Business Statistics, Global Edition

Name: Business Statistics, Global Edition | Global Edition
Brand: Pearson Education Limited
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Global Edition

David F. Groebner Patrick W. Shannon Phillip C. Fry(Author)

Pearson Education Limited (Publisher)

1st Edition

Published on 27. September 2017

864 pages

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978-1-292-22043-7 (ISBN)

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Content

Cover
Title Page
Copyright Page
About the Authors
Brief Contents
Contents
Preface
Acknowledgments
Preface
Chapter 1: The Where, Why, and How of Data Collection
1.1. What Is Business Statistics?
Descriptive Statistics
Inferential Procedures
1.2. Procedures for Collecting Data
Primary Data Collection Methods
Other Data Collection Methods
Data Collection Issues
1.3. Populations, Samples, and Sampling Techniques
Populations and Samples
Sampling Techniques
1.4. Data Types and Data Measurement Levels
Quantitative and Qualitative Data
Time-Series Data and Cross-Sectional Data
Data Measurement Levels
1.5. A Brief Introduction to Data Mining
Data Mining-Finding the Important, Hidden Relationships in Data
Summary
Key Terms
Chapter Exercises
Chapter 2: Graphs, Charts, and Tables-Describing Your Data
2.1. Frequency Distributions and Histograms
Frequency Distributions
Grouped Data Frequency Distributions
Histograms
Relative Frequency Histograms and Ogives
Joint Frequency Distributions
2.2. Bar Charts, Pie Charts, and Stem and Leaf Diagrams
Bar Charts
Pie Charts
Stem and Leaf Diagrams
2.3. Line Charts, Scatter Diagrams, and Pareto Charts
Line Charts
Scatter Diagrams
Pareto Charts
Summary
Equations
Key Terms
Chapter Exercises
Case 2.1: Server Downtime
Case 2.2: Hudson Valley Apples, Inc.
Case 2.3: Pine River Lumber Company-Part 1
Chapter 3: Describing Data Using Numerical Measures
3.1. Measures of Center and Location
Parameters and Statistics
Population Mean
Sample Mean
The Impact of Extreme Values on the Mean
Median
Skewed and Symmetric Distributions
Mode
Applying the Measures of Central Tendency
Other Measures of Location
Box and Whisker Plots
Developing a Box and Whisker Plot in Excel 2016
Data-Level Issues
3.2. Measures of Variation
Range
Interquartile Range
Population Variance and Standard Deviation
Sample Variance and Standard Deviation
3.3. Using the Mean and Standard Deviation Together
Coefficient of Variation
Tchebysheff's Theorem
Standardized Data Values
Summary
Equations
Key Terms
Chapter Exercises
Case 3.1: SDW-Human Resources
Case 3.2: National Call Center
Case 3.3: Pine River Lumber Company-Part 2
Case 3.4: AJ's Fitness Center
Chapters 1-3: Special Review Section
Chapters 1-3
Exercises
Review Case 1: State Department of Insurance
Term Project Assignments
Chapter 4: Introduction to Probability
4.1. The Basics of Probability
Important Probability Terms
Methods of Assigning Probability
4.2. The Rules of Probability
Measuring Probabilities
Conditional Probability
Multiplication Rule
Bayes' Theorem
Summary
Equations
Key Terms
Chapter Exercises
Case 4.1: Great Air Commuter Service
Case 4.2: Pittsburg Lighting
Chapter 5: Discrete Probability Distributions
5.1. Introduction to Discrete Probability Distributions
Random Variables
Mean and Standard Deviation of Discrete Distributions
5.2. The Binomial Probability Distribution
The Binomial Distribution
Characteristics of the Binomial Distribution
5.3. Other Probability Distributions
The Poisson Distribution
The Hypergeometric Distribution
Summary
Equations
Key Terms
Chapter Exercises
Case 5.1: SaveMor Pharmacies
Case 5.2: Arrowmark Vending
Case 5.3: Boise Cascade Corporation
Chapter 6: Introduction to Continuous Probability Distributions
6.1. The Normal Distribution
The Normal Distribution
The Standard Normal Distribution
Using the Standard Normal Table
6.2. Other Continuous Probability Distributions
The Uniform Distribution
The Exponential Distribution
Summary
Equations
Key Terms
Chapter Exercises
Case 6.1: State Entitlement Programs
Case 6.2: Credit Data, Inc.
Case 6.3: National Oil Company-Part 1
