Title Statistics for Business and Economics 9.6 MB 1117
```                            Front Cover
Title Page
Contents
Preface
Chapter 1 Data and Statistics
1.1 Applications in Business and Economics
Accounting
Finance
Marketing
Production
Economics
1.2 Data
Elements, Variables, and Observations
Scales of Measurement
Categorical and Quantitative Data
Cross-Sectional and Time Series Data
1.3 Data Sources
Existing Sources
Statistical Studies
Data Acquisition Errors
1.4 Descriptive Statistics
1.5 Statistical Inference
1.6 Computers and Statistical Analysis
1.7 Data Mining
1.8 Ethical Guidelines for Statistical Practice
Summary
Glossary
Supplementary Exercises
Appendix: An Introduction to StatTools
Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations
Statistics in Practice: Colgate-Palmolive Company
2.1 Summarizing Categorical Data
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Bar Charts and Pie Charts
2.2 Summarizing Quantitative Data
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Dot Plot
Histogram
Cumulative Distributions
Ogive
2.3 Exploratory Data Analysis: The Stem-and-Leaf Display
2.4 Crosstabulations and Scatter Diagrams
Crosstabulation
Scatter Diagram and Trendline
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Motion Picture Industry
Appendix 2.1 Using Minitab for Tabular and Graphical Presentations
Appendix 2.2 Using Excel for Tabular and Graphical Presentations
Appendix 2.3 Using StatTools for Tabular and Graphical Presentations
Chapter 3 Descriptive Statistics: Numerical Measures
Statistics in Practice: Small Fry Design
3.1 Measures of Location
Mean
Median
Mode
Percentiles
Quartiles
3.2 Measures of Variability
Range
Interquartile Range
Variance
Standard Deviation
Coefficient of Variation
3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
Distribution Shape
z-Scores
Chebyshev's Theorem
Empirical Rule
Detecting Outliers
3.4 Exploratory Data Analysis
Five-Number Summary
Box Plot
3.5 Measures of Association Between Two Variables
Covariance
Interpretation of the Covariance
Correlation Coefficient
Interpretation of the Correlation Coefficient
3.6 The Weighted Mean and Working with Grouped Data
Weighted Mean
Grouped Data
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Pelican Stores
Case Problem 2: Motion Picture Industry
Case Problem 3: Business Schools of Asia-Pacific
Case Problem 4: Heavenly Chocolates Website Transactions
Appendix 3.1 Descriptive Statistics Using Minitab
Appendix 3.2 Descriptive Statistics Using Excel
Appendix 3.3 Descriptive Statistics Using StatTools
Chapter 4 Introduction to Probability
Statistics in Practice: Oceanwide Seafood
4.1 Experiments, Counting Rules, and Assigning Probabilities
Counting Rules, Combinations, and Permutations
Assigning Probabilities
Probabilities for the KP&L Project
4.2 Events and Their Probabilities
4.3 Some Basic Relationships of Probability
Complement of an Event
4.4 Conditional Probability
Independent Events
Multiplication Law
4.5 Bayes' Theorem
Tabular Approach
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Hamilton County Judges
Chapter 5 Discrete Probability Distributions
Statistics in Practice: Citibank
5.1 Random Variables
Discrete Random Variables
Continuous Random Variables
5.2 Discrete Probability Distributions
5.3 Expected Value and Variance
Expected Value
Variance
5.4 Binomial Probability Distribution
A Binomial Experiment
Martin Clothing Store Problem
Using Tables of Binomial Probabilities
Expected Value and Variance for the Binomial Distribution
5.5 Poisson Probability Distribution
An Example Involving Time Intervals
An Example Involving Length or Distance Intervals
5.6 Hypergeometric Probability Distribution
Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 5.1 Discrete Probability Distributions with Minitab
Appendix 5.2 Discrete Probability Distributions with Excel
Chapter 6 Continuous Probability Distributions
Statistics in Practice: Procter & Gamble
6.1 Uniform Probability Distribution
Area as a Measure of Probability
6.2 Normal Probability Distribution
Normal Curve
Standard Normal Probability Distribution
Computing Probabilities for Any Normal Probability Distribution
Grear Tire Company Problem
6.3 Normal Approximation of Binomial Probabilities
6.4 Exponential Probability Distribution
Computing Probabilities for the Exponential Distribution
Relationship Between the Poisson and Exponential Distributions
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Specialty Toys
Appendix 6.1 Continuous Probability Distributions with Minitab
Appendix 6.2 Continuous Probability Distributions with Excel
Chapter 7 Sampling and Sampling Distributions
7.1 The Electronics Associates Sampling Problem
7.2 Selecting a Sample
