### Test Bank for Basic Business Statistics 14th Edition by Mark L. Berenson

#### Test Bank for Basic Business Statistics 14th Edition by Mark L. Berenson

For one- or-two-semester courses in business statistics. Give students the statistical foundation to hone their analysis skills for real-world decisions Basic Business Statistics helps students see the essential role that statistics will play in their future careers by using examples drawn from all functional areas of real-world business. Guided by principles set forth by ASA’s Guidelines for Assessment and Instruction (GAISE) reports and the authors’ diverse teaching experiences, the text continues to innovate and improve the way this course is taught to students. The 14th Edition includes new and updated resources and tools to enhance students’ understanding, and provides the best framework for learning statistical concepts. Also available with MyLab Business Statistics By combining trusted authors’ content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student. Note: You are purchasing a standalone product; MyLab Business Statistics does not come packaged with this content. Students, if interested in purchasing this title with MyLab, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab Business Statistics, search for: 0135168465/ 9780135168462 Basic Business Statistics Plus MyLab Business Statistics with Pearson eText — Access Card Package, 14/e Package consists of: 0134684842 / 9780134684840 Basic Business Statistics, 14/e 0134780604 / 9780134780603 MyLab Business Statistics with Pearson eText — Access Card

A Roadmap for Selecting a Statistical Method

Basic Business Statistics Concepts and Applications

Basic Business Statistics Concepts and Applications

About the Authors

Brief Contents

Contents

Preface

What’s New in this Edition?

Continuing Features that Readers Have Come to Expect

Chapter-by-Chapter Changes Made for this Edition

Serious About Writing Improvements

A Note of Thanks

Contact Us!

First Things First

Contents

Objectives

FTF.1 Think Differently About Statistics

Statistics: A Way of Thinking

DCOVA Framework

Analytical Skills More Important than Arithmetic Skills

Statistics: An Important Part of Your Business Education

FTF.2 Business Analytics: The Changing Face of Statistics

“Big Data”

Unstructured data

FTF.3 Starting Point for Learning Statistics

Statistic

Can Statistics (pl., statistic) Lie?

FTF.4 Starting Point for Using Software

Using Software Properly

Software instruction conventions and notation

References

Key Terms

Excel Guide

EG.1 Getting Started with Excel

EG.2 Entering Data

EG.3 Open or Save a Workbook

EG.4 Working with a Workbook

EG.5 Print a Worksheet

EG.6 Reviewing Worksheets

EG.7 If You Use the Workbook Instructions

JMP Guide

JG.1 Getting Started with JMP

JG.2 Entering Data

JG.3 Create New Project or Data Table

JG.4 Open or Save Files

JG.5 Print Data Tables or Report Windows

JG.6 JMP Script Files

Minitab Guide

MG.1 Getting Started with Minitab

MG.2 Entering Data

MG.3 Open or Save Files

MG.4 Insert or Copy Worksheets

MG.5 Print Worksheets

1 Defining and Collecting Data

Contents

Objectives

1.1 Defining Variables

Solution

Classifying Variables by Type

Measurement Scales

Problems for Section 1.1

Learning the Basics

Applying the Concepts

1.2 Collecting Data

Populations and Samples

Data Sources

Problems for Section 1.2

Applying the Concepts

1.3 Types of Sampling Methods

Simple Random Sample

Systematic Sample

Stratified Sample

Cluster Sample

Problems for Section 1.3

Learning the Basics

Applying the Concepts

1.4 Data Cleaning

Invalid Variable Values

Coding Errors

Data Integration Errors

Missing Values

Algorithmic Cleaning of Extreme Numerical Values

1.5 Other Data Preprocessing Tasks

Data Formatting

Stacking and Unstacking Data

Recoding Variables

Problems for Sections 1.4 and 1.5

Applying the Concepts

1.6 Types of Survey Errors

Coverage Error

Nonresponse Error

Sampling Error

Measurement Error

Ethical Issues About Surveys

Problems for Section 1.6

Applying the Concepts

Summary

References

Key Terms

Checking Your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services

CardioGood Fitness

Clear Mountain State Student Survey

Learning with the Digital Cases

Chapter 1 Excel Guide

EG1.1 Defining Variables

Classifying Variables by Type

EG1.2 Collecting Data

EG1.3 Types of Sampling Methods

Simple Random Sample

Key Technique

Example 1

Workbook

Analysis ToolPak

Example 2

PHStat

Workbook

EG1.4 Data Cleaning

EG1.5 Other Data Preprocessing

Recoding Variables

Key Technique

Example

Workbook

Chapter 1 JMP Guide

JG1.1 Defining Variables

Classifying Variables by Type

JG1.2 Collecting Data

JG1.3 Types of Sampling Methods

Simple Random Sample and Stratified Sample

Systematic Sample

JG1.4 Data Cleaning

JG1.5 Other Preprocessing Tasks

Stacking and Unstacking Variables

Recoding Variables

Chapter 1 Minitab Guide

MG1.1 Defining Variables

Classifying Variables by Type

MG1.2 Collecting Data

MG1.3 Types of Sampling Methods

Simple Random Samples

MG1.4 Data Cleaning

MG1.5 Other Preprocessing Tasks

Recoding Variables

2 Organizing and Visualizing Variables

Contents

Objectives

2.1 Organizing Categorical Variables

The Summary Table

Solution

The Contingency Table

Problems for Section 2.1

Learning the Basics

Applying the Concepts

2.2 Organizing Numerical Variables

The Frequency Distribution

Solution

The Relative Frequency Distribution and the Percentage Distribution

Solution

The Cumulative Distribution

Solution

Problems for Section 2.2

Learning the Basics

Applying the Concepts

2.3 Visualizing Categorical Variables

The Bar Chart

Solution

The Pie Chart and the Doughnut Chart

Solution

The Pareto Chart

Solution

Visualizing Two Categorical Variables

The Side-by-Side Chart

The doughnut chart

Problems for Section 2.3

Applying the Concepts

2.4 Visualizing Numerical Variables

The Stem-and-Leaf Display

The Histogram

Solution

Solution

The Percentage Polygon

Solution

The Cumulative Percentage Polygon (Ogive)

Solution

Problems for Section 2.4

Learning the Basics

Applying the Concepts

2.5 Visualizing Two Numerical Variables

The Scatter Plot

Solution

The Time-Series Plot

Solution

Problems for Section 2.5

Learning the Basics

Applying the Concepts

2.6 Organizing a Mix of Variables

Drill-down

2.7 Visualizing a Mix of Variables

Colored Scatter Plot

Bubble Charts

PivotChart (Excel)

Treemap (Excel, JMP)

Sparklines (Excel)

2.8 Filtering and Querying Data

Excel Slicers

Problems for Sections 2.6 through 2.8

Applying the Concepts

2.9 Pitfalls in Organizing and Visualizing Variables

Obscuring Data

Creating False Impressions

Chartjunk

Problems for Section 2.9

Applying the Concepts

Summary

References

Key Equations

Determining the Class Interval Width

Computing the Proportion or Relative Frequency

Key Terms

Checking Your Understanding

Chapter Review Problems

Report Writing Exercises

Managing Ashland MultiComm Services

Digital Case

CardioGood Fitness

The Choice Is Yours Follow-Up

Clear Mountain State Student Survey

Chapter 2 Excel Guide

EG2.1 Organizing Categorical Variables

The Summary Table

Key Technique

Example

PHStat

Workbook (untallied data)

Workbook (tallied data)

The Contingency Table

Key Technique

Example

PHStat (untallied data)

Workbook (untallied data)

Workbook (tallied data)

EG2.2 Organizing Numerical Variables

The Ordered Array

The Frequency Distribution

Key Technique

Example

PHStat (untallied data)

Workbook (untallied data)

Analysis ToolPak (untallied data)

The Relative Frequency, Percentage, and Cumulative Distributions

Key Technique

Example

PHStat (untallied data)

Workbook (untallied data)

Analysis ToolPak

EG2 Charts Group Reference

EG2.3 Visualizing Categorical Variables

The Bar Chart and the Pie (or Doughnut) Chart

Key Technique

Example

PHStat

Workbook

The Pareto Chart

Key Technique

Example

PHStat

Workbook

The Side-by-Side Chart

Key Technique

Example

PHStat

Workbook

EG2.4 Visualizing Numerical Variables

The Stem-and-Leaf Display

Key Technique

Example

PHStat

Workbook

The Histogram

Key Technique

Example

PHStat

Workbook

Analysis ToolPak Use Histogram.

