About the Author
Contents in Brief
Chapter 1 Introduction and Descriptive Statistics

1– 1 Using Statistics Samples and Populations Data and Data Collection
1– 2 Percentiles and Quartiles
1– 3 Measures of Central Tendency
1– 4 Measures of Variability
1– 5 Grouped Data and the Histogram
1– 6 Skewness and Kurtosis
1– 7 Relations between the Mean and the Standard Deviation
      Chebyshev’s Theorem
      The Empirical Rule
1– 8 Methods of Displaying Data
      Pie Charts
      Bar Charts
      Frequency Polygons and Ogives
      A Caution about Graphs
      Time Plots
1– 9 Exploratory Data Analysis
      Stem- and- Leaf Displays
      Box Plots
1– 10 Summary and Review of Terms
      Case 1: NASDAQ Volatiliy

Chapter 2 Probability
2- 1 Using Statistics
      A Missed Pickup Is a Missed Opportunity
2– 2 Basic Definitions: Events, Sample Space, and Probabilities
2– 3 Basic Rules for Probability
      The Range of Values
      The Rule of Complements
      Mutually Exclusive Events
2– 4 Conditional Probability
2– 5 Independence of Events
      Product Rules for Independent Events
2– 6 Combinatorial Concepts
2– 7 The Law of Total Probability and Bayes’ Theorem
      The Law of Total Probability
      Bayes’ Theorem
2– 8 The Joint Probability Table
2– 9 Summary and Review of Terms
      Case 2: Service Encounters

Chapter 3 Random Variables
3- 1 Using Statistics
      Discrete and Continuous Random Variables
      Cumulative Distribution Function
3– 2 Expected Values of Discrete Random Variables
      The Expected Value of a Function of a Random Variable
      Variance and Standard Deviation of a Random Variable
      Variance of a Linear Function of a Random Variable
3– 3 Sum and Linear Composites of Random Variables
      Chebyshev’s Theorem
3- 4 Covariance
3– 5 Bernoulli Random Variable
3– 6 The Binomial Random Variable
      Conditions for a Binomial Random Variable
      Binomial Distribution Formulas
3– 7 Negative Binomial Distribution
      Negative Binomial Distribution Formulas
3– 8 The Geometric Distribution
      Geometric Distribution Formulas
3– 9 The Hypergeometric Distribution
      Hypergeometric Distribution Formulas
3– 10 The Poisson Distribution
3– 11 Continuous Random Variables
3– 12 The Uniform Distribution
3– 13 The Exponential Distribution
      A Remarkable Property
      Value at Risk
3– 14 Summary and Review of Terms
      Case 3: Concepts Testing

Chapter 4 The Normal Distribution
4- 1 Using Statistics
4– 2 Properties of the Normal Distribution
4– 3 The Standard Normal Distribution
      Finding Probabilities of the Standard Normal Distribution
      Finding Values of Z Given a Probability
4– 4 The Transformation of Normal Random Variables
      Using the Normal Transformation
4– 5 The Inverse Transformation
4– 7 Normal Approximation of Binomial Distributions
4– 8 Summary and Review of Terms
      Case 4: Acceptable Pins
      Case 5: A Multicurrency Decision

Chapter 5 Sampling and Sampling Distributions
5- 1 Using Statistics
5– 2 Sample Statistics as Estimators of Population Parameters
      Obtaining a Random Sample
      Other Sampling Methods
5– 3 Sampling Distributions
      The Central Limit Theorem
      The History of the Central Limit Theorem
      The Standardized Sampling Distribution of the Sample Mean When s Is Not Known
      The Sampling Distribution of the Sample Proportion Pˆ
5– 4 Estimators and Their Properties
      Applying the Concepts of Unbiasedness, Efficiency, Consistency, and Sufficiency
5– 5 Degrees of Freedom
5– 6 Summary and Review of Terms

Chapter 6 Confidence Intervals
6– 1 Using Statistics
6– 3 Confidence Intervals for æ When ó Is Unknown— The t Distribution
      The t Distribution
6– 4 Large- Sample Confidence Intervals for the Population Proportion p
6– 5 Confidence Intervals for the Population Variance
6– 6 Sample- Size Determination
6– 7 Summary and Review of Terms
      Case 7: Acme Whistles

