Statistics for Business and Economics – James T. McClave, P. George Benson, Terry Sincich – 10th Edition

Chapter 1 Statistics, Data, and Statistical Thinking
1.1 The Science of Statistics
1.2 Types of Statistical Applications
1.3 Fundamental Elements of Statistics
1.4 Processes (Optional)
1.5 Types of Data
1.6 Collecting Data
1.7 The Role of Statistics in Managerial Decision-Making

Statistics in Action: A "20/20" View of Survey Results - Fact or Fiction?
Using Technology: Creating and Listing Data in SPSS, MINITAB, and EXCEL

Chapter 2 Methods for Describing Sets of Data
2.1 Describing Qualitative Data
2.2 Graphical Methods for Describing Quantitative Data
2.3 Summation Notation
2.4 Numerical Measures of Central Tendency
2.5 Numerical Measures of Variability
2.6 Interpreting the Standard Deviation
2.7 Numerical Measures of Relative Standing
2.8 Methods for Detecting Outliers (Optional)
2.9 Graphing Bivariate Relationships (Optional)
2.10 The Time Series Plot (Optional)
2.11 Distorting the Truth with Descriptive Techniques

Statistics In Action: Characteristics of Physicians who Use or Refuse Ethics Consultation
Using Technology: Describing Data using SPSS, MINITAB, and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART I (A Case Covering Chapters 1 and 2)

Chapter 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 Random Sampling
3.8 Bayes’ Rule (Optional)

Statistics In Action: Lottery Buster!
Using Technology: Generating a Random Sample Using SPSS, MINITAB, and EXCEL/PHStat2

Chapter 4 Random Variables and Probability Distributions
4.1 Two Types of Random Variables
4.2 Probability Distributions for Discrete Random Variables
4.3 The Binomial Random Variable
4.4 The Poisson Random Variable (Optional)
4.5 Probability Distributions for Continuous Random Variables
4.6 The Uniform Distribution (Optional)
4.7 The Normal Distribution
4.8 Descriptive Methods for Assessing Normality
4.9 Approximating a Binomial Distribution with a Normal Distribution (Optional)
4.10 Sampling Distributions
4.11 The Sampling Distribution of and the Central Limit Theorem

Statistics in Action: Super Weapons Development — Optimizing the Hit Ratio
Using Technology: Binomial Probabilities, Normal Probabilities, and Simulated Sampling Distributions using SPSS, MINITAB, and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: THE FURNITURE FIRE CASE (A Case Covering Chapters 3-4)

Chapter 5 Inferences Based on a Single Sample: Estimation with Confidence Intervals
5.1 Identifying the Target Parameter
5.2 Large-Sample Confidence Interval for a Population Mean
5.3 Small-Sample Confidence Interval for a Population Mean
5.4 Large-Sample Confidence Interval for a Population Proportion
5.5 Determining the Sample Size
5.6 Finite Population Correction for Simple Random Sampling (Optional)
5.7 Sample survey Designs (Optional)

Statistics in Action: Scallops, Sampling, and the Law
Using Technology: Confidence Intervals using SPSS, MINITAB and EXCEL/PHStat2

Chapter 6 Inferences Based on a Single Sample: Tests of Hypothesis
6.1 The Elements of a Test of Hypothesi
6.2 Large-Sample Test of Hypothesis About a Population Mean
6.3 Observed Significance Levels: p-Values
6.4 Small-Sample Test of Hypothesis About a Population Mean
6.5 Large-Sample Test of Hypothesis About a Population Proportion
6.6 Calculating Type II Error Probabilities: More About β (Optional)
6.7 Test of Hypothesis About a Population Variance (Optional)

Statistics in Action: Diary of a Kleenex User
Using Technology: Tests of Hypotheses using SPSS, MINITAB and EXCEL/PHStat2

Chapter 7 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
7.1 Identifying the Target Parameter
7.2 Comparing Two Population Means: Independent Sampling
7.3 Comparing Two Population Means: Paired Difference Experiments
7.4 Comparing Two Population Proportions: Independent Sampling
7.5 Determining the Sample Size
7.6 Comparing Two Population Variances: Independent Sampling

Statistics in Action: The Effect of Self-Managed Work Teams on Family Life
Using Technology: Two-Sample Inferences using SPSS, MINITAB and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART II (A Case Covering Chapters 7-9)

Chapter 8 Analysis of Variance: Comparing More the Two Means
8.1 Elements of a Designed Experiment
8.2 The Completely Randomized Dsign
8.3 Multiple Comparisons of Mean
8.4 The Randomized Block Design (Optional)
8.5 Factorial Experiments