The Percentage Polygon and the Cumulative Percentage Polygon (Ogive)

Key Technique

Example

PHStat

Workbook

EG2.5 Visualizing Two Numerical Variables

The Scatter Plot

Key Technique

Example

PHStat

Workbook

The Time-Series Plot

Key Technique

Example

Workbook

EG2.6 Organizing a Mix of Variables

Multidimensional Contingency Tables

Key Technique

Example

Workbook

Adding a Numerical Variable

Key Technique

Example

Workbook

EG2.7 Visualizing a Mix of Variables

PivotChart

Key Technique

Example

Workbook

Treemap

Key Technique

Example

Workbook

Sparklines

Key Technique

Example

Workbook

EG2.8 Filtering and Querying Data

Chapter 2 JMP Guide

JG2 JMP Choices for Creating Summaries

JG2.1 Organizing Categorical Variables

The Summary Table (classical)

The Summary Table (interactive)

The Contingency Table

JG2.2 Organizing Numerical Variables

The Ordered Array

The Frequency, Relative Frequency, Percentage, and Cumulative Percentage Distributions

Cumulative Percentages.

Classes

JG2.3 Visualizing Categorical Variables

The Bar Chart or the Pie Chart

The Pareto Chart

Visualizing Two Categorical Variables

JG2.4 Visualizing Numerical Variables

The Stem-and-Leaf Display

The Histogram

The Percentage Polygon and the Cumulative Percentage Polygon (Ogive)

Percentage Polygons.

Cumulative Percentage Polygons.

JG2.5 Visualizing Two Numerical Variables

The Scatter Plot

The Time-Series Plot

JG2.6 Organizing a Mix of Variables

Multidimensional Contingency Table

JG2.7 Visualizing a Mix of Variables

Colored Scatter Plots

Treemap

JG2.8 Filtering and Querying Data

JMP Guide Gallery

Chapter 2 Minitab Guide

MG2.1 Organizing Categorical Variables

The Summary Table

The Contingency Table

MG2.2 Organizing Numerical Variables

The Ordered Array

The Frequency-Distribution

MG2.3 Visualizing Categorical Variables

The Bar Chart and the Pie Chart

The Pareto Chart

The Side-by-Side Chart

MG2.4 Visualizing Numerical Variables

The Stem-and-Leaf Display

The Histogram

The Percentage Polygon the Cumulative Percentage Polygon (Ogive)

MG2.5 Visualizing Two Numerical Variables

The Scatter Plot

The Time-Series Plot

MG2.6 Organizing a Mix of Variables

Multidimensional Contingency Tables

Multidimensional Contingency Table With a Numerical Variable

MG2.7 Visualizing a Mix of Variables

Colored Scatter Plots

MG2.8 Filtering and Querying Data

3 Numerical Descriptive Measures

Contents

Objectives

3.1 Measures of Central Tendency

The Mean

Solution

The Median

Solution

The Mode

Solution

The Geometric Mean

Solution

3.2 Measures of Variation and Shape

The Range

Solution

The Variance and the Standard Deviation

Solution

The Coefficient of Variation

Solution

Z Scores

Solution

Shape: Skewness

Shape: Kurtosis

Solution

Solution

Problems for Sections 3.1 and 3.2

Learning the Basics

Applying the Concepts

3.3 Exploring Numerical Variables

Quartiles

Percentiles

Solution

The Interquartile Range

Solution

The Five-Number Summary

Solution

The Boxplot

Solution

Problems for Section 3.3

Learning the Basics

Applying the Concepts

3.4 Numerical Descriptive Measures for a Population

The Population Mean

The Population Variance and Standard Deviation

The Empirical Rule

Solution

Chebyshev’s Theorem

Solution

Problems for Section 3.4

Learning the Basics

Applying the Concepts

3.5 The Covariance and the Coefficient of Correlation

The Covariance

Solution

The Coefficient of Correlation

Solution

Problems for Section 3.5

Learning the Basics

Applying the Concepts

3.6 Descriptive Statistics: Pitfalls and Ethical Issues

Summary

References

Key Equations

Sample Mean

Median

Geometric Mean

Geometric Mean Rate of Return

Range

Sample Variance

Sample Standard Deviation

Coefficient of Variation

Z Score

First Quartile, Q1

Third Quartile, Q3

Interquartile Range

Population Mean

Population Variance

Population Standard Deviation

Sample Covariance

Sample Coefficient of Correlation

Key Terms

Checking your Understanding

Chapter Review Problems

Report Writing Exercises

Managing Ashland MultiComm Services

Digital Case

CardioGood Fitness

More Descriptive Choices Follow-up

Clear Mountain State Student Survey

Chapter 3 Excel Guide

EG3.1 Measures of Central Tendency

The Mean, Median, and Mode

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

The Geometric Mean

Key Technique

Example

Workbook

EG3.2 Measures of Variation and Shape

The Range

Key Technique

Example

PHStat

Workbook

The Variance, Standard Deviation, Coefficient of Variation, and Z Scores

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

Shape: Skewness and Kurtosis

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

EG3.3 Exploring Numerical Variables

Quartiles

Key Technique

Example

PHStat

Workbook

The Interquartile Range

Key Technique

Example

Workbook

The Five-Number Summary and the Boxplot

Key Technique

Example

PHStat

Workbook

EG3.4 Numerical Descriptive Measures for a Population

The Population Mean, Population Variance, and Population Standard Deviation

Key Technique

Example

Workbook

The Empirical Rule and Chebyshev’s Theorem

EG3.5 The Covariance and the Coefficient of Correlation

The Covariance

Key Technique

Example

Workbook

The Coefficient of Correlation

Key Technique

Example

Workbook

Chapter 3 JMP Guide

JG3.1 Measures of Central Tendency

The Mean, Median, and Mode

The Geometric Mean

JG3.2 Measures of Variation and Shape

The Range, Variance, Standard Deviation, Coefficient of Variation, Skewness, and Kurtosis

Z Scores

JG3.3 Exploring Numerical Variables

Quartiles, the Interquartile Range, the Five-Number Summary, and the Boxplot

The Boxplot (second form)

JG3.4 Numerical Descriptive Measures for a Population

The Population Mean, Population Variance, and Population Standard Deviation

The Empirical Rule and the Chebyshev Rule

JG3.5 The Covariance and the Coefficient of Correlation

The Covariance and the Coefficient of Correlation

Chapter 3 Minitab Guide

MG3.1 Measures of Central Tendency

The Mean, Median, and Mode

The Geometric Mean

MG3.2 Measures of Variation and Shape

The Range, Variance, Standard Deviation, Coefficient of Variation, Skewness, and Kurtosis

