Table of contents for Statistics for managers using Microsoft Excel / David M. Levine ... [et al.].

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 Contents
Preface 
Chapter 1
Introduction and Data Collection
1.1 Why Learn Statistics
1.2 Statistics for Managers
USING STATISTICS @ Good Tunes 
1.3	Basic Vocabulary of Statistics
1.4	Data Collection
1.5	Types of Variables
		Levels of Measurement and Measurement Scales
1.6 	Microsoft Excel Worksheets
Worksheet Cells
Designing Effective Worksheets
Summary 
Key Terms 
Chapter Review Problems 
Introduction to the Web Cases
Excel Companion to Chapter 1
E1.1 Preliminaries: Basic Computing Skills
E1.2 Basic Workbook Operations
E1.3 Worksheet Entries
E1.4 Worksheet Formatting
E1.5 Copy-and-Paste Operations
E1.6 Add-ins: Making Things Easier for You
Chapter 2
Presenting Data in Tables and Charts
USING STATISTICS@ CHOICE IS YOURS
2.1	Tables and Charts for Categorical Data
The Summary Table
The Bar Chart
The Pie Chart
The Pareto Diagram
2.2	Organizing Numerical Data
The Ordered Array
The Stem-and-Leaf Display
2.3	Tables and Charts for Numerical Data
The Frequency Distribution
The Relative Frequency Distribution and the Percentage Distribution
The Cumulative Distribution
The Histogram
The Polygon
The Cumulative Percentage Polygon (Ogive)
2.4	Cross Tabulations
The Contingency Table
The Side-by-Side Bar Chart
2.5	Scatter Plots and Time-Series Plots
The Scatter Plot
The Time-Series Plot
2.6	Misusing Graphs and Ethical Issues
Summary 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 2
E2.1 Creating Summary Tables
E2.2 Creating Charts 
E2.3 Creating Bar and Pie Charts from Summary Tables
E2.4 Creating Pareto Diagrams from Summary Tables
E2.5 Creating an Ordered Array
E2.6 Creating Stem-and Leaf Displays
E2.7 Creating Frequency Distributions and Histograms
E2.8 Creating a Histogram from Summarized Data
E2.9 Creating Polygons
E2.10 Creating Contingency Tables
E2.11 Creating Side-by-Side Charts
E2.12 Creating Scatter Plots
E2.13 Creating Time Series Plots
3 Numerical Descriptive Measures 
Using Statistics@ Choice Is Yours
3.1	Measures of Central Tendency 
The Mean
The Median
The Mode
Quartiles
The Geometric Mean
3.2 Variation and Shape 
The Range
The Interquartile Range
The Variance and the Standard Deviation
The Coefficient of Variation
Z Scores
Shape
Visual Explorations: Exploring Descriptive Statistics
Microsoft Excel Descriptive Statistics Output
3.3	Numerical Descriptive Measures for a Population
The Population Mean
The Population Variance and Standard Deviation
The Empirical Rule
The Chebychev Rule
3.4	Exploratory Data Analysis
The Five-Number Summary
The Box-and-Whisker Plot
3.5	The Covariance and the Coefficient of Correlation
The Covariance
The Coefficient of Correlation
3.6	Pitfalls in Numerical Descriptive Measures and Ethical Issues
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 3
E3.1 Computing Measures of Central Tendency, Variation, and Shape
E3.2 Creating Dot Scale Diagrams
E3.3 Computing Measures for a Population
E3.4 Creating Box-and-Whisker Plots
E3.5 Computing the Covariance
E3.6 Computing the Correlation Coefficient
4 Basic Probability 
Using Statistics@The Consumer Electronics Company
4.1	Basic Probability Concepts
Events and Sample Spaces
Contingency Tables 
Simple (Marginal) Probability
Joint Probability
General Addition Rule
4.2	Conditional Probability
Computing Conditional Probabilities
Decision Trees
Statistical Independence
Multiplication Rules
Marginal Probability Using the General Multiplication Rule
4.3	Bayes' Theorem
4.4	Ethical Issues and Probability
CD-ROM Topic 4.5 Counting Rules
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Web Case 
Excel Companion to Chapter 4
E4.1 Computing Basic Probabilities
E4.2 Using Bayes' Theorem
5 Some Important Discrete Probability Distributions 
Using Statistics@ Saxon Home Improvement 
5.1 The Probability Distribution for a Discrete Random Variable
Expected Value of a Discrete Random Variable
Variance and Standard Deviation of a Discrete Random Variable
5.2 Covariance and Its Application in Finance
The Covariance
The Expected Value, Variance, and Standard Deviation of the Sum of Two 
Random Variables
Portfolio Expected Return and Portfolio Risk
5.3	Binomial Distribution
5.4	Poisson Distribution
5.5	Hypergeometric Distribution
5.6	(CD ROM Topic) Using the Poisson Distribution to Approximate the Binomial 
Distribution
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 5
E5.1 Computing the Expected Value of a Discrete Random Variable
E5.2 Computing Portfolio Expected Return & Portfolio Risk
E5.3 Computing Binomial Probabilities
E5.4 Computing Poisson Probabilities
E5.5 Computing Hypergeometric Probabilities
E5.6 Creating Histograms for Discrete Probability Distributions
6 The Normal Distribution and Other Continuous Distributions 
Using Statistics@OurCampus!
