ANOVA
  • *25.3 Multiple Comparisons
  • 25.4 ANOVA on Observational Data
  • 25.5 Analysis of Multifactor Designs
  • Chapter 25: From Learning to Earning
  • Tech Support: Analysis of Variance
  • Brief Case: Analyze your Multifactor Experiment
  • Appendixes
  • Appendix A: Answers
  • Appendix B: Tables and Selected Formulas
  • Appendix C: Photo Acknowledgments
  • Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z