(EBook PDF) Statistics For Business and Economics 13th Edition by David Ray Anderson 9798214350462 full chapters

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ISBN-13 ‏ : ‎ 9798214350462

Author:  David Ray Anderson 

Get more out of your statistics course than simply solving equations. Discover how statistical information enables strong decisions in today’s business world with STATISTICS FOR BUSINESS AND ECONOMICS, REVISED 13E. Sound methodology combines with a proven problem-scenario approach, and meaningful applications for the most powerful approach to mastering critical business statistics. This edition’s prestigious author team brings together more than 25 years of unmatched experience to this thoroughly updated text. More than 350 real business examples, timely cases, and memorable exercises present the latest statistical data and business information with unwavering accuracy. To ensure the most relevant coverage, this edition includes coverage of popular commercial statistical software Minitab 17 and Excel 2016.

Statistics For Business & Economics 13th Table of contents:

Chapter 1. Data and Statistics
1.1. Applications in Business and Economics
Accounting
Finance
Marketing
Production
Economics
Information Systems
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
Observational Study
Experiment
Time and Cost Issues
Data Acquisition Errors
1.4. Descriptive Statistics
1.5. Statistical Inference
1.6. Analytics
1.7. Big Data and Data Mining
1.8. Computers and Statistical Analysis
1.9. Ethical Guidelines for Statistical Practice
Summary
Glossary
Supplementary Exercises
Chapter 2. Descriptive Statistics: Tabular and Graphical Displays
2.1. Summarizing Data for a Categorical Variable
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Bar Charts and Pie Charts
Exercises: Methods
Exercises: Applications
2.2. Summarizing Data for a Quantitative Variable
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Dot Plot
Histogram
Cumulative Distributions
Stem-and-Leaf Display
Exercises: Methods
Exercises: Applications
2.3. Summarizing Data for Two Variables Using Tables
Crosstabulation
Simpson’s Paradox
Exercises: Methods
Exercises: Applications
2.4. Summarizing Data for Two Variables Using Graphical Displays
Scatter Diagram and Trendline
Side-by-Side and Stacked bar Charts
Exercises: Methods
Exercises: Applications
2.5. Data Visualization: Best Practices in Creating Effective Graphical Displays
Creating Effective Graphical Displays
Choosing the Type of Graphical Display
Data Dashboards
Data Visualization in Practice: Cincinnati Zoo and Botanical Garden
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1. Pelican Stores
Case Problem 2. Motion Picture Industry
Case Problem 3. Queen City
Appendix 2.1. Using Minitab for Tabular and Graphical Presentations
Appendix 2.2. Using Excel for Tabular and Graphical Presentations
Chapter 3. Descriptive Statistics: Numerical Measures
3.1. Measures of Location
Mean
Weighted Mean
Median
Geometric Mean
Mode
Percentiles
Quartiles
Exercises: Methods
Exercises: Applications
3.2. Measures of Variability
Range
Interquartile Range
Variance
Standard Deviation
Coefficient of Variation
Exercises: Methods
Exercises: Applications
3.3. Measures of Distribution Shape, Relative Location, and Detecting Outliers
Distribution Shape
z-Scores
Chebyshev’s Theorem
Empirical Rule
Detecting Outliers
Exercises: Methods
Exercises: Applications
3.4. Five-Number Summaries and Boxplots
Five-Number Summary
Box Plot
Comparative Analysis Using Boxplots
Exercises: Methods
Exercises: Applications
3.5. Measures of Association between Two Variables
Covariance
Interpretation of the Covariance
Correlation Coefficient
Interpretation of the Correlation Coefficient
Exercises: Methods
Exercises: Applications
3.6. Data Dashboards: Adding Numerical Measures to Improve Effectiveness
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
Case Problem 5. African Elephant Populations
Appendix 3.1. Descriptive Statistics Using Minitab
Appendix 3.2. Descriptive Statistics Using Excel
Chapter 4. Introduction to Probability
4.1. Random Experiments, counting Rules, and Assigning Probabilities
Counting Rules, Combinations, and Permutations
Assigning Probabilities
Probabilities for the KP&L Project
Exercises: Methods
Exercises: Applications
4.2. Events and Their Probabilities
Exercises: Methods
Exercises: Applications
4.3. Some Basic Relationships of Probability
Complement of an Event
Addition Law
Exercises: Methods
Exercises: Applications
4.4. Conditional Probability
Independent Events
Multiplication Law
Exercises: Methods
Exercises: Applications
4.5. Bayes’ Theorem
Tabular Approach
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem. Hamilton County Judges
Chapter 5. Discrete Probability Distributions
5.1. Random Variables
Discrete Random Variables
Continuous Random Variables
Exercises: Methods
Exercises: Applications
5.2. Developing Discrete Probability Distributions
Exercises: Methods
Exercises: Applications
5.3. Expected Value and Variance
Expected Value
Variance
Exercises: Methods
Exercises: Applications
5.4. Bivariate Distributions, Covariance, and Financial Portfolios
A Bivariate Empirical Discrete Probability Distribution
Financial Applications
Summary
Exercises: Methods
5.5. Binomial Probability Distribution
A Binomial Experiment
Martin Clothing Store Problem
Using Tables of Binomial Probabilities
Expected Value and Variance for the Binomial Distribution
Exercises: Methods
Exercises: Applications
5.6. Poisson Probability Distribution
An Example Involving Time Intervals
An Example Involving Length or Distance Intervals
Exercises: Methods
Exercises: Applications
5.7. Hypergeometric Probability Distribution
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem. Go Bananas!