Chapter 7: Introduction to Sampling Distributions
7.1. Sampling Error: What It Is and Why It Happens
Calculating Sampling Error
7.2. Sampling Distribution of the Mean
Simulating the Sampling Distribution for x
The Central Limit Theorem
7.3. Sampling Distribution of a Proportion
Working with Proportions
Sampling Distribution of p
Summary
Equations
Key Terms
Chapter Exercises
Case 7.1: Carpita Bottling Company-Part 1
Case 7.2: Truck Safety Inspection
Chapter 8: Estimating Single Population Parameters
8.1. Point and Confidence Interval Estimates for a Population Mean
Point Estimates and Confidence Intervals
Confidence Interval Estimate for the Population Mean, S Known
Confidence Interval Estimates for the Population Mean, S Unknown
Student's t-Distribution
8.2. Determining the Required Sample Size for Estimating a Population Mean
Determining the Required Sample Size for Estimating M, S Known
Determining the Required Sample Size for Estimating M, S Unknown
8.3. Estimating a Population Proportion
Confidence Interval Estimate for a Population Proportion
Determining the Required Sample Size for Estimating a Population Proportion
Summary
Equations
Key Terms
Chapter Exercises
Case 8.1: Management Solutions, Inc.
Case 8.2: Federal Aviation Administration
Case 8.3: Cell Phone Use
Chapter 9: Introduction to Hypothesis Testing
9.1. Hypothesis Tests for Means
Formulating the Hypotheses
Significance Level and Critical Value
Hypothesis Test for M, S Known
Types of Hypothesis Tests
p-Value for Two-Tailed Tests
Hypothesis Test for M, S Unknown
9.2. Hypothesis Tests for a Proportion
Testing a Hypothesis about a Single Population Proportion
9.3. Type II Errors
Calculating Beta
Controlling Alpha and Beta
Power of the Test
Summary
Equations
Key Terms
Chapter Exercises
Case 9.1: Carpita Bottling Company-Part 2
Case 9.2: Wings of Fire
Chapter 10: Estimation and Hypothesis Testing for Two Population Parameters
10.1. Estimation for Two Population Means Using Independent Samples
Estimating the Difference between Two Population Means When S1 and S2 Are Known, Using Independent Samples
Estimating the Difference between Two Population Means When S1 and S2 Are Unknown, Using Independent Samples
10.2. Hypothesis Tests for Two Population Means Using Independent Samples
Testing for M1 M2 When S1 and S2 Are Known, Using Independent Samples
Testing for M1 M2 When S1 and S2 Are Unknown, Using Independent Samples
10.3. Interval Estimation and Hypothesis Tests for Paired Samples
Why Use Paired Samples?
Hypothesis Testing for Paired Samples
10.4. Estimation and Hypothesis Tests for Two Population Proportions
Estimating the Difference between Two Population Proportions
Hypothesis Tests for the Difference between Two Population Proportions
Summary
Equations
Key Terms
Chapter Exercises
Case 10.1: Larabee Engineering-Part 1
Case 10.2: Hamilton Marketing Services
Case 10.3: Green Valley Assembly Company
Case 10.4: U-Need-It Rental Agency
Chapter 11: Hypothesis Tests and Estimation for Population Variances
11.1. Hypothesis Tests and Estimation for a Single Population Variance
Chi-Square Test for One Population Variance
Interval Estimation for a Population Variance
11.2. Hypothesis Tests for Two Population Variances
F-Test for Two Population Variances
Summary
Equations
Key Term
Chapter Exercises
Case 11.1: Larabee Engineering-Part 2
Chapter 12: Analysis of Variance
12.1. One-Way Analysis of Variance
Introduction to One-Way ANOVA
Partitioning the Sum of Squares
The ANOVA Assumptions
Applying One-Way ANOVA
The Tukey-Kramer Procedure for Multiple Comparisons
Fixed Effects Versus Random Effects in Analysis of Variance
12.2. Randomized Complete Block Analysis of Variance
Randomized Complete Block ANOVA
Fisher's Least Significant Difference Test
12.3. Two-Factor Analysis of Variance with Replication
Two-Factor ANOVA with Replications
A Caution about Interaction
Summary
Equations
Key Terms
Chapter Exercises
Case 12.1: Agency for New Americans
Case 12.2: McLaughlin Salmon Works
Case 12.3: NW Pulp and Paper
Case 12.4: Quinn Restoration
Business Statistics Capstone Project