Z Scores

MG3.3 Exploring Numerical Variables

Quartiles, the Interquartile Range, and the Five-Number Summary

The Boxplot

MG3.4 Numerical Descriptive Measures for a Population

The Population Mean, Population Variance, and Population Standard Deviation

The Empirical Rule and the Chebyshev Rule

MG3.5 The Covariance and the Coefficient of Correlation

The Covariance

The Coefficient of Correlation

4 Basic Probability

Contents

Objectives

4.1 Basic Probability Concepts

Events and Sample Spaces

Types of Probability

Solution

Summarizing Sample Spaces

Solution

Simple Probability

Solution

Joint Probability

Solution

Marginal Probability

General Addition Rule

Solution

Problems for Section 4.1

Learning the Basics

Applying the Concepts

4.2 Conditional Probability

Computing Conditional Probabilities

Solution

Decision Trees

Solution

Independence

Solution

Multiplication Rules

Solution

Marginal Probability Using the General Multiplication Rule

Problems for Section 4.2

Learning the Basics

Applying the Concepts

4.3 Ethical Issues and Probability

4.4 Bayes’ Theorem

Problems for Section 4.4

Learning the Basics

Applying the Concepts

4.5 Counting Rules

Summary

References

Key Equations

Probability of Occurrence

Marginal Probability

General Addition Rule

Conditional Probability

Independence

General Multiplication Rule

Multiplication Rule for Independent Events

Marginal Probability Using the General Multiplication Rule

Bayes’ Theorem

Counting Rule 1

Counting Rule 2

Counting Rule 3

Counting Rule 4: Permutations

Counting Rule 5: Combinations

Key Terms

Checking Your Understanding

Chapter Review Problems

Digital Case

CardioGood Fitness

The Choice Is Yours Follow-Up

Clear Mountain State Student Survey

Chapter 4 Excel Guide

EG4.1 Basic Probability Concepts

Simple Probability, Joint Probability, and the General Addition Rule

Key Technique

Example

PHStat

Workbook

EG4.4 Bayes’ Theorem

Key Technique

Example

Workbook

EG4.5 Counting Rules

Counting Rule 1

Counting Rule 2

Counting Rule 3

Counting Rule 4

Counting Rule 5

Chapter 4 JMP Guide

JG4.4 Bayes’ Theorem

Chapter 4 Minitab Guide

MG4.5 Counting Rules

Counting Rule 1

Counting Rule 2

Counting Rule 3

Counting Rule 4

Counting Rule 5

5 Discrete Probability Distributions

Contents

Objectives

5.1 The Probability Distribution for a Discrete Variable

Expected Value of a Discrete Variable

Variance and Standard Deviation of a Discrete Variable

Problems for Section 5.1

Learning the Basics

Applying the Concepts

5.2 Binomial Distribution

Solution

Solution

Solution

Histograms for Discrete Variables

Summary Measures for the Binomial Distribution

Solution

Problems for Section 5.2

Learning the Basics

Applying the Concepts

5.3 Poisson Distribution

Solution

Problems for Section 5.3

Learning the Basics

Applying the Concepts

5.4 Covariance of a Probability Distribution and Its Application in Finance

5.5 Hypergeometric Distribution

5.6 Using the Poisson Distribution to Approximate the Binomial Distribution

Summary

References

Key Equations

Expected Value, μ of a Discrete Variable

Variance of a Discrete Variable

Standard Deviation of a Discrete Variable

Combinations

Binomial Distribution

Mean of the Binomial Distribution

Standard Deviation of the Binomial Distribution

Poisson Distribution

Key Terms

Checking Your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services

Digital Case

Chapter 5 Excel Guide

EG5.1 The Probability Distribution for a Discrete Variable

EG5.2 Binomial Distribution

EG5.3 Poisson Distribution

Chapter 5 JMP Guide

JG5.1 The Probability Distribution for a Discrete Variable

Expected Value of a Discrete Variable

JG5.2 Binomial Distribution

JG5.3 Poisson Distribution

Chapter 5 Minitab Guide

MG5.1 The Probability Distribution for a Discrete Variable

Expected Value of a Discrete Variable

MG5.2 Binomial Distribution

MG5.3 Poisson Distribution

6 The Normal Distribution and Other Continuous Distributions

Contents

Objectives

6.1 Continuous Probability Distributions

6.2 The Normal Distribution

Role of the Mean and the Standard Deviation

Calculating Normal Probabilities

Solution

Solution

Solution

Finding X Values

Solution

Problems for Section 6.2

Learning the Basics

Applying the Concepts

6.3 Evaluating Normality

Comparing Data Characteristics to Theoretical Properties

Constructing the Normal Probability Plot

Problems for Section 6.3

Learning the Basics

Applying the Concepts

6.4 The Uniform Distribution

Solution

Problems For Section 6.4

Learning the Basics

Applying the Concepts

6.5 The Exponential Distribution

6.6 The Normal Approximation to the Binomial Distribution

Summary

References

Key Equations

Normal Probability Density Function

Z Transformation Formula

Finding an X Value Associated with a Known Probability

Uniform Probability Density Function

Mean of the Uniform Distribution

Variance and Standard Deviation of the Uniform Distribution

Key Terms

Checking your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services

CardioGood Fitness

More Descriptive Choices Follow-up

Clear Mountain State Student Survey

Digital Case

Chapter 6 Excel Guide

EG6.2 The Normal Distribution

EG6.3 Evaluating Normality

Comparing Data Characteristics to Theoretical Properties

Constructing the Normal Probability Plot

Key Technique

Example

PHStat

Workbook

Chapter 6 JMP Guide

JG6.2 The Normal Distribution

Finding X Values

JG6.3 Evaluating Normality

Comparing Data Characteristics to Theoretical Properties

Constructing the Normal Probability Plot

Chapter 6 Minitab Guide

MG6.2 The Normal Distribution

Finding X Values

MG6.3 Evaluating Normality

Comparing Data Characteristics to Theoretical Properties

Constructing the Normal Probability Plot

7 Sampling Distributions

Contents

Objectives

7.1 Sampling Distributions

7.2 Sampling Distribution of the Mean

The Unbiased Property of the Sample Mean

Standard Error of the Mean

Solution

Sampling from Normally Distributed Populations

Solution

Solution

Solution

Sampling from Non-normally Distributed Populations—The Central Limit Theorem

Solution

Problems for Section 7.2

Learning the Basics

Applying the Concepts

7.3 Sampling Distribution of the Proportion

Problems for Section 7.3

Learning the Basics

Applying the Concepts

7.4 Sampling from Finite Populations

Summary

References

Key Equations

Population Mean

Population Standard Deviation

Standard Error of the Mean

Finding Z for the Sampling Distribution of the Mean

Finding X̄ for the Sampling Distribution of the Mean

Sample Proportion

Standard Error of the Proportion

Finding Z for the Sampling Distribution of the Proportion

Key Terms

Checking Your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services

Digital Case

Chapter 7 Excel Guide

EG7.2 Sampling Distribution of the Mean

Chapter 7 JMP Guide

JG7.2 Sampling Distribution of the Mean

Sampling from Normally Distributed Populations

Chapter 7 Minitab Guide

MG7.2 Sampling Distribution of the Mean

Sampling from Normally Distributed Populations

8 Confidence Interval Estimation

Contents

Objectives

8.1 Confidence Interval Estimate for the Mean (σ Known)

Sampling Error

Solution

Solution

Can You Ever Know the Population Standard Deviation?