6.1	Continuous Probability Distributions
6.2	The Normal Distribution
6.3	Evaluating Normality
Comparing Data Characteristics to Theoretical Properties
Constructing the Normal Probability Plot
6.4	The Uniform Distribution
6.5	The Exponential Distribution
CD-ROM Topic 6.6	The Normal Approximation to the Binomial Distribution
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 6
E6.1 Computing Normal Probabilities
E6.2 Creating Normal Probability Plots
E6.3 Computing Exponential Probabilities
7 Sampling and Sampling Distributions 
Using Statistics@Oxford Cereals
7.1	Types of Survey Sampling Methods
Simple Random Sample
Systematic Sample
Stratified Sample
Cluster Sample
7.2	Evaluating Survey Worthiness
Survey Errors
Ethical Issues 
7.3	Sampling Distributions
7.4	Sampling Distribution of the Mean
The Unbiased Property of the Sample Mean
Standard Error of the Mean
Sampling from Normally Distributed Populations
Sampling from Nonnormally Distributed Populations-The Central Limit 
Theorem
7.5	Sampling Distribution of the Proportion
7.6	(CD-ROM Topic) Sampling from Finite Populations
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 7
E7.1 Creating Simple Random Samples (without replacement)
E7.2 Creating Simulated Sampling Distributions
8 Confidence Interval Estimation 
Using Statistics@ SAXON HOME IMPROVEMENT
8.1	Confidence Interval Estimation for the Mean (? Known)
8.2	Confidence Interval Estimation for the Mean (? Unknown)
Student's t Distribution
Properties of the t Distribution
The Concept of Degrees of Freedom
The Confidence Interval Statement
8.3	Confidence Interval Estimation for the Proportion
8.4	Determining Sample Size
Sample Size Determination for the Mean
Sample Size Determination for the Proportion
8.5	Applications of Confidence Interval Estimation in Auditing
Estimating the Population Total Amount
Difference Estimation
One-Sided Confidence Interval Estimation of the Rate of Noncompliance 
with Internal Controls
8.6	Confidence Interval Estimation and Ethical Issues
8.7	CD-ROM Topic: Estimation and Sample Size Determination for Finite 
Populations
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
EXCEL COMPANION to Chapter 8
E8.1 Computing the Confidence Interval Estimate for the Mean (? known)
E8.2 Computing the Confidence Interval Estimate for the Mean (? unknown)
E8.3 Computing the Confidence Interval Estimate for the Proportion
E8.4 Computing the Sample Size Needed for Estimating the Mean
E8.5 Computing the Sample Size Needed for Estimating the Proportion
E8.6 Computing the Confidence Interval Estimate for the Population Total
E8.7 Computing the Confidence Interval Estimate for the Total Difference
E8.8 Computing Finite Population Correction Factors
9 Fundamentals of Hypothesis Testing: One-Sample Tests 
Using Statistics@ Oxford Cereals, Part II
9.1	Hypothesis-Testing Methodology
The Null and Alternative Hypotheses
The Critical Value of the Test Statistic
Regions of Rejection and Nonrejection
Risks in Decision Making Using Hypothesis-Testing Methodology
9.2	Z Test of Hypothesis for the Mean ??? Known)
The Critical Value Approach to Hypothesis Testing
The p-Value Approach to Hypothesis Testing
A Connection between Confidence Interval Estimation and Hypothesis 
Testing
9.3	One-Tail Tests
The Critical Value Approach
The p-Value Approach
9.4	t Test of Hypothesis for the Mean (? Unknown)
The Critical Value Approach
The p-Value Approach
Checking Assumptions
9.5	Z Test of Hypothesis for the Proportion
The Critical Value Approach
The p-Value Approach
9.6	Potential Hypothesis-Testing Pitfalls and Ethical Issues
9.7	CD-ROM Topic The Power of a Test
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 9
E9.1 Using the Z Test for the Mean (? known)
E9.2 Using the t Test for the Mean (? unknown)
E9.3 Using the Z Test for the Proportion
10 Two-Sample Tests 
Using Statistics@BLK Foods
10.1	Comparing the Means of Two Independent Populations
Z Test for the Difference Between Two Means
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
10.2	Comparing the Means of Two Related Populations
Paired t Test
Confidence Interval Estimate for the Mean Difference
10.3	Comparing Two Population Proportions
Z Test for the Difference Between Two Proportions
Confidence Interval Estimate for the Difference Between Two Proportions
10.4	F Test for the Difference Between Two Variances
Finding Lower-Tail Critical Values
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 10
Two-Sample Hypothesis Testing in Microsoft Excel
E10.1 Using the Z Test for the Difference Between Two Means (Unsummarized 