Appendix 5.1. Discrete Probability Distributions with Minitab
Appendix 5.2. Discrete Probability Distributions with Excel
Chapter 6. Continuous Probability Distributions
6.1. Uniform Probability Distribution
Area as a Measure of Probability
Exercises: Methods
Exercises: Applications
6.2. Normal Probability Distribution
Normal Curve
Standard Normal Probability Distribution
Computing Probabilities for Any Normal Probability Distribution
Grear Tire Company Problem
Exercises: Methods
Exercises: Applications
6.3. Normal Approximation of Binomial Probabilities
Exercises: Methods
Exercises: Applications
6.4. Exponential Probability Distribution
Computing Probabilities for the Exponential Distribution
Relationship between the Poisson and Exponential Distributions
Exercises: Methods
Exercises: Applications
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
Exercises: Methods
Exercises: Applications
7.3. Point Estimation
Practical Advice
Exercises: Methods
Exercises: Applications
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 ¯
Exercises: Methods
Exercises: Applications
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 ¯
Exercises: Methods
Exercises: Applications
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
Case Problem. Marion Dairies
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
Chapter 8. Interval Estimation
8.1. Population Mean: σ Known
Margin of Error and the Interval Estimate
Practical Advice
Exercises: Methods
Exercises: Applications
8.2. Population Mean: σ Unknown
Margin of Error and the Interval Estimate
Practical Advice
Using a Small Sample
Summary of Interval Estimation Procedures
Exercises: Methods
Exercises: Applications
8.3. Determining the Sample Size
Exercises: Methods
Exercises: Applications
8.4. Population Proportion
Determining the Sample Size
Exercises: Methods
Exercises: Applications
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 Using Excel
Chapter 9. Hypothesis Tests
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
Exercises
9.2. Type I and Type II Errors
Exercises
9.3. Population Mean: σ Known
One-Tailed Test
Two-Tailed Test
Summary and Practical Advice
Relationship between Interval Estimation and Hypothesis Testing
Exercises
Exercises: Methods
Exercises: Applications
9.4. Population Mean: σ Unknown
One-Tailed Test
Two-Tailed Test
Summary and Practical Advice
Exercises: Methods
Exercises: Applications
9.5. Population Proportion
Summary
Exercises: Methods
Exercises: Applications
9.6. Hypothesis Testing and Decision Making
9.7. Calculating the Probability of Type II Errors
Exercises: Methods
Exercises: Applications
9.8. Determining the Sample Size for a Hypothesis Test about a Population Mean
Exercises: Methods
Exercises: Applications
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
Chapter 10. Inference about Means and Proportions with Two Populations
10.1. Inferences about the Difference between Two Population Means: σ 1 and σ 2 Known
Interval Estimation of μ 1 – μ 2
Hypothesis Tests about μ 1 – μ 2
Practical Advice
Exercises: Methods
Exercises: Applications
10.2. Inferences about the Difference between Two Population Means: σ 1 and σ 2 Unknown
Interval Estimation of μ 1 – μ 2
Hypothesis Tests about μ 1 – μ 2
Practical Advice
Exercises: Methods
Exercises: Applications
10.3. Inferences about the Difference between Two Population Means: Matched Samples
Exercises: Methods
Exercises: Applications
10.4. Inferences about the Difference between Two Population Proportions
Interval Estimation of p 1 – p 2
Hypothesis Tests about p 1 – p 2
Exercises: Methods
Exercises: Applications
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
Chapter 11. Inferences about Population Variances
11.1. Inferences about a Population Variance
Interval Estimation
Hypothesis Testing
Exercises: Methods
Exercises: Applications
11.2. Inferences about Two Population Variances