Problems for Section 8.1

Learning the Basics

Applying the Concepts

8.2 Confidence Interval Estimate for the Mean (σ Unknown)

Student’s t Distribution

The Concept of Degrees of Freedom

Properties of the t Distribution

The Confidence Interval Statement

Solution

Problems for Section 8.2

Learning the Basics

Applying the Concepts

8.3 Confidence Interval Estimate for the Proportion

Solution

Problems for Section 8.3

Learning the Basics

Applying the Concepts

8.4 Determining Sample Size

Sample Size Determination for the Mean

Solution

Sample Size Determination for the Proportion

Solution

Problems for Section 8.4

Learning the Basics

Applying the Concepts

8.5 Confidence Interval Estimation and Ethical Issues

8.6 Application of Confidence Interval Estimation in Auditing

8.7 Estimation and Sample Size Estimation for Finite Populations

8.8 Bootstrapping

Summary

References

Key Equations

Confidence Interval for the Mean (σ known).

Confidence Interval for the Mean (σ unknown)

Confidence Interval Estimate for the Proportion

Sample Size Determination for the Mean

Sample Size Determination for the Proportion

Key Terms

Checking Your Understanding

Chapter Review Problems

Report Writing Exercise

Managing Ashland MultiComm Services

Digital Case

Sure Value Convenience Stores

CardioGood Fitness

More Descriptive Choices Follow-Up

Clear Mountain State Student Survey

Chapter 8 Excel Guide

EG8.1 Confidence Interval Estimate for the Mean (σ Known)

Key Technique

Example

PHStat

Workbook

EG8.2 Confidence Interval Estimate for the Mean (σ Unknown)

Key Technique

Example

PHStat

Workbook

EG8.3 Confidence Interval Estimate for the Proportion

Key Technique

Example

PHStat

Workbook

EG8.4 Determining Sample Size

Sample Size Determination for the Mean

Key Technique

Example

PHStat

Workbook

Sample Size Determination for the Proportion

Key Technique

Example

PHStat

Workbook

Chapter 8 JMP Guide

JG8.1 Confidence Interval Estimate for the Mean (σ Known)

JG8.2 Confidence Interval Estimate for the Mean (σ Unknown)

JG8.3 Confidence Interval Estimate for the Proportion

JG8.4 Determining Sample Size

Sample Size Determination for the Mean

Sample Size Determination for the Proportion

Chapter 8 Minitab Guide

MG8.1 Confidence Interval Estimate for the Mean (σ Known)

MG8.2 Confidence Interval Estimate for the Mean (σ Unknown)

MG8.3 Confidence Interval Estimate for the Proportion

MG8.4 Determining Sample Size

Sample Size Determination for the Mean

Sample Size Determination for the Proportion

9 Fundamentals of Hypothesis Testing: One-Sample Tests

Contents

Objectives

9.1 Fundamentals of Hypothesis Testing

Solution

The Critical Value of the Test Statistic

Regions of Rejection and Nonrejection

Risks in Decision Making Using Hypothesis Testing

Z Test for the Mean (σ known)

Hypothesis Testing Using the Critical Value Approach

Solution

Solution

Hypothesis Testing Using the p-Value Approach

Solution

A Connection Between Confidence Interval Estimation and Hypothesis Testing

Can You Ever Know the Population Standard Deviation?

Problems for Section 9.1

Learning the Basics

Applying the Concepts

9.2 t Test of Hypothesis for the Mean (σ Unknown)

The Critical Value Approach

p-Value Approach

Checking the Normality Assumption

Problems for Section 9.2

Learning the Basics

Applying the Concepts

9.3 One-Tail Tests

The Critical Value Approach

The p-Value Approach

Solution

Problems for Section 9.3

Learning the Basics

Applying the Concepts

9.4 Z Test of Hypothesis for the Proportion

The Critical Value Approach

The p-Value Approach

Solution

Problems for Section 9.4

Learning the Basics

Applying the Concepts

9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues

Statistical Significance Versus Practical Significance

Statistical Insignificance Versus Importance

Reporting of Findings

Ethical Issues

9.6 Power of the Test

Summary

References

Key Equations

Z Test for the Mean (σ Known)

t Test for the Mean (σ Unknown)

Z Test for the Proportion

Z Test for the Proportion in Terms of the Number of Events of Interest

Key Terms

Checking Your Understanding

Chapter Review Problems

Report Writing Exercise

Managing Ashland MultiComm Services

Digital Case

Sure Value Convenience Stores

Chapter 9 Excel Guide

EG9.1 Fundamentals of Hypothesis Testing

EG9.2 t Test of Hypothesis for the Mean (σ Unknown)

EG9.3 One-Tail Tests

EG9.4 Z Test of Hypothesis for the Proportion

PHStat

Workbook

Chapter 9 JMP Guide

JG9.1 Fundamentals of Hypothesis Testing

JG9.2 t Test of Hypothesis for the Mean (σ Unknown)

JG9.3 One-Tail Tests

JG9.4 Z Test of Hypothesis for the Proportion

Chapter 9 Minitab Guide

MG9.1 Fundamentals of Hypothesis Testing

MG9.2 t Test of Hypothesis for the Mean (σ Unknown)

MG9.3 One-Tail Tests

MG9.4 Z Test of Hypothesis for the Proportion

10 Two-Sample Tests

Contents

Objectives

10.1 Comparing the Means of Two Independent Populations

Pooled-Variance t Test for the Difference Between Two Means Assuming Equal Variances

Evaluating the Normality Assumption

Solution

Confidence Interval Estimate for the Difference Between Two Means

Separate-Variance t Test for the Difference Between Two Means, Assuming Unequal Variances

Problems for Section 10.1

Learning the Basics

Applying the Concepts

10.2 Comparing the Means of Two Related Populations

Paired t Test

Solution

Confidence Interval Estimate for the Mean Difference

Problems for Section 10.2

Learning the Basics

Applying the Concepts

10.3 Comparing the Proportions of Two Independent Populations

Z Test for the Difference Between Two Proportions

Solution

Confidence Interval Estimate for the Difference Between Two Proportions

Problems for Section 10.3

Learning the Basics

Applying the Concepts

10.4 F Test for the Ratio of Two Variances

Solution

Problems for Section 10.4

Learning the Basics

Applying the Concepts

10.5 Effect Size

Summary

References

Key Equations

Pooled-Variance t Test for the Difference Between Two Means

Confidence Interval Estimate for the Difference Between the Means of Two Independent Populations

Paired t Test for the Mean Difference

Confidence Interval Estimate for the Mean Difference

Z Test for the Difference Between Two Proportions

Confidence Interval Estimate for the Difference Between Two Proportions

F Test Statistic for Testing the Ratio of Two Variances

Key Terms

Checking your Understanding

Chapter Review Problems

Report Writing Exercise

Managing Ashland MultiComm Services

Digital Case

Sure Value Convenience Stores

CardioGood Fitness

More Descriptive Choices Follow-Up

Clear Mountain State Student Survey

Chapter 10 Excel Guide

EG10.1 Comparing the Means of Two Independent Populations

Pooled-Variance t Test for the Difference Between Two Means

PHStat

Workbook

Analysis ToolPak

Confidence Interval Estimate for the Difference Between Two Means

PHStat

Workbook

Separate-Variance t Test for the Difference Between Two Means, Assuming Unequal Variances

PHStat

Workbook

Analysis ToolPak

EG10.2 Comparing the Means of Two Related Populations

Paired t Test

PHStat

Workbook

Analysis ToolPak

EG10.3 Comparing the Proportions of Two Independent Populations

Z Test for the Difference Between Two Proportions

PHStat

Workbook

Confidence Interval Estimate for the Difference Between Two Proportions

PHStat

Workbook

EG10.4 F Test for the Ratio of Two Variances

Chapter 10 JMP Guide

JG10.1 Comparing the Means of Two Independent Populations

Pooled-Variance t Test for the Difference Between Two Means

Confidence Interval Estimate for the Difference Between Two Means

Separate-Variance t Test for the Difference Between Two Means, Assuming Unequal Variances

