Statistics for business and economics

著者

• McClave, James T.
• Benson, P. George
• Sincich, Terry

書誌事項

Statistics for business and economics

James T. McClave, P. George Benson, Terry Sincich,

Pearson, c2014

12th ed., International ed.

• : pbk

注記

Includes bibliographical references and index

内容説明・目次

内容説明

Statistics for Business and Economics, Twelfth Edition, meets today's business students with a balance of clarity and rigor, and applications incorporated from a diverse range of industries. This classic text covers a wide variety of data collection and analysis techniques with these goals in mind: developing statistical thinking, learning to assess the credibility and value of inferences made from data, and making informed business decisions. The Twelfth Edition has been updated with real, current data in many of the exercises, examples, and applications. Exercises draw on actual business situations and recent economic events so that students can test their knowledge throughout the course. Statistics in Action case studies open each chapter with a recent, controversial, or high-profile business issue, motivating students to critically evaluate the findings and think through the statistical issues involved. A continued emphasis on ethics highlights the importance of ethical behavior in collecting, interpreting, and reporting on data.

目次

• 1. Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications in Business 1.3 Fundamental Elements of Statistics 1.4 Processes (Optional) 1.5 Types of Data 1.6 Collecting Data: Sampling and Related Issues 1.7 Critical Thinking with Statistics Statistics in Action: A 20/20 View of Surveys: Fact or Fiction? Activity 1.1: Keep the Change: Collecting Data Activity 2.2: Identifying Misleading Statistics Using Technology: Accessing and Listing Data
• Random Sampling 2. Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Numerical Measures of Central Tendency 2.4 Numerical Measures of Variability 2.5 Using the Mean and Standard Deviation to Describe Data 2.6 Numerical Measures of Relative Standing 2.7 Methods for Detecting Outliers: Box Plots and z-Scores 2.8 Graphing Bivariate Relationships (Optional) 2.9 The Time Series Plot (Optional) 2.10 Distorting the Truth with Descriptive Techniques Statistics in Action: Can Money Buy Love? Activity 2.1: Real Estate Sales Activity 2.2: Keep the Change: Measures of Central Tendency and Variability Using Technology: Describing Data Making Business Decisions: The Kentucky Milk CasePart 1 (Covers Chapters 1 and 2) 3. Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Bayes's Rule Statistics in Action: Lotto Buster! Activity 3.1: Exit Polls: Conditional Probability Activity 3.2: Keep the Change: Independent Events Using Technology: Combinations and Permutations 4. Random Variables and Probability Distributions 4.1 Two Types of Random Variables PART I: Discrete Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 The Binomial Distribution 4.4 Other Discrete Distributions: Poisson and Hypergeometric PART II: Continuous Random Variables 4.5 Probability Distributions for Continuous Random Variables 4.6 The Normal Distribution 4.7 Descriptive Methods for Assessing Normality 4.8 Other Continuous Distributions: Uniform and Exponential Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold? Activity 4.1: Warehouse Club Memberships: Exploring a Binomial Random Variable Activity 4.2: Identifying the Type of Probability Distribution Using Technology: Discrete Probabilities, Continuous Probabilities, and Normal Probability Plots 5. Sampling Distributions 5.1 The Concept of a Sampling Distribution 5.