Book , Print in English

Statistics for business and economics

James T. McClave, P. George Benson, Terry Sincich.
  • Boston : Pearson, ©2014.
  • 12th ed.
  • xix, 815 pages : colored illustrations; 29 cm. + CD-ROM (4 3/4 in.)

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Contents
  • 1. Statistics, Data, and Statistical Thinking
  • 1.1. 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. 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 Case-Part 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. Additive Rule and Mutually Exclusive Events
  • 3.5. Conditional Probability
  • 3.6. 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
  • pt. I Discrete Random Variables
  • 4.2. Probability Distributions for Discrete Random Variables
  • 4.3. Binomial Distribution
  • 4.4. Other Discrete Distributions: Poisson and Hypergeometric
  • pt. II Continuous Random Variables
  • 4.5. Probability Distributions for Continuous Random Variables
  • 4.6. 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. Concept of a Sampling Distribution
  • 5.2. Properties of Sampling Distributions: Unbiasedness and Minimum Variance
  • 5.3. Sampling Distribution of the Sample Mean and the Central Limit Theorem
  • 5.4. Sampling Distribution of the Sample Proportion
  • Statistics in Action: The Insomnia Pill: Is It Effective?
  • Activity 5.1 Simulating a Sampling Distribution--Cell 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)
  • 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. 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 β(Optional)
  • Statistics in Action: Diary of a Kleenex® 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: Zixlt 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. Completely Randomized Design: Single Factor
  • 9.3. Multiple Comparisons of Means
  • 9.4. 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. 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 9and 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 β
  • 11.5. Coefficients of Correlation and Determination
  • 11.6. Using the Model for Estimation and Prediction
  • 11.7. 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
  • pt. I First-Order Models With Quantitative Independent Variables
  • 12.2. Estimating and Making Inferences about the /β Parameters
  • 12.3. Evaluating Overall Model Utility
  • 12.4. Using the Model for Estimation and Prediction
  • pt. 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
  • pt. 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. Logic of Control Charts
  • 13.4. Control Chart for Monitoring the Mean of a Process: The x-Chart
  • 13.5. Control Chart for Monitoring the Variation of a Process: The R-Chart
  • 13.6. 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 --
  • Contents note continued: 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, α = .10
  • Table VI Percentage Points of the F-Distribution, α = .05
  • Table VII Percentage Points of the F-Distribution, α = .025
  • Table VIII Percentage Points of the F-Distribution, α = .01
  • Table IX Control Chart Constants
  • Table X Critical Values for the Durbin-Watson d-Statistic, α = .05
  • Table XI Critical Values for the Durbin-Watson d-Statistic, α = .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, α = .05.
ISBN
  • 9780321826237
  • 032182623X
Identifying numbers
  • LCCN: 2012027020
  • OCLC: 800352512
  • OCLC: 800352512

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