JG10.2 Comparing the Means of Two Related Populations

Paired t Test

JG10.3 Comparing the Proportions of Two Independent Populations

Z Test for the Difference Between Two Proportions

JG10.4 F Test for the Ratio of Two Variances

Chapter 10 Minitab Guide

MG10.1 Comparing the Means of Two Independent Populations

Pooled-Variance t Test for the Difference Between Two Means

Confidence Interval Estimate for the Difference Between Two Means

Separate-Variance t Test for the Difference Between Two Means, Assuming Unequal Variances

MG10.2 Comparing the Means of Two Related Populations

Paired t Test

Confidence Interval Estimate for the Mean Difference

MG10.3 Comparing the Proportions of Two Independent Populations

Z Test for the Difference Between Two Proportions

Confidence Interval Estimate for the Difference Between Two Proportions

MG10.4 F Test for the Ratio of Two Variances

11 Analysis of Variance

Contents

Objectives

11.1 The Completely Randomized Design: One-Way ANOVA

Analyzing Variation in One-Way ANOVA

F Test for Differences Among More Than Two Means

One-Way ANOVA F Test Assumptions

Levene Test for Homogeneity of Variance

Multiple Comparisons: The Tukey-Kramer Procedure

Solution

The Analysis of Means (ANOM)

Problems for Section 11.1

Learning the Basics

Applying the Concepts

11.2 The Factorial Design: Two-Way ANOVA

Factor and Interaction Effects

Testing for Factor and Interaction Effects

Multiple Comparisons: The Tukey Procedure

Visualizing Interaction Effects: The Cell Means Plot

Interpreting Interaction Effects

Solution

Problems for Section 11.2

Learning the Basics

Applying the Concepts

11.3 The Randomized Block Design

11.4 Fixed Effects, Random Effects, and Mixed Effects Models

Summary

References

Key Equations

Total Variation in One-Way ANOVA

Among-Group Variation in One-Way ANOVA

Within-Group Variation in One-Way ANOVA

Mean Squares in One-Way ANOVA

One-Way ANOVA FSTAT Test Statistic

Critical Range for the Tukey-Kramer Procedure

Total Variation in Two-Way ANOVA

Factor A Variation in Two-Way ANOVA

Factor B Variation in Two-Way ANOVA

Interaction Variation in Two-Way ANOVA

Random Variation in Two-Way ANOVA

Mean Squares in Two-Way ANOVA

F Test for Factor A Effect

F Test for Factor B Effect

F Test for Interaction Effect

Critical Range for Factor A

Critical Range for Factor B

Key Terms

Checking Your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services Phase 1

Phase 2

Digital Case

Sure Value Convenience Stores

CardioGood Fitness

More Descriptive Choices Follow-Up

Clear Mountain State Student Survey

Chapter 11 Excel Guide

EG11.1 The Completely Randomized Design: One-Way Anova

Analyzing Variation in One-Way ANOVA

Key Technique

F Test for Differences Among More Than Two Means

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

Levene Test for Homogeneity of Variance

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

Multiple Comparisons: The Tukey-Kramer Procedure

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

EG11.2 The Factorial Design: Two-Way Anova

Visualizing Interaction Effects: The Cell Means Plot

Key Technique

Example

PHStat

Analysis ToolPak

Workbook

Chapter 11 JMP Guide

JG11.1 The Completely Randomized Design: One-Way Anova

Analyzing Variation in One-Way ANOVA

F Test for Differences Among More Than Two Means

Levene Test for Homogeneity of Variance

Multiple Comparisons: The Tukey-Kramer Procedure

JG11.2 The FACTORIAL DESIGN: TWO-WAY ANOVA

Visualizing Interaction Effects: The Cell Means Plot

Chapter 11 Minitab Guide

MG11.1 The Completely Randomized Design: One-Way Anova

Analyzing Variation in One-Way ANOVA

F Test for Differences Among More Than Two Means

Multiple Comparisons: The Tukey-Kramer Procedure

Levene Test for Homogeneity of Variance

MG11.2 The Factorial Design: Two-Way Anova

Visualizing Interaction Effects: The Cell Means Plot

12 Chi-Square and Nonparametric Tests

Contents

Objectives

12.1 Chi-Square Test for the Difference Between Two Proportions

Solution

Assumptions of the chi-square test

Interrelationship of the standardized normal distribution and the chi-square distribution

Problems for Section 12.1

Learning the Basics

Applying the Concepts

12.2 Chi-Square Test for Differences Among More Than Two Proportions

Solution

Assumptions of the chi-square test for the 2 × c contingency table

The Marascuilo Procedure

The Analysis of Proportions (ANOP)

Problems for Section 12.2

Learning the Basics

Applying the Concepts

12.3 Chi-Square Test of Independence

Assumptions of the chi-square test of independence

Problems for Section 12.3

Learning the Basics

Applying the Concepts

12.4 Wilcoxon Rank Sum Test for Two Independent Populations

Problems for Section 12.4

Learning the Basics

Applying the Concepts

12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA

Assumptions of the Kruskal-Wallis Rank Test

Problems for Section 12.5

Learning the Basics

Applying the Concepts

12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)

12.7 Chi-Square Test for the Variance or Standard Deviation

12.8 Wilcoxon Signed Ranks Test for Two Related Populations

12.9 Friedman Rank Test for the Randomized Block Design

Summary

References

Key Equations

χ2 Test for the Difference Between Two Proportions

Computing the Estimated Overall Proportion for Two Groups

Computing the Estimated Overall Proportion for c Groups

Critical Range for the Marascuilo Procedure

Computing the Expected Frequency

Checking the Rankings

Large-Sample Wilcoxon Rank Sum Test

Kruskal-Wallis Rank Test for Differences Among c Medians

Key Terms

Checking your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services PHASE 1

Phase 2

Digital Case

Sure Value Convenience Stores

CardioGood Fitness

More Descriptive Choices Follow-Up

Clear Mountain State Student Survey

Chapter 12 Excel Guide

EG12.1 Chi-Square Test For the Difference Between Two Proportions

EG12.2 Chi-Square Test for Differences Among More Than Two Proportions

The Marascuilo Procedure

Key Technique

Example

PHStat

Workbook

EG12.3 Chi-Square Test of Independence

EG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method For Two Independent Populations

EG12.5 Kruskal-Wallis Rank Test: A Nonparametric Method For The One-Way Anova

Chapter 12 JMP Guide

JG12.1 Chi-Square Test For the Difference Between Two Proportions

JG12.2 Chi-Square Test For Difference Among More Than Two Proportions

The Marascuilo Procedure

JG12.3 Chi-Square Test of Independence

JG12.4 Wilcoxon Rank Sum Test for Two Independent Populations

JG12.5 Kruskal-Wallis Rank Test For The One-Way Anova

Chapter 12 Minitab Guide

MG12.1 Chi-Square Test for the Difference Between Two Proportions

MG12.2 Chi-Square Test for Differences Among More Than Two Proportions

The Marascuilo Procedure

MG12.3 Chi-Square Test of Independence

MG12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for Two Independent Populations