Sampling from a Finite Population
Sampling from an Infinite Population
7.3 Point Estimation
7.4 Introduction to Sampling Distributions
7.5 Sampling Distribution of x
Expected Value of x
Standard Deviation of x
Form of the Sampling Distribution of x
Sampling Distribution of x for the EAI Problem
Practical Value of the Sampling Distribution of x
Relationship Between the Sample Size and the Sampling Distribution of x
7.6 Sampling Distribution of p
Expected Value of p
Standard Deviation of p
Form of the Sampling Distribution of p
Practical Value of the Sampling Distribution of p
7.7 Properties of Point Estimators
Unbiased
Efficiency
Consistency
7.8 Other Sampling Methods
Stratified Random Sampling
Cluster Sampling
Systematic Sampling
Convenience Sampling
Judgment Sampling
Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 7.1 The Expected Value and Standard Deviation of x
Appendix 7.2 Random Sampling with Minitab
Appendix 7.3 Random Sampling with Excel
Appendix 7.4 Random Sampling with StatTools
Chapter 8 Interval Estimation
Statistics in Practice: Food Lion
8.1 Population Mean: &#963; Known
Margin of Error and the Interval Estimate
8.2 Population Mean: &#963; Unknown
Margin of Error and the Interval Estimate
Using a Small Sample
Summary of Interval Estimation Procedures
8.3 Determining the Sample Size
8.4 Population Proportion
Determining the Sample Size
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Young Professional Magazine
Case Problem 2: Gulf Real Estate Properties
Case Problem 3: Metropolitan Research, Inc.
Appendix 8.1 Interval Estimation with Minitab
Appendix 8.2 Interval Estimation with Excel
Appendix 8.3 Interval Estimation with StatTools
Chapter 9 Hypothesis Tests
Statistics in Practice: John Morrell & Company
9.1 Developing Null and Alternative Hypotheses
The Alternative Hypothesis as a Research Hypothesis
The Null Hypothesis as an Assumption to Be Challenged
Summary of Forms for Null and Alternative Hypotheses
9.2 Type I and Type II Errors
9.3 Population Mean: &#963; Known
One-Tailed Test
Two-Tailed Test
Relationship Between Interval Estimation and Hypothesis Testing
9.4 Population Mean: &#963; Unknown
One-Tailed Test
Two-Tailed Test
9.5 Population Proportion
Summary
9.6 Hypothesis Testing and Decision Making
9.7 Calculating the Probability of Type II Errors
9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Quality Associates, Inc.
Case Problem 2: Ethical Behavior of Business Students at Bayview University
Appendix 9.1 Hypothesis Testing with Minitab
Appendix 9.2 Hypothesis Testing with Excel
Appendix 9.3 Hypothesis Testing with StatTools
Chapter 10 Inference About Means and Proportions with Two Populations
Statistics in Practice: U.S. Food and Drug Administration
10.1 Inferences About the Difference Between Two Population Means: &#963;[sub(1)] and &#963;[sub(2)] Known
Interval Estimation of &#956;[sub(1)] – &#956;[sub(2)]
Hypothesis Tests About &#956;[sub(1)] – &#956;[sub(2)]
10.2 Inferences About the Difference Between Two Population Means: &#963;[sub(1)] and &#963;[sub(2)] Unknown
Interval Estimation of &#956;[sub(1)] – &#956;[sub(2)]
Hypothesis Tests About &#956;[sub(1)] – &#956;[sub(2)]
10.3 Inferences About the Difference Between Two Population Means: Matched Samples
10.4 Inferences About the Difference Between Two Population Proportions
Interval Estimation of p[sub(1)] – p[sub(2)]
Hypothesis Tests About p[sub(1)] – p[sub(2)]
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Par, Inc.
Appendix 10.1 Inferences About Two Populations Using Minitab
Appendix 10.2 Inferences About Two Populations Using Excel
Appendix 10.3 Inferences About Two Populations Using StatTools
Chapter 11 Inferences About Population Variances
Statistics in Practice: U.S. Government Accountability Office
11.1 Inferences About a Population Variance
Interval Estimation
Hypothesis Testing
11.2 Inferences About Two Population Variances
Summary
Key Formulas
Supplementary Exercises
Case Problem: Air Force Training Program
Appendix 11.1 Population Variances with Minitab
Appendix 11.2 Population Variances with Excel
Appendix 11.3 Population Standard Deviation with StatTools
Chapter 12 Tests of Goodness of Fit and Independence
Statistics in Practice: United Way
12.1 Goodness of Fit Test: A Multinomial Population
12.2 Test of Independence
12.3 Goodness of Fit Test: Poisson and Normal Distributions
Poisson Distribution
Normal Distribution
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: A Bipartisan Agenda for Change
Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab
Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel
Chapter 13 Experimental Design and Analysis of Variance
Statistics in Practice: Burke Marketing Services, Inc.