Chapter 7 Hypothesis Testing
7- 1 Using Statistics
      The Null Hypothesis
7– 2 The Concepts of Hypothesis Testing
      Evidence Gathering
      Type I and Type II Errors
      The p- Value
      The Significance Level
      Optimal  á and the Compromise between Type I and Type II Errors
      â and Power
      Sample Size
7– 3 Computing the p- Value
      The Test Statistic
      p- Value Calculations
      One- Tailed and Two- Tailed Tests
      Computing â
7– 4 The Hypothesis Test
      Testing Population Means
      A Note on t Tables and p- Values
      Testing Population Proportions
      Testing Population Variances
7– 5 Pretest Decisions
      Testing Population Means
      Manual Calculation of Required Sample Size
      Testing Population Proportions
      Manual Calculation of Sample Size
7– 6 Summary and Review of Terms
      Case 8: Tiresome Tires I

Chapter 8 The Comparison of Two Populations
8- 1 Using Statistics
8– 2 Paired- Observation Comparisons
      Confidence Intervals
      Confidence Intervals
      Confidence Intervals
8– 4 A Large- Sample Test for the Difference between Two Population Proportions
      Confidence Intervals
8– 5 The F Distribution and a Test for Equality of Two Population Variances
      A Statistical Test for Equality of Two Population Variances
8–6 Summary and Review of Terms
      Case 9: Tiresome Tires II

Chapter 9 Analysis of Variance
9- 1 Using Statistics
9– 2 The Hypothesis Test of Analysis of Variance
      The Test Statistic
9– 3 The Theory and the Computations of ANOVA
      The Sum- of- Squares Principle
      The Degrees of Freedom
      The Mean Squares
      The Expected Values of the Statistics MSTR and MSE under the Null Hypothesis
      The F Statistic
9– 4 The ANOVA Table and Examples
9– 5 Further Analysis
      The Tukey Pairwise- Comparisons Test
      Conducting the Tests
      The Case of Unequal Sample Sizes, and Alternative Procedures
9– 6 Models, Factors, and Designs
      One- Factor versus Multifactor Models
      Fixed- Effects versus Random- Effects Models
      Experimental Design
9– 7 Two- Way Analysis of Variance
      The Two- Way ANOVA Model
      The Hypothesis Tests in Two- Way ANOVA
      Sums of Squares, Degrees of Freedom, and Mean Squares
      The F Ratios and the Two- Way ANOVA Table
      The Overall Significance Level
      The Tukey Method for Two- Way Analysis
      Extension of ANOVA to Three Factors
      Two- Way ANOVA with One Observation per Cell
9– 8 Blocking Designs
      Randomized Complete Block Design
9– 10 Summary and Review of Terms
      Case 10: Rating Wines
      Case 11: Checking Out Checkout

Chapter 10 Simple Linear Regression and Correlation
10- 1 Using Statistics
      Model Building
10– 2 The Simple Linear Regression Model
10– 3 Estimation: The Method of Least Squares
10– 4 Error Variance and the Standard
      Errors of Regression Estimators
      Confidence Intervals for the Regression Parameters
10– 5 Correlation
10– 6 Hypothesis Tests about the Regression Relationship
      Other Tests
10– 7 How Good Is the Regression?
10– 8 Analysis- of- Variance Table and an F Test of the Regression Model
10– 9 Residual Analysis and Checking for Model Inadequacies
      A Check for the Equality of Variance of the Errors
      Testing for Missing Variables
      Detecting a Curvilinear Relationship between Y and X
      The Normal Probability Plot
10– 10 Use of the Regression Model for Prediction
      Point Predictions
      Prediction Intervals
      A Confidence Interval for the Average Y, Given a Particular Value of X
10– 11 Summary and Review of Terms
      Case 12: Firm Leverage and Shareholder Rights
      Case 13: Risk and Return