Exercises: Methods
Exercises: Applications
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
Chapter 12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit
12.1. Testing the Equality of Population Proportions for Three or More Populations
A Multiple Comparison Procedure
Exercises: Methods
Exercises: Applications
12.2. Test of Independence
Exercises: Methods
Exercises: Applications
12.3. Goodness of Fit Test
Multinomial Probability Distribution
Normal Probability Distribution
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem. A Bipartisan Agenda for Change
Appendix 12.1. Chi-Square Tests Using Minitab
Appendix 12.2. Chi-Square Tests Using Excel
Chapter 13. Experimental Design and Analysis of Variance
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
Exercises: Methods
Exercises: Applications
13.3. Multiple Comparison Procedures
Fisher’s LSD
Type I Error Rates
Exercises: Methods
Exercises: Applications
13.4. Randomized Block Design
Air Traffic Controller Stress Test
ANOVA Procedure
Computations and Conclusions
Exercises: Methods
Exercises: Applications
13.5. Factorial Experiment
ANOVA Procedure
Computations and Conclusions
Exercises: Methods
Exercises: Applications
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
Chapter 14. Simple Linear Regression
14.1. Simple Linear Regression Model
Regression Model and Regression Equation
Estimated Regression Equation
14.2. Least Squares Method
Exercises: Methods
Exercises: Applications
14.3. Coefficient of Determination
Correlation Coefficient
Exercises: Methods
Exercises: Applications
14.4. Model Assumptions
14.5. Testing for Significance
Estimate of σ 2
t Test
Confidence Interval for β 1
F Test
Some Cautions about the Interpretation of Significance Tests
Exercises: Methods
Exercises: Applications
14.6. Using the Estimated Regression Equation for Estimation and Prediction
Interval Estimation
Confidence Interval for the Mean Value of y
Prediction Interval for an Individual Value of y
Exercises: Methods
Exercises: Applications
14.7. Computer Solution
Exercises: Applications
14.8. Residual Analysis: Validating Model Assumptions
Residual Plot against x
Residual Plot against y ^
Standardized Residuals
Normal Probability Plot
Exercises: Methods
Exercises: Applications
14.9. Residual Analysis: Outliers and Influential Observations
Detecting Outliers
Detecting Influential Observations
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1. Measuring Stock Market Risk
Case Problem 2. U.S. Department of Transportation
Case Problem 3. Selecting a Point-and-Shoot Digital Camera
Case Problem 4. Finding the Best Car Value
Case Problem 5. Buckeye Creek Amusement Park
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
Chapter 15. Multiple Regression
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
Exercises: Methods
Exercises: Applications
15.3. Multiple Coefficient of Determination
Exercises: Methods
Exercises: Applications
15.4. Model Assumptions
15.5. Testing for Significance
F Test
t Test
Multicollinearity
Exercises: Methods
Exercises: Applications
15.6. Using the Estimated Regression Equation for Estimation and Prediction
Exercises: Methods
Exercises: Applications
15.7. Categorical Independent Variables
An Example: Johnson Filtration, Inc.
Interpreting the Parameters
More Complex Categorical Variables
Exercises: Methods
Exercises: Applications
15.8. Residual Analysis
Detecting Outliers
Studentized Deleted Residuals and Outliers
Influential Observations
Using Cook’S Distance Measure to Identify Influential Observations
Exercises: Methods
Exercises: Applications
15.9. Logistic Regression
Logistic Regression Equation
Estimating the Logistic Regression Equation
Testing for Significance
Managerial Use
Interpreting the Logistic Regression Equation
Logit Transformation
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1. Consumer Research, Inc.