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance 5.3 The Sampling Distribution of the Sample Mean and the Central Limit Theorem 5.4 The Sampling Distribution of the Sample Proportion Statistics in Action: The Insomnia Pill: Is It Effective? Activity 5.1: Simulating a Sampling DistributionCell Phone Usage Using Technology: Simulating a Sampling Distribution Making Business Decisions: The Furniture Fire Case (Covers Chapters 3-5) 6. Inferences Based on a Single Sample: Estimation with Confidence Intervals 6.1 Identifying and Estimating the Target Parameter 6.2 Confidence Interval for a Population Mean: Normal (z) Statistic 6.3 Confidence Interval for a Population Mean: Student's t-Statistic 6.4 Large-Sample Confidence Interval for a Population Proportion 6.5 Determining the Sample Size 6.6 Finite Population Correction for Simple Random Sampling (Optional) 6.7 Confidence Interval for a Population Variance (Optional) Inferences Based on a Single Sample: Estimation with Confidence Intervals Statistics in Action: Medicare Fraud Investigations Activity 6.1: Conducting a Pilot Study Using Technology: Confidence Intervals 7. Inferences Based on a Single Sample: Tests of Hypotheses 7.1 The Elements of a Test of Hypothesis 7.2 Formulating Hypotheses and Setting Up the Rejection Region 7.3 Observed Significance Levels: p-Values 7.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic 7.5 Test of Hypothesis about a Population Mean: Student's t-Statistic 7.6 Large-Sample Test of Hypothesis about a Population Proportion 7.7 Test of Hypothesis about a Population Variance 7.8 Calculating Type II Error Probabilities: More about b (Optional) Statistics in Action: Diary of a Kleenex (R) User-How Many Tissues in a Box? Activity 7.1: Challenging a Company's Claim: Tests of Hypotheses Activity 7.2: Keep the Change: Tests of Hypotheses Using Technology: Tests of Hypotheses 8. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 8.1 Identifying the Target Parameter 8.2 Comparing Two Population Means: Independent Sampling 8.3 Comparing Two Population Means: Paired Difference Experiments 8.4 Comparing Two Population Proportions: Independent Sampling 8.5 Determining the Required Sample Size 8.6 Comparing Two Population Variances: Independent Sampling Statistics in Action: ZixIt Corp. v. Visa USA Inc.-A Libel Case Activity 8.1: Box Office Receipts: Comparing Population Means Activity 8.2: Keep the Change: Inferences Based on Two Samples Using Technology: Two-Sample Inferences Making Business Decisions: The Kentucky Milk Case-Part II (Covers Chapters 6-8) 9. Design of Experiments and Analysis of Variance 9.1 Elements of a Designed Experiment 9.2 The Completely Randomized Design: Single Factor 9.3 Multiple Comparisons of Means 9.4 The Randomized Block Design 9.5 Factorial Experiments: Two Factors Statistics in Action: Pollutants at a Housing Development-A Case of Mishandling Small Samples Activity 9.1: Designed vs. Observational Experiments Using Technology: Analysis of Variance 10. Categorical Data Analysis 10.1 Categorical Data and the Multinomial Experiment 10.2 Testing Category Probabilities: One-Way Table 10.3 Testing Category Probabilities: Two-Way (Contingency) Table 10.4 A Word of Caution about Chi-Square Tests Statistics in Action: The Case of the Ghoulish Transplant Tissue-Who Is Responsible for Paying Damages? Activity 10.1: Binomial vs. Multinomial Experiments Activity 10.2: Contingency Tables Using Technology: Chi-Square Analyses Making Business Decisions: Discrimination in the Workplace (Covers Chapters 9 and 10) 11. Simple Linear Regression 11.1 Probabilistic Models 11.2 Fitting the Model: The Least Squares Approach 11.3 Model Assumptions 11.4 Assessing the Utility of the Model: Making Inferences about the Slope b1 11.5 The Coefficients of Correlation and Determination 11.6 Using the Model for Estimation and Prediction 11.7 A Complete Example Statistics in Action: Legal Advertising-Does It Pay? Activity 11.1: Apply Simple Linear Regression to Your Favorite Data Using Technology: Simple Linear Regression 12. Multiple Regression and Model Building 12.1 Multiple Regression Models PART I: First-Order Models with Quantitative Independent Variables 12.2 Estimating and Making Inferences about the b Parameters 12.3 Evaluating Overall Model Utility 12.4 Using the Model for Estimation and Prediction PART II: Model Building in Multiple Regression 12.5 Interaction Models 12.6 Quadratic and Other Higher-Order Models 12.7 Qualitative (Dummy) Variable Models 12.8 Models with Both Quantitative and Qualitative Variables 12.9 Comparing Nested Models 12.10 Stepwise