Chapter 11 Multiple Regression
11- 1 Using Statistics
11– 2 The k- Variable Multiple Regression Model
      The Estimated Regression Relationship
11– 3 The F Test of a Multiple Regression Model
11– 4 How Good Is the Regression?
11– 5 Tests of the Significance of Individual Regression Parameters
11– 6 Testing the Validity of the Regression Model
      Residual Plots
      Standardized Residuals
      The Normal Probability Plot
      Outliers and Influential Observations
      Lack of Fit and Other Problems
11– 7 Using the Multiple Regression Model for Prediction
11– 8 Qualitative Independent Variables
      Interactions between Qualitative and Quantitative Variables
11– 9 Polynomial Regression
      Other Variables and Cross- Product Terms
11– 10 Nonlinear Models and Transformations
      Variance- Stabilizing Transformations
      Regression with Dependent Indicator Variable
11– 11 Multicollinearity
      Causes of Multicollinearity
      Detecting the Existence of Multicollinearity
      Solutions to the Multicollinearity Problem
11– 12 Residual Autocorrelation and the Durbin- Watson Test
11– 13 Partial F Tests and Variable Selection Methods
      Partial F Tests
      Variable Selection Methods
11– 14 Summary and Review of Terms
      Case 12: Return on Capital for Four Different Sectors

Chapter 12 Time Series, Forecasting, and Index Numbers
12- 1 Using Statistics
12– 2 Trend Analysis
12– 3 Seasonality and Cyclical Behavior
12– 4 The Ratio- to- Moving- Average Method
      The Cyclical Component of the Series
      Forecasting a Multiplicative Series
      Contents xvii
12– 5 Exponential Smoothing Methods
12– 6 Index Numbers
      The Consumer Price Index
12– 7 Summary and Review of Terms
      Case 13: Auto Parts Sales Forecast

Chapter 13 Quality Control and Improvement
13- 1 Using Statistics
13– 2 W. Edwards Deming Instructs
13– 3 Statistics and Quality
      Deming’s 14 Points
      Process Capability
      Control Charts
      Pareto Diagrams
      Six Sigma
      Acceptance Sampling
      Analysis of Variance and Experimental Design
      Taguchi Methods
13– 4 The x
13– 5 The R Chart and the s Chart
      The R Chart
      The s Chart
13– 6 The p Chart
      The Template
13– 7 The c Chart
13– 8 The x Chart
13– 9 Summary and Review of Terms
      Case 14: Quality Control and Improvement at Nashua Corporation

Chapter 14 Nonparametric Methods and Chi- Square Tests
14- 1 Using Statistics
14– 2 The Sign Test
14– 3 The Runs Test— A Test for Randomness
      Large- Sample Properties
      The Wald- Wolfowitz Test
14– 4 The Mann- Whitney U Test
      The Computational Procedure
14– 5 The Wilcoxon Signed- Rank Test
      The Paired- Observations Two- Sample Test
      Large- Sample Version of the Test
      A Test for the Mean or Median of a Single Population
14– 6 The Kruskal- Wallis Test— A Nonparametric Alternative to One- Way ANOVA
      Further Analysis
14– 7 The Friedman Test for a Randomized Block Design
      The Template
14– 8 The Spearman Rank Correlation Coefficient
14– 9 A Chi- Square Test for Goodness of Fit
      A Goodness- of- Fit Test for the Multinomial Distribution
      Unequal Probabilities
14– 10 Contingency Table Analysis— A Chi- Square Test for Independence
14– 11 A Chi- Square Test for Equality of Proportions
      The Median Test
14– 12 Summary and Review of Terms
      Case 15: The Nine Nations of North America

Chapter 15 Bayesian Statistics and Decision Analysis
15- 1 Using Statistics
15– 2 Bayes’ Theorem and Discrete
      Probability Models
15– 3 Bayes’ Theorem and Continuous
      Probability Distributions
      The Normal Probability Model
      Credible Sets
15– 4 The Evaluation of Subjective Probabilities
      Assessing a Normal Prior Distribution
15– 5 Decision Analysis: An Overview
      Chance Occurrences
      Final Outcomes
      Additional Information
15– 6 Decision Trees
      The Payoff Table
15– 7 Handling Additional Information Using Bayes’ Theorem
      Determining the Payoffs
      Determining the Probabilities
15– 8 Utility
      A Method of Assessing Utility
15– 9 The Value of Information
15– 10 Summary and Review of Terms
      Case 16: Pizzas ‘R’ Us
      Case 17: New Drug Development

APPENDIX A References
APPENDIX B Answers to Most Odd- Numbered Problems
APPENDIX C Statistical Tables


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