Case Problem 2. Predicting Winnings for NASCAR Drivers
Case Problem 3. Finding the Best Car Value
Appendix 15.1. Multiple Regression with Minitab
Appendix 15.2. Multiple Regression with Excel
Appendix 15.3. Logistic Regression with Minitab
Chapter 16. Regression Analysis: Model Building
16.1. General Linear Model
Modeling Curvilinear Relationships
Interaction
Transformations Involving the Dependent Variable
Nonlinear Models That Are Intrinsically Linear
Exercises: Methods
Exercises: Applications
16.2. Determining When to Add or Delete Variables
General Case
Use of p-Values
Exercises: Methods
Exercises: Applications
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
Exercises: Applications
16.5. Multiple Regression Approach to Experimental Design
Exercises: Methods
Exercises: Applications
16.6. Autocorrelation and the Durbin-Watson Test
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1. Analysis of PGA Tour Statistics
Case Problem 2. Rating Wines from the Piedmont Region of Italy
Appendix 16.1. Variable Selection Procedures with Minitab
Chapter 17. Time Series Analysis and Forecasting
17.1. Time Series Patterns
Horizontal Pattern
Trend Pattern
Seasonal Pattern
Trend and Seasonal Pattern
Cyclical Pattern
Selecting a Forecasting Method
17.2. Forecast Accuracy
Exercises: Methods
17.3. Moving Averages and Exponential Smoothing
Moving Averages
Weighted Moving Averages
Exponential Smoothing
Exercises: Methods
Exercises: Applications
17.4. Trend Projection
Linear Trend Regression
Nonlinear Trend Regression
Exercises: Methods
Exercises: Applications
17.5. Seasonality and Trend
Seasonality without Trend
Seasonality and Trend
Models Based on Monthly Data
Exercises: Methods
Exercises: Applications
17.6. Time Series Decomposition
Calculating the Seasonal Indexes
Deseasonalizing the Time Series
Using the Deseasonalized Time Series to Identify Trend
Seasonal Adjustments
Models Based on Monthly Data
Cyclical Component
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem 1. Forecasting Food and Beverage Sales
Case Problem 2. Forecasting Lost Sales
Appendix 17.1. Forecasting with Minitab
Appendix 17.2. Forecasting with Excel
Chapter 18. Nonparametric Methods
18.1. Sign Test
Hypothesis Test about a Population Median
Hypothesis Test with Matched Samples
Exercises: Methods
Exercises: Applications
18.2. Wilcoxon Signed-Rank Test
Exercises: Applications
18.3. Mann-Whitney-Wilcoxon Test
Exercises: Applications
18.4. Kruskal-Wallis Test
Exercises: Applications
18.5. Rank Correlation
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 18.1. Nonparametric Methods with Minitab
Appendix 18.2. Nonparametric Methods with Excel
Chapter 19. Statistical Methods for Quality Control
19.1. Philosophies and Frameworks
Malcolm Baldrige National Quality Award
ISO 9000
Six Sigma
Quality in the Service Sector
19.2. 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
Exercises: Methods
Exercises: Applications
19.3. Acceptance Sampling
KALI, Inc.: An Example of Acceptance Sampling
Computing the Probability of Accepting a Lot
Selecting an Acceptance Sampling Plan
Multiple Sampling Plans
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Appendix 19.1. Control Charts with Minitab
Chapter 20. Index Numbers
20.1. Price Relatives
20.2. Aggregate Price Indexes
Exercises: Methods
Exercises: Applications
20.3. Computing an Aggregate Price Index from Price Relatives
Exercises: Methods
Exercises: Applications
20.4. Some Important Price Indexes
Consumer Price Index
Producer Price Index
Dow Jones Averages
20.5. Deflating a Series by Price Indexes
Exercises: Applications
20.6. Price Indexes: Other Considerations
Selection of Items
Selection of a Base Period
Quality Changes
20.7. Quantity Indexes
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Chapter 21. Decision Analysis
21.1. Problem Formulation
Payoff Tables
Decision Trees
21.2. Decision Making with Probabilities
Expected Value Approach
Expected Value of Perfect Information
Exercises: Methods
Exercises: Applications
21.3. Decision Analysis with Sample Information
Decision Tree
Decision Strategy
Expected Value of Sample Information
Exercises: Methods
Exercises: Applications
21.4. Computing Branch Probabilities Using Bayes’ Theorem
Exercises: Methods
Exercises: Applications
Summary
Glossary
Key Formulas
Supplementary Exercises
Case Problem. Lawsuit Defense Strategy
Chapter 22. Sample Survey
22.1. Terminology Used in Sample Surveys
22.2. Types of Surveys and Sampling Methods
22.3. Survey Errors
Nonsampling Error
Sampling Error
22.4. Simple Random Sampling
Population Mean
Population Total
Population Proportion
Determining the Sample Size
Exercises: Methods
Exercises: Applications
22.5. Stratified Simple Random Sampling
Population Mean
Population Total
Population Proportion
Determining the Sample Size
Exercises: Methods
Exercises: Applications
22.6. Cluster Sampling
Population Mean
Population Total
Population Proportion
Determining the Sample Size
Exercises: Methods
Exercises: Applications
22.7. Systematic Sampling
Summary
Glossary
Key Formulas

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