Regression PART III: Multiple Regression Diagnostics 12.11 Residual Analysis: Checking the Regression Assumptions 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Statistics in Action: Bid Rigging in the Highway Construction Industry Activity 12.1: Insurance Premiums: Collecting Data for Several Variables Activity 12.2: Collecting Data and Fitting a Multiple Regression Model Using Technology: Multiple Regression Making Business Decisions: The Condo Sales Case (Covers Chapters 11 and 12) 13. Methods for Quality Improvement: Statistical Process Control (Available on CD) 13.1 Quality, Processes, and Systems 13.2 Statistical Control 13.3 The Logic of Control Charts 13.4 A Control Chart for Monitoring the Mean of a Process: The [x-bar]-Chart 13.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart 13.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart 13.7 Diagnosing the Causes of Variation 13.8 Capability Analysis Statistics in Action: Testing Jet Fuel Additive for Safety Activity 13.1: Quality Control: Consistency Using Technology: Control Charts MAKING BUSINESS DECISIONS: The Gasket Manufacturing Case (Covers Chapter 13) 14. Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD) 14.1 Descriptive Analysis: Index Numbers 14.2 Descriptive Analysis: Exponential Smoothing 14.3 Time Series Components 14.4 Forecasting: Exponential Smoothing 14.5 Forecasting Trends: Holt's Method 14.6 Measuring Forecast Accuracy: MAD and RMSE 14.7 Forecasting Trends: Simple Linear Regression 14.8 Seasonal Regression Models 14.9 Autocorrelation and the Durbin-Watson Test Statistics in Action: Forecasting the Monthly Sales of a New Cold Medicine Activity 14.1: Time Series Using Technology: Forecasting 15. Nonparametric Statistics (Available on CD) 15.1 Introduction: Distribution-Free Tests 15.2 Single Population Inferences 15.3 Comparing Two Populations: Independent Samples 15.4 Comparing Two Populations: Paired Difference Experiment 15.5 Comparing Three or More Populations: Completely Randomized Design 15.6 Comparing Three or More Populations: Randomized Block Design 15.7 Rank Correlation Statistics in Action: How Vulnerable Are New Hampshire Wells to Groundwater Contamination? Activity 15.1: Keep the Change: Nonparametric Statistics Using Technology: Nonparametric Tests Making Business Decisions: Detecting "Sales Chasing" (Covers Chapters 10 and 15) Appendix A: Summation Notation Appendix B: Basic Counting Rules Appendix C: Calculation Formulas for Analysis of Variance C.1 Formulas for the Calculations in the Completely Randomized Design C.2 Formulas for the Calculations in the Randomized Block Design C.3 Formulas for the Calculations for a Two-Factor Factorial Experiment C.4 Tukey's Multiple Comparisons Procedure (Equal Sample Sizes) C.5 Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons) C.6 Scheffe's Multiple Comparisons Procedure (Pairwise Comparisons) Appendix D: Tables Table I. Binomial Probabilities Table II. Normal Curve Areas Table III. Critical Values of t Table IV. Critical Values of x2 Table V. Percentage Points of the F-Distribution, = .10 Table VI. Percentage Points of the F-Distribution, = .05 Table VII. Percentage Points of the F-Distribution, = .025 Table VIII. Percentage Points of the F-Distribution, = .01 Table IX. Control Chart Constants Table X. Critical Values for the Durbin-Watson d-Statistic, = .05 Table XI. Critical Values for the Durbin-Watson d-Statistic, = .01 Table XII. Critical Values of TL and Tu for the Wilcoxon Rank Sum Test: Independent Samples Table XIII. Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test Table XIV. Critical Values of Spearman's Rank Correlation Coefficient Table XV. Critical Values of the Studentized Range, = .05 Answers to Selected Exercises Index Credits

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詳細情報

• NII書誌ID(NCID): BB15521165
• ISBN:
  • 9780321866820
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boston
• ページ数/冊数: xix, 815, 10, 8, 2 p.
• 大きさ: 28 cm
• 付属資料: 1 CD-ROM
• 分類:
  • DC23 : 519.5
• 件名:
  • LCSH : Commercial statistics
  • LCSH : Economics -- Statistical methods
  • LCSH : Statistics