Sale!

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

\$45.00

-36%

❤️ Complete Test Bank for Basic Business Statistics 14th Edition by Mark L. Berenson
✅ Original Source from Publisher
🔥 Full Solution Manual & Testbank Solutions

(2 customer reviews)

115 in stock

Test Bank for Basic Business Statistics 14th Edition by Mark L. Berenson \$70.00 \$45.00
This item is selling fast!

✅ Format: Digital copy DOC DOCX PDF RTF in a "ZIP file."

☑️ All the chapters are included.

⌛ Time: 30 min to 5 Hours after Payment

😍 Chat Online Available 24/24

Guaranteed Safe Checkout

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

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

A Roadmap for Selecting a Statistical Method
Basic Business Statistics Concepts and Applications
Basic Business Statistics Concepts and Applications
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
A Note of Thanks
First Things First
Contents
Objectives
Statistics: A Way of Thinking
DCOVA Framework
Analytical Skills More Important than Arithmetic Skills
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
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
Problems for Section 1.6
Applying the Concepts
Summary
References
Key Terms
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
JG14.5 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
Finding the Regression Coefficients and Predicting Y
Testing for the Significance of the Quadratic Model
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
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
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
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
JG15.2 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
MG15.2 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 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
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
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
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 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 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
Contents
Objectives
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
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
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
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 3: Verify Excel Security Settings
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
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 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
###### Free Sample Test Bank for Basic Business Statistics 14th Edition by Mark L. Berenson

For customer’s satisfaction, we provide free samples for any required Textbook solution or test bank to check and evaluate before making the final purchase..

If you require any further information, let me know. using Live Chat or Contact Us

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

1. Paul (verified owner)

The product is firmly packed.