13.1 An Introduction to Experimental Design and Analysis of Variance
Data Collection
Assumptions for Analysis of Variance
Analysis of Variance: A Conceptual Overview
13.2 Analysis of Variance and the Completely Randomized Design
Between-Treatments Estimate of Population Variance
Within-Treatments Estimate of Population Variance
Comparing the Variance Estimates: The F Test
ANOVA Table
Computer Results for Analysis of Variance
Testing for the Equality of k Population Means: An Observational Study
13.3 Multiple Comparison Procedures
Fisher’s LSD
Type I Error Rates
13.4 Randomized Block Design
Air Traffic Controller Stress Test
ANOVA Procedure
Computations and Conclusions
13.5 Factorial Experiment
ANOVA Procedure
Computations and Conclusions
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Wentworth Medical Center
Case Problem 2: Compensation for Sales Professionals
Appendix 13.1 Analysis of Variance with Minitab
Appendix 13.2 Analysis of Variance with Excel
Appendix 13.3 Analysis of Variance with StatTools
Chapter 14 Simple Linear Regression
Statistics in Practice: Alliance Data Systems
14.1 Simple Linear Regression Model
Regression Model and Regression Equation
Estimated Regression Equation
14.2 Least Squares Method
14.3 Coefficient of Determination
Correlation Coefficient
14.4 Model Assumptions
14.5 Testing for Significance
Estimate of &#963;[sup(2)]
t Test
Confidence Interval for &#946;[sub(1)]
F Test
Some Cautions About the Interpretation of Significance Tests
14.6 Using the Estimated Regression Equation for Estimation and Prediction
Point Estimation
Interval Estimation
Confidence Interval for the Mean Value of y
Prediction Interval for an Individual Value of y
14.7 Computer Solution
14.8 Residual Analysis: Validating Model Assumptions
Residual Plot Against x
Residual Plot Against y
Standardized Residuals
Normal Probability Plot
14.9 Residual Analysis: Outliers and Influential Observations
Detecting Outliers
Detecting Influential Observations
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Measuring Stock Market Risk
Case Problem 2: U.S. Department of Transportation
Case Problem 3: Alumni Giving
Case Problem 4: PGA Tour Statistics
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
Appendix 14.2 A Test for Significance Using Correlation
Appendix 14.3 Regression Analysis with Minitab
Appendix 14.4 Regression Analysis with Excel
Appendix 14.5 Regression Analysis with StatTools
Chapter 15 Multiple Regression
Statistics in Practice: dunnhumby
15.1 Multiple Regression Model
Regression Model and Regression Equation
Estimated Multiple Regression Equation
15.2 Least Squares Method
An Example: Butler Trucking Company
Note on Interpretation of Coefficients
15.3 Multiple Coefficient of Determination
15.4 Model Assumptions
15.5 Testing for Significance
F Test
t Test
Multicollinearity
15.6 Using the Estimated Regression Equation for Estimation and Prediction
15.7 Categorical Independent Variables
An Example: Johnson Filtration, Inc.
Interpreting the Parameters
More Complex Categorical Variables
15.8 Residual Analysis
Detecting Outliers
Studentized Deleted Residuals and Outliers
Influential Observations
Using Cook’s Distance Measure to Identify Influential Observations
15.9 Logistic Regression
Logistic Regression Equation
Estimating the Logistic Regression Equation
Testing for Significance
Managerial Use
Interpreting the Logistic Regression Equation
Logit Transformation
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Consumer Research, Inc.