MG12.5 Kruskal-Wallis Rank Test: A Nonparametric Method For The One-Way Anova

13 Simple Linear Regression

Contents

Objectives

13.1 Simple Linear Regression Models

13.2 Determining the Simple Linear Regression Equation

The Least-Squares Method

Solution

Solution

Predictions in Regression Analysis: Interpolation Versus Extrapolation

Computing the Y Intercept, b0 and the Slope, b1

Solution

Problems for Section 13.2

Learning the Basics

Applying the Concepts

13.3 Measures of Variation

Computing the Sum of Squares

The Coefficient of Determination

Solution

Standard Error of the Estimate

Problems for Section 13.3

Learning the Basics

Applying the Concepts

13.4 Assumptions of Regression

13.5 Residual Analysis

Evaluating the Assumptions

Linearity

Independence

Normality

Equal Variance

Problems for Section 13.5

Learning the Basics

Applying the Concepts

13.6 Measuring Autocorrelation: The Durbin-Watson Statistic

Residual Plots to Detect Autocorrelation

The Durbin-Watson Statistic

Problems for Section 13.6

Learning the Basics

Applying the Concepts

13.7 Inferences About the Slope and Correlation Coefficient

t Test for the Slope

F Test for the Slope

Confidence Interval Estimate for the Slope

t Test for the Correlation Coefficient

Problems for Section 13.7

Learning the Basics

Applying the Concepts

13.8 Estimation of Mean Values and Prediction of Individual Values

The Confidence Interval Estimate for the Mean Response

The Prediction Interval for an Individual Response

Problems for Section 13.8

Learning the Basics

Applying the Concepts

13.9 Potential Pitfalls in Regression

Summary

References

Key Equations

Simple Linear Regression Model

Simple Linear Regression Equation: The Prediction Line

Computational Formula for the Slope, b1

Computational Formula for the Y Intercept, b0

Measures of Variation in Regression

Total Sum of Squares (SST)

Regression Sum of Squares (SSR)

Error Sum of Squares (SSE)

Coefficient of Determination

Computational Formula for SST

Computational Formula for SSR

Computational Formula for SSE

Standard Error of the Estimate

Residual

Durbin-Watson Statistic

Testing a Hypothesis for a Population Slope, β1, Using the t Test

Testing a Hypothesis for a Population Slope, β1, Using the F Test

Confidence Interval Estimate of the Slope, β1

Testing for the Existence of Correlation

Confidence Interval Estimate for the Mean of Y

Prediction Interval for an Individual Response, Y

Key Terms

Checking your Understanding

Chapter Review Problems

Report Writing Exercise

Managing Ashland MultiComm Services

Digital Case

Brynne Packaging

Chapter 13 Excel Guide

EG13.2 Determining the Simple Linear Regression Equation

Key Technique

Example

PHStat

Workbook

Scatter Plot

Analysis ToolPak

EG13.3 Measures of Variation

EG13.4 Assumptions of Regression

EG13.5 Residual Analysis

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

EG13.6 Measuring Autocorrelation: the Durbin-Watson Statistic

Key Technique

Example

PHStat

Workbook

EG13.7 Inferences about the Slope and Correlation Coefficient

EG13.8 Estimation of Mean Values and Prediction of Individual Values

Key Technique

Example

PHStat

Workbook

Chapter 13 JMP Guide

JG13.2 Determining the Simple Linear Regression Equation

JG13.3 Measures of Variation

JG13.4 Assumptions of Regression

JG13.5 Residual Analysis

JG13.6 Measuring Autocorrelation: the Durbin-Watson Statistic

JG13.7 Inferences about the Slope and Correlation Coefficient

JG13.8 Estimation of Mean Values and Prediction of Individual Values

Chapter 13 Minitab Guide

MG13.2 Determining the Simple Linear Regression Equation

MG13.3 Measures of Variation

MG13.4 Assumptions of Regression

MG13.5 Residual Analysis

MG13.6 Measuring Autocorrelation: The Durbin-Watson Statistic

MG13.7 Inferences about the Slope and Correlation Coefficient

MG13.8 Estimation of Mean Values and Prediction of Individual Values

14 Introduction to Multiple Regression

Contents

Objectives

14.1 Developing a Multiple Regression Model

Interpreting the Regression Coefficients

Predicting the Dependent Variable Y

Problems for Section 14.1

Learning the Basics

Applying the Concepts

14.2 r2, Adjusted r2, and the Overall F Test

Coefficient of Multiple Determination

Adjusted r2

Test for the Significance of the Overall Multiple Regression Model

Problems for Section 14.2

Learning the Basics

Applying the Concepts

14.3 Multiple Regression Residual Analysis

Problems for Section 14.3

Applying the Concepts

14.4 Inferences About the Population Regression Coefficients

Tests of Hypothesis

Solution

Confidence Interval Estimation

Solution

Problems for Section 14.4

Learning the Basics

Applying the Concepts

14.5 Testing Portions of the Multiple Regression Model

Coefficients of Partial Determination

Problems for Section 14.5

Learning the Basics

Applying the Concepts

14.6 Using Dummy Variables and Interaction Terms

Solution

Interactions

Solution

Solution

Problems for Section 14.6

Learning the Basics

Applying the Concepts

14.7 Logistic Regression

Problems for Section 14.7

Learning the Basics

Applying the Concepts

14.8 Influence Analysis

Summary

References

Key Equations

Multiple Regression Model with k Independent Variables

Multiple Regression Model with Two Independent Variables

Multiple Regression Equation with Two Independent Variables

Coefficient of Multiple Determination

Adjusted r2

Overall F Test

Testing for the Slope in Multiple Regression

Confidence Interval Estimate for the Slope

Determining the Contribution of an Independent Variable to the Regression Model

Contribution of Variable X1, Given That X2 Has Been Included

Contribution of Variable X2, Given That X1 Has Been Included

Partial F Test Statistic

Relationship Between a t Statistic and an F Statistic

Coefficients of Partial Determination for a Multiple Regression Model Containing Two Independent Variables

Coefficient of Partial Determination for a Multiple Regression Model Containing k Independent Variables