Chapters 8-12: Special Review Section
Chapters 8-12
Using the Flow Diagrams
Exercises
Chapter 13: Goodness-of-Fit Tests and Contingency Analysis
13.1. Introduction to Goodness-of-Fit Tests
Chi-Square Goodness-of-Fit Test
13.2. Introduction to Contingency Analysis
2 x 2 Contingency Tables
r x c Contingency Tables
Chi-Square Test Limitations
Summary
Equations
Key Term
Chapter Exercises
Case 13.1: National Oil Company-Part 2
Case 13.2: Bentford Electronics-Part 1
Chapter 14: Introduction to Linear Regression and Correlation Analysis
14.1. Scatter Plots and Correlation
The Correlation Coefficient
14.2. Simple Linear Regression Analysis
The Regression Model Assumptions
Meaning of the Regression Coefficients
Least Squares Regression Properties
Significance Tests in Regression Analysis
14.3. Uses for Regression Analysis
Regression Analysis for Description
Regression Analysis for Prediction
Common Problems Using Regression Analysis
Summary
Equations
Key Terms
Chapter Exercises
Case 14.1: A & A Industrial Products
Case 14.2: Sapphire Coffee-Part 1
Case 14.3: Alamar Industries
Case 14.4: Continental Trucking
Chapter 15: Multiple Regression Analysis and Model Building
15.1. Introduction to Multiple Regression Analysis
Basic Model-Building Concepts
15.2. Using Qualitative Independent Variables
15.3. Working with Nonlinear Relationships
Analyzing Interaction Effects
Partial F-Test
15.4. Stepwise Regression
Forward Selection
Backward Elimination
Standard Stepwise Regression
Best Subsets Regression
15.5. Determining the Aptness of the Model
Analysis of Residuals
Corrective Actions
Summary
Equations
Key Terms
Chapter Exercises
Case 15.1: Dynamic Weighing, Inc.
Case 15.2: Glaser Machine Works
Case 15.3: Hawlins Manufacturing
Case 15.4: Sapphire Coffee-Part 2
Case 15.5: Wendell Motors
Chapter 16: Analyzing and Forecasting Time-Series Data
16.1. Introduction to Forecasting and Time-Series Data
General Forecasting Issues
Components of a Time Series
Introduction to Index Numbers
Using Index Numbers to Deflate a Time Series
16.2. Trend-Based Forecasting Techniques
Developing a Trend-Based Forecasting Model
Comparing the Forecast Values to the Actual Data
Nonlinear Trend Forecasting
Adjusting for Seasonality
16.3. Forecasting Using Smoothing Methods
Exponential Smoothing
Forecasting with Excel 2016
Summary
Equations
Key Terms
Chapter Exercises
Case 16.1: Park Falls Chamber of Commerce
Case 16.2: The St. Louis Companies
Case 16.3: Wagner Machine Works
Chapter 17: Introduction to Nonparametric Statistics
17.1. The Wilcoxon Signed Rank Test for One Population Median
The Wilcoxon Signed Rank Test-Single Population
17.2. Nonparametric Tests for Two Population Medians
The Mann-Whitney U-Test
Mann-Whitney U-Test-Large Samples
17.3. Kruskal-Wallis One-Way Analysis of Variance
Limitations and Other Considerations
Summary
Equations
Chapter Exercises
Case 17.1: Bentford Electronics-Part 2
Chapter 18: Introducing Business Analytics
18.1. What Is Business Analytics?
Descriptive Analytics
Predictive Analytics
18.2. Data Visualization Using Microsoft Power BI Desktop
Using Microsoft Power BI Desktop
Summary
Key Terms
Case 18.1: New York City Taxi Trips
Appendices
A: Random Numbers Table
B: Cumulative Binomial Distribution Table
C: Cumulative Poisson Probability Distribution Table
D: Standard Normal Distribution Table
E: Exponential Distribution Table
F: Values of t for Selected Probabilities
G: Values of x2 for Selected Probabilities
H: F-Distribution Table: Upper 5% Probability (or 5% Area) under F-Distribution Curve
I: Distribution of the Studentized Range (q-values)
J: Critical Values of r in the Runs Test
K: Mann-Whitney U Test Probabilities (n * 9)
L: Mann-Whitney U Test Critical Values (9 " n " 20)
M: Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n " 25)
N: Critical Values dL and du of the Durbin-Watson Statistic D (Critical Values Are One-Sided)
O: Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test 808
P: Control Chart Factors
Answers to Selected Odd-Numbered Problems
References
Glossary
Index
Credits
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