Case Problem 2: Alumni Giving
Case Problem 3: PGA Tour Statistics
Case Problem 4: Predicting Winning Percentage for the NFL
Appendix 15.1 Multiple Regression with Minitab
Appendix 15.2 Multiple Regression with Excel
Appendix 15.3 Logistic Regression with Minitab
Appendix 15.4 Multiple Regression with StatTools
Chapter 16 Regression Analysis: Model Building
Statistics in Practice: Monsanto Company
16.1 General Linear Model
Modeling Curvilinear Relationships
Interaction
Transformations Involving the Dependent Variable
Nonlinear Models That Are Intrinsically Linear
16.2 Determining When to Add or Delete Variables
General Case
Use of p-Values
16.3 Analysis of a Larger Problem
16.4 Variable Selection Procedures
Stepwise Regression
Forward Selection
Backward Elimination
Best-Subsets Regression
Making the Final Choice
16.5 Multiple Regression Approach to Experimental Design
16.6 Autocorrelation and the Durbin-Watson Test
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Analysis of PGA Tour Statistics
Case Problem 2: Fuel Economy for Cars
Appendix 16.1 Variable Selection Procedures with Minitab
Appendix 16.2 Variable Selection Procedures with StatTools
Chapter 17 Index Numbers
Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics
17.1 Price Relatives
17.2 Aggregate Price Indexes
17.3 Computing an Aggregate Price Index from Price Relatives
17.4 Some Important Price Indexes
Consumer Price Index
Producer Price Index
Dow Jones Averages
17.5 Deflating a Series by Price Indexes
17.6 Price Indexes: Other Considerations
Selection of Items
Selection of a Base Period
Quality Changes
17.7 Quantity Indexes
Summary
Glossary
Key Formulas
Supplementary Exercises
Chapter 18 Time Series Analysis and Forecasting
Statistics in Practice: Nevada Occupational Health Clinic
18.1 Time Series Patterns
Horizontal Pattern
Trend Pattern
Seasonal Pattern
Trend and Seasonal Pattern
Cyclical Pattern
Selecting a Forecasting Method
18.2 Forecast Accuracy
18.3 Moving Averages and Exponential Smoothing
Moving Averages
Weighted Moving Averages
Exponential Smoothing
18.4 Trend Projection
Linear Trend Regression
Holt’s Linear Exponential Smoothing
Nonlinear Trend Regression
18.5 Seasonality and Trend
Seasonality Without Trend
Seasonality and Trend
Models Based on Monthly Data
18.6 Time Series Decomposition
Calculating the Seasonal Indexes
Deseasonalizing the Time Series
Using the Deseasonalized Time Series to Identify Trend
Models Based on Monthly Data
Cyclical Component
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1: Forecasting Food and Beverage Sales
Case Problem 2: Forecasting Lost Sales
Appendix 18.1 Forecasting with Minitab
Appendix 18.2 Forecasting with Excel
Appendix 18.3 Forecasting with StatTools
Chapter 19 Nonparametric Methods
Statistics in Practice: West Shell Realtors
19.1 Sign Test
Hypothesis Test About a Population Median
Hypothesis Test with Matched Samples
19.2 Wilcoxon Signed-Rank Test
19.3 Mann-Whitney-Wilcoxon Test
19.4 Kruskal-Wallis Test
19.5 Rank Correlation
Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 19.1 Nonparametric Methods with Minitab
Appendix 19.2 Nonparametric Methods with Excel
Appendix 19.3 Nonparametric Methods with StatTools
Chapter 20 Statistical Methods for Quality Control
Statistics in Practice: Dow Chemical Company
20.1 Philosophies and Frameworks
Malcolm Baldrige National Quality Award
ISO 9000
Six Sigma
20.2 Statistical Process Control
Control Charts
x Chart: Process Mean and Standard Deviation Known
x Chart: Process Mean and Standard Deviation Unknown
R Chart
p Chart
np Chart
Interpretation of Control Charts
20.3 Acceptance Sampling
KALI, Inc.: An Example of Acceptance Sampling
Computing the Probability of Accepting a Lot
Selecting an Acceptance Sampling Plan
Multiple Sampling Plans
Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 20.1 Control Charts with Minitab
Appendix 20.2 Control Charts with StatTools
Chapter 21 Decision Analysis
Statistics in Practice: Ohio Edison Company
21.1 Problem Formulation
Payoff Tables
Decision Trees
21.2 Decision Making with Probabilities
Expected Value Approach
Expected Value of Perfect Information
21.3 Decision Analysis with Sample Information
Decision Tree
Decision Strategy
Expected Value of Sample Information
21.4 Computing Branch Probabilities Using Bayes’ Theorem
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem: Lawsuit Defense Strategy
Appendix: An Introduction to PrecisionTree
Appendix A: References and Bibliography
Appendix B: Tables
Appendix C: Summation Notation
Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises
Appendix E: Using Excel Functions
Appendix F: Computing p-Values Using Minitab and Excel
Index
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