Odds Ratio

Logistic Regression Model

Logistic Regression Equation

Estimated Odds Ratio

Estimated Probability of an Event of Interest

Key Terms

Checking Your Understanding

Chapter Review Problems

Managing Ashland MultiComm Services

Digital Case

Chapter 14 Excel Guide

EG14.1 Developing a Multiple Regression Model

Interpreting the Regression Coefficients

Key Technique

Example

PHStat

Workbook

Analysis ToolPak

Predicting the Dependent Variable Y

Key Technique

Example

PHStat

Workbook

EG14.2 r2, ADJUSTED r2, and the OVERALL F TEST

EG14.3 Multiple Regression Residual Analysis

EG14.4 Inferences about the Population Regression Coefficients

EG14.5 Testing Portions of the Multiple Regression Model

EG14.6 Using Dummy Variables and Interaction Terms

Dummy Variables

Key Technique

Example

Workbook

Interactions

EG14.7 Logistic Regression

Chapter 14 JMP Guide

JG14.1 Developing a Multiple Regression Model

Interpreting the Regression Coefficients

Predicting the Dependent Variable Y

JG14.2 r2, Adjusted r2, and the Overall F Test Measures of Variation

JG14.3 Multiple Regression Residual Analysis

JG14.4 Inferences about the Population

JG14.5 Testing Portions of the Multiple Regression Model

JG14.6 Using Dummy Variables and Interaction Terms

Dummy Variables

Interaction Terms

JG14.7 Logistic Regression

Chapter 14 Minitab Guide

MG14.1 Developing a Multiple Regression Model

Interpreting the Regression Coefficients

Predicting the Dependent Variable Y

MG14.2 r2, Adjusted r2, and the Overall F Test

MG14.3 Multiple Regression Residual Analysis

MG14.4 Inferences about the Population Regression Coefficients

MG14.5 Testing Portions of the Multiple Regression Model

MG14.6 Using Dummy Variables and Interaction Terms in Regression Models

Dummy Variables

Interactions

MG14.7 Logistic Regression

MG14.8 Influence Analysis

15 Multiple Regression Model Building

Contents

Objectives

15.1 The Quadratic Regression Model

Finding the Regression Coefficients and Predicting Y

Testing for the Significance of the Quadratic Model

Testing the Quadratic Effect

Solution

The Coefficient of Multiple Determination

Problems for Section 15.1

Learning the Basics

Applying the Concepts

15.2 Using Transformations in Regression Models

The Square-Root Transformation

Solution

The Log Transformation

Solution

Problems for Section 15.2

Learning the Basics

Applying the Concepts

15.3 Collinearity

Problems for Section 15.3

Learning the Basics

Applying the Concepts

15.4 Model Building

The Stepwise Regression Approach to Model Building

The Best Subsets Approach to Model Building

Solution

Model Validation

Problems for Section 15.4

Learning the Basics

Applying the Concepts

15.5 Pitfalls in Multiple Regression and Ethical Issues

Pitfalls in Multiple Regression

Ethical Issues

Summary

References

Key Equations

Quadratic Regression Model

Quadratic Regression Equation

Regression Model with a Square-Root Transformation

Original Multiplicative Model

Transformed Multiplicative Model

Original Exponential Model

Transformed Exponential Model

Variance Inflationary Factor

Cp Statistic

Key Terms

Checking your Understanding

Chapter Review Problems

Report Writing Exercise

The Mountain States Potato Company

Sure Value Convenience Stores

Digital Case

The Craybill Instrumentation Company Case

More Descriptive Choices Follow-Up

Chapter 15 Excel Guide

EG15.1 The Quadratic Regression Model

Key Technique

Example

PHStat, Workbook, and Analysis ToolPak

EG15.2 Using Transformations in Regression Models

The Square-Root Transformation

The Log Transformation

EG15.3 Collinearity

PHStat

Workbook

EG15.4 Model Building

The Stepwise Regression Approach to Model Building

Key Technique

Example

PHStat

The Best Subsets Approach to Model Building

Key Technique

Example

PHStat

Chapter 15 JMP Guide

JG15.1 The Quadratic Regression Model

JG15.2 Using Transformations in Regression Models

JG15.3 Collinearity

JG15.4 Model Building

The Stepwise Regression Approach to Model Building

The Best Subsets Approach to Model Building

Chapter 15 Minitab Guide

MG15.1 The Quadratic Regression Model

MG15.2 Using Transformations in Regression Models

MG15.3 Collinearity

MG15.4 Model Building

The Stepwise Regression Approach to Model Building

The Best Subsets Approach to Model Building

16 Time-Series Forecasting

Contents

Objectives

16.1 Time-Series Component Factors

16.2 Smoothing an Annual Time Series

Moving Averages

Solution

Exponential Smoothing

Problems For Section 16.2

Learning The Basics

Applying The Concepts

16.3 Least-Squares Trend Fitting and Forecasting

The Linear Trend Model

The Quadratic Trend Model

The Exponential Trend Model

Model Selection Using First, Second, and Percentage Differences

Solution

Solution

Solution

Problems For Section 16.3

Learning The Basics

Applying The Concepts

16.4 Autoregressive Modeling for Trend Fitting and Forecasting

Selecting an Appropriate Autoregressive Model

Solution

Solution

Determining the Appropriateness of a Selected Model

Problems For Section 16.4

Learning The Basics

Applying The Concepts

16.5 Choosing an Appropriate Forecasting Model

Residual Analysis

The Magnitude of the Residuals Through Squared or Absolute Differences

The Principle of Parsimony

A Comparison of Four Forecasting Methods

Problems For Section 16.5

Learning The Basics

Applying The Concepts

16.6 Time-Series Forecasting of Seasonal Data

Least-Squares Forecasting with Monthly or Quarterly Data

Problems For Section 16.6

Learning The Basics

Applying The Concepts

16.7 Index Numbers

Summary

References

Key Equations

Computing an Exponentially Smoothed Value in Time Period i

Forecasting Time Period i + 1

Linear Trend Forecasting Equation

Quadratic Trend Forecasting Equation

Exponential Trend Model

Transformed Exponential Trend Model

Exponential Trend Forecasting Equation

pth-Order Autoregressive Models

First-Order Autoregressive Model

Second-Order Autoregressive Model

t Test for Significance of the Highest-Order Autoregressive Parameter, Ap

Fitted pth-Order Autoregressive Equation

pth-Order Autoregressive Forecasting Equation

Mean Absolute Deviation

Exponential Model with Quarterly Data

Transformed Exponential Model with Quarterly Data

Exponential Growth with Quarterly Data Forecasting Equation

Exponential Model with Monthly Data

Transformed Exponential Model with Monthly Data

Exponential Growth with Monthly Data Forecasting Equation

Key Terms

Checking Your Understanding

Chapter Review Problems

Report Writing Exercise

Managing Ashland MultiComm Services

Digital Case

Chapter 16 Excel Guide

EG16.2 Smoothing an Annual Time Series

Moving Averages

Key Technique

Example

Workbook

Exponential Smoothing

Key Technique

Example

Workbook

Analysis ToolPak

EG16.3 Least-Squares Trend Fitting and Forecasting

The Linear Trend Model

Key Technique

The Quadratic Trend Model

Key Technique

The Exponential Trend Model

Key Technique

Model Selection Using First, Second, and Percentage Differences

Key Technique

EG16.4 Autoregressive Modeling for Trend Fitting and Forecasting

Creating Lagged Predictor Variables

Key Technique

Autoregressive Modeling

Key Technique

EG16.5 Choosing an Appropriate Forecasting Model

Performing a Residual Analysis

Measuring the Magnitude of the Residuals Through Squared or Absolute Differences

Key Technique

A Comparison of Four Forecasting Methods

Key Technique

EG16.6 Time-Series Forecasting of Seasonal Data

Least-Squares Forecasting with Monthly or Quarterly Data

Chapter 16 JMP Guide

JG16.2 Smoothing an Annual Time Series

Moving Averages

Exponential Smoothing

JG16.3 Least-Squares Trend Fitting and Forecasting

The Linear Trend Model

The Quadratic Trend Model

The Exponential Trend Model

Model Selection Using First, Second, and Percentage Differences

JG16.4 Autoregressive Modeling for Trend Fitting and Forecasting

Creating Lagged Predictor Variables

Autoregressive Modeling

JG16.5 Choosing an Appropriate Forecasting Model

A Comparison of Four Forecasting Methods

JG16.6 Time-Series Forecasting of Seasonal Data

Least-Squares Forecasting with Monthly or Quarterly Data

Chapter 16 Minitab Guide

MG16.2 Smoothing an Annual Time Series

Moving Averages

Exponential Smoothing

MG16.3 Least-Squares Trend Fitting and Forecasting

The Linear Trend Model

The Quadratic Trend Model

The Exponential Trend Model

Model Selection Using First, Second, and Percentage Differences

MG16.4 Autoregressive Modeling for Trend Fitting and Forecasting

Creating Lagged Predictor Variables

Autoregressive Modeling

MG16.5 Choosing an Appropriate Forecasting Model

A Comparison of Four Forecasting Methods

MG16.6 Time-Series Forecasting of Seasonal Data

Least-Squares Forecasting with Monthly or Quarterly Data

17 Business Analytics

Contents

Objectives

17.1 Business Analytics Categories

Inferential Statistics and Predictive Analytics

Supervised and Unsupervised Methods

17.2 Descriptive Analytics

Dashboards

Data Dimensionality and Descriptive Analytics

17.3 Predictive Analytics for Prediction

Problems for Section 17.3

17.4 Predictive Analytics for Classification

Problems for Section 17.4

17.5 Predictive Analytics for Clustering

Problems for Section 17.5

17.6 Predictive Analytics for Association

Multidimensional Scaling (MDS)