Statistics in Action: The Ethics of Downsizing
Using Technology: Analysis of Variance using SPSS, MINITAB and EXCEL/PHStat2

Chapter 9 The Chi-Square Test and the Analysis of Contingency Tables
9.1 Categorical Data and the Multinomial Distribution
9.2 Testing Category Probabilities: One-Way Table
9.3 Testing Category Probabilities: Two-Way (Contingency) Table
9.4 A Word of Caution About Chi-Square Tests

Statistics in Action: A Study of Coupon Users — Mail versus the Internet
Using Technology: Chi-Square Analyses using SPSS, MINITAB and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: DISCRIMINATION IN THE WORKPLACE (A Case Covering Chapters 8-9)

Chapter 10 Simple Linear Regression
10.1 Probabilistic Models
10.2 Fitting the Model: The Least Squares Approach
10.3 Model Assumptions
10.4 An Estimator of σ2
10.5 Making Inferences About the Slope β1
10.6 The Coefficient of Correlation
10.7 The Coefficient of Determination
10.8 Using the Model for Estimation and Prediction
10.9 A Complete Example

Statistics in Action: An MBA’s Work-Life Balance
Using Technology: Simple Linear Regression using SPSS, MINITAB and EXCEL/PHStat2

Chapter 11 Multiple Regression and ModelBuilding
11.1 Multiple Regression Models
11.2 The First-Order Model: Estimating and Interpreting the β-Parameters
11.3 Inferences About the Individual β Parameters and the Overall Model Utility
11.4 Using the Model for Estimation and Prediction
11.5 Model Building: Interaction Models
11.6 Model Building: Quadratic and other Higher-Order Models
11.7 Model Building: Qualitative (Dummy) Variable Models
11.8 Model Building: Models with both Quantitative and Qualitative Variables (Optional)
11.9 Model Building: Comparing Nested Models (Optional)
11.10 Model Building: Stepwise Regression (Optional)
11.11 Residual Analysis: Checking the Regression Assumptions
11.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

Statistics in Action: Bid-Rigging in the Highway construction Industry
Using Technology: Multiple Regression using SPSS, MINITAB and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: THE CONDO SALES CASE (A Case Covering Chapters 10-11)

Chapter 12 Methods for Quality Improvement
12.1 Quality, Processes, and Systems
12.2 Statistical Control
12.3 The Logic of Control Charts
12.4 A Control Chart for Monitoring the Mean of a Process: The -Chart
12.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart
12.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart
12.7 Diagnosing the Causes of Variation (Optional)
12.8 Capability Analysis (Optional)

Statistics in Action: Testing Jet Fuel Additive for Safety
Using Technology: Control Charts using SPSS, MINITAB and EXCEL/PHStat2

Chapter 13 Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD)
13.1 Descriptive Analysis: Index Numbers
13.2 Descriptive Analysis: Exponential Smoothing
13.3 Time Series Components
13.4 Forecasting: Exponential Smoothing
13.5 Forecasting Trends: The Holt-Winters Model (Optional)
13.6 Measuring Forecast Accuracy: MAD and RMSE
13.7 Forecasting Trends: Simple Linear Regression
13.8 Seasonal Regression Models
13.9 Autocorrelation and the Durbin-Watson Test

Statistics In Action: Forecasting the Monthly Sales of a New Cold Medicine
Using Technology: Forecasting using SPSS, MINITAB and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: THE GASKET MANUFACTURING CASE (A Case Covering Chapters 12-13)

Chapter 14 Nonparametric Statistics (available on CD)
14.1 Single Population Inferences
14.2 Comparing Two Populations: Independent Samples
14.3 Comparing Two Populations: Paired Difference Experiment
14.4 Comparing Three or More Populations: Completely Randomized Design
14.5 Comparing Three or More Populations: Randomized Block Design (Optional)
14.6 Rank Correlation

Statistics in Action: Deadly Exposure — Agent Orange and Vietnam Vets
Using Technology: Nonparametric Analyses using SPSS, MINITAB and EXCEL/PHStat2

Appendix A Basic Counting Rules
Appendix B Tables
Table I Random Numbers
Table II Binomial Probabilities
Table III Poisson Probabilities
Table IV Normal Curve Areas
Table V Critical Values of t
Table VI Critical Values of χ2
Table VII Percentage Points of the F Distribution, α=.10
Table VIII Percentage Points of the F Distribution, α=.05
Table IX Percentage Points of the F Distribution, α=.025
Table X Percentage Points of the F Distribution, α=.01
Table XI Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples
Table XII Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test
Table XIII Critical Values of Spearman's Rank Correlation Coefficient
Appendix C Calculation Formulas for Analysis of Variance