Problems for Section 17.6

17.7 Text Analytics

17.8 Prescriptive Analytics

References

Key Equations

Akaike Information Criterion (AIC)

Akaike Information Criterion corrected (AICC)

LogWorth

Euclidean Distance

Key Terms

Checking Your Understanding

Chapter Review Problems

The Mountain States Potato Company

The Craybill Instrumentation Company

Chapter 17 Software Guide

Introduction

SG17.2 Descriptive Analytics

Dashboards

Excel

JMP

Minitab

Dynamic Bubble Charts

Example

JMP

SG17.3 Predictive Analytics for Prediction

SG17.4 Predictive Analytics for Classification

SG17.5 Predictive Analytics for Clustering

SG17.6 Predictive Analytics for Association

Multidimensional Scaling (MDS)

Example

JMP

18 Getting Ready to Analyze Data in the Future

Contents

Objectives

18.1 Analyzing Numerical Variables

Describe the Characteristics of a Numerical Variable?

Reach Conclusions About the Population Mean or the Standard Deviation?

Determine Whether the Mean and/or Standard Deviation Differs Depending on the Group?

If the Grouping Variable Defines Two Independent Groups and You Are Interested in Central Tendency

If the Grouping Variable Defines Two Groups of Matched Samples or Repeated Measurements and You Are Interested in Central Tendency

If the Grouping Variable Defines Two Independent Groups and You Are Interested in Variability

If the Grouping Variable Defines More Than Two Independent Groups and You Are Interested in Central Tendency

If the Grouping Variable Defines More Than Two Groups of Matched Samples or Repeated Measurements and You Are Interested in Central Tendency

Determine Which Factors Affect the Value of a Variable?

Predict the Value of a Variable Based on the Values of Other Variables?

Classify or Associate Items?

Determine Whether the Values of a Variable Are Stable Over Time?

18.2 Analyzing Categorical Variables

Describe the Proportion of Items of Interest in Each Category?

Reach Conclusions About the Proportion of Items of Interest?

Determine Whether the Proportion of Items of Interest Differs Depending on the Group?

For Two Categories and Two Independent Groups

For Two Categories and Two Groups of Matched or Repeated Measurements

For Two Categories and More Than Two Independent Groups

For More Than Two Categories and More Than Two Groups

Predict the Proportion of Items of Interest Based on the Values of Other Variables?

Classify or Associate Items?

Determine Whether the Proportion of Items of Interest Is Stable Over Time?

Chapter Review Problems

Appendices

Appendix A Basic Math Concepts and Symbols

A.1 Operators

A.2 Rules for Arithmetic Operations

A.3 Rules for Algebra: Exponents and Square Roots

A.4 Rules for Logarithms

Base 10

Solution

Base e

Solution

A.5 Summation Notation

Answers

References

A.6 Greek Alphabet

Appendix B Important Software Skills and Concepts

B.1 Identifying the Software Version

Excel

Identify the build number

JMP

Minitab

B.2 Formulas

Entering a Formula

Entering an Array Formula (Excel)

Pasting with Paste Special (Excel)

Verifying Formulas

B.3 Excel Cell References

Absolute and Relative Cell References

Selecting Cell Ranges for Charts

Selecting Non-contiguous Cell Ranges

B.4 Excel Worksheet Formatting

Format Cells Method

Home Tab Shortcuts Method

B.5E Excel Chart Formatting

Most Commonly Made Excel Changes

Chart and Axis Titles

Chart Axes

Correcting the Display of the X Axis

Emphasizing Histogram Bars

B.5J JMP Chart Formatting

B.5M Minitab Chart Formatting

B.6 Creating Histograms for Discrete Probability Distributions (Excel)

B.7 Deleting the “Extra” Histogram Bar (Excel)

Appendix C Online Resources

C.1 About the Online Resources for This Book

Access the Online Resources

C.2 Data Files

C.3 Files Integrated With Microsoft Excel

Excel Guide Workbooks

Visual Explorations

PHStat

C.4 Supplemental Files

Appendix D Configuring Software

D.1 Microsoft Excel Configuration

Step 1: Update Excel

Step 2: Verify Microsoft Add-Ins

Step 3: Verify Excel Security Settings

Step 4: Opening Add-ins

D.2 JMP Configuration

D.3 Minitab Configuration

Appendix E Table

Appendix F Useful Knowledge

F.1 Keyboard Shortcuts

Editing Shortcuts

Excel Formatting & Utility Shortcuts

JMP Utility Commands

Minitab Utility Commands

F.2 Understanding the Nonstatistical Functions

Excel

JMP

Appendix G Software FAQs

G.1 Microsoft Excel FAQs

G.2 PHStat FAQs

G.3 JMP FAQs

G.4 Minitab FAQs

Appendix H All About PHStat

H.1 What is PHStat?

How PHStat Works

Preparing Data for PHStat Analysis

H.2 Obtaining and Setting Up PHStat

H.3 Using PHStat

H.4 PHStat Procedures, by Category

Self-Test Solutions and Answers to Selected Even-Numbered Problems

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Chapter 16

Chapter 17

Index

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

Credits

Photos

Front Matter

First Things First

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Chapter 16

Chapter 17

Online Chapter 19

Online Chapter 20

Text

Chapter 2

Chapter 3

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 17

Short Takes for Chapter 1

For 1.1 Defining Variables

Measurement Scales for Variables

For Nominal and Ordinal Scales

For Interval and Ratio Scales

For 1.2 Collecting Data

For Data Sources

For EG1.3 Types of Sampling Methods

For Simple Random Sample

For EG1.4 Data Cleaning

For MG1.4 Data Cleaning

For MG1.5 Other Preprocessing Tasks

For Recoding Variables

Short Takes for Chapter 2

For 2.5 Visualizing Two Numerical Variables

Short Takes for Chapter 3

For 3.2 Measures of Variation and Shape

For The Coefficient of Variation

For Shape: Skewness

For Shape: Kurtosis

For 3.3 Exploring Numerical Data

For Percentiles

For EG3.3 Exploring Numerical Data

For Quartiles

For The Five-Number Summary and the Boxplot

For EG3.5 The Covariance and the Coefficient of Correlation

For The Covariance

Short Takes for Chapter 5

For EG5.2 Binomial Distribution

For EG5.3 Poisson Distribution

For EG5.5 Hypergeometric Distribution

Short Takes for Chapter 6

For EG6.2 The Normal Distribution

Excel Template

JMP Templates

Minitab Templates

Short Takes for Chapter 7

For 7.2 Sampling Distribution of the Mean

For The Unbiased Property of the Sample Mean

Short Takes for Chapter 11

For EG11.2 The Factorial Design: Two-Way Analysis of Variance

Short Takes for Chapter 14

For EG14.1 Developing a Multiple Regression Model

Interpreting the Regression Coefficients

Chapter 17 Software Guide Extended

Introduction

Getting Started with Tableau

Connecting to Tableau Data Sources

How Tableau Classifies Data

Tableau Terminology: Cards, Shelves, and Pills

SGE17.2 Descriptive Analytics

Dashboards

Dynamic Bubble Charts

SGE17.5 Predictive Analytics for Clustering

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