Data)
E10.2 Using the Z Test for the Difference Between Two Means (Summarized 
Data)
E10.3 Using the Pooled-Variance t Test (Unsummarized Data)
E10.4 Using the Pooled-Variance t Test (Summarized Data)
E10.5 Using the Separate-Variance t Test for the Difference Between Two Means 
(Unsummarized Data)
E10.6 Using the Paired t Test for the Difference Between Two Means 
(Unsummarized Data)
E10.7 Using the Z Test for the Difference Between Two Proportions 
(Summarized Data)
E10.8 Using the F Test for the Difference Between Two Variances 
(Unsummarized Data)
E10.9 Using the F Test for the Difference Between Two Variances (Summarized 
Data)
11 Analysis of Variance 
Using Statistics @ Perfect Parachutes
11.1	The Completely Randomized Design: One-Way Analysis of Variance
F Test for Differences Among More Than Two Means
Multiple Comparisons: The Tukey-Kramer Procedure
ANOVA Assumptions
Levene's Test for Homogeneity of Variance
11.2	The Factorial Design: Two-Way Analysis of Variance
Testing for Factor and Interaction Effects
Interpreting Interaction Effects
Multiple Comparisons: The Tukey Procedure
11.3 	CD-ROM Topic The Randomized Block Design
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 11 
E11.1 Using the F Test for Differences Among More Than Two Means
E11.2 Using the Tukey-Kramer Procedure
E11.3 Using the Levene Test for Homogeneity of Variance
E11.4 Using The Two-Way ANOVA
12	Chi-Square Tests and Nonparametric Tests 
Using Statistics@ T.C. Resort Properties
12.1	Chi-Square Test for the Difference Between Two Proportions (Independent 
Samples)
12.2	Chi-Square Test for Differences Among More than Two Proportions
The Marascuilo Procedure
12.3	Chi-Square Test of Independence
12.4	McNemar Test for the Difference Between Two Proportions (Related Samples)
12.5	Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent 
Populations
12.6	Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way ANOVA
12.7	CD-ROM Topic Chi-Square Test for a Variance or Standard Deviation
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 12
E12.1 Using the Chi-Square Test for the Difference Between Two Proportions
E12.2 Using the Chi-Square Test for the Differences in More Than Two 
Proportions
E12.3 Using the Chi-Square Test of Independence
E12.4 Using the McNemar Test
E12.5 Using the Wilcoxon Rank Sum Test
E12.6 Using the Kruskal-Wallis Rank Test
13	Simple Linear Regression 
Using Statistics@ Sunflowers Apparel 
13.1	Types of Regression Models
13.2	Determining the Simple Linear Regression Equation
The Least-Squares Method
Visual Explorations: Exploring Simple Linear Regression Coefficients
Predictions in Regression Analysis: Interpolation versus Extrapolation
Computing the Y Intercept b0 and the Slope b1
13.3	Measures of Variation
Computing the Sum of Squares
The Coefficient of Determination
Standard Error of the Estimate
13.4	Assumptions
13.5	Residual Analysis
Evaluating the Assumptions
13.6	Measuring Autocorrelation: The Durbin-Watson Statistic
Residual Plots to Detect Autocorrelation
The Durbin-Watson Statistic
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 (?1)
t Test for the Correlation Coefficient
13.8	Estimation of Mean Values and Prediction of Individual Values
The Confidence Interval Estimate
The Prediction Interval
13.9	Pitfalls in Regression and Ethical Issues
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 13
E13.1 Performing Simple Linear Regression Analysis
E13.2 Creating Scatter Diagrams and Adding a Prediction Line 
E13.3 Performing Residual Analyses
E13.4 Computing the Durbin-Watson Statistic
E13.5 Estimating the Mean of Y and Predicting Y Values
E13.6 Example: Sunflowers Site Selection Data
14	Introduction to Multiple Regression 
Using Statistics@ OMNIFOODS
14.1	Developing the Multiple Regression Model
Interpreting the Regression Coefficients
Predicting the Dependent Variable Y
14.2	R2, Adjusted R2, and the Overall F Test
Coefficient of Multiple Determination
Test for the Significance of the Overall Multiple Regression Model
14.3	Residual Analysis for the Multiple Regression Model
14.4	Inferences Concerning the Population Regression Coefficients
Tests of Hypothesis
Confidence Interval Estimation
14.5	Testing Portions of the Multiple Regression Model
Coefficients of Partial Determination
14.6	Using Dummy Variables and Interaction Terms in Regression Models
Interactions
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 14
E14.1 Creating Multiple Regression Models
E14.2 Creating Multiple Regression Residual Plots 
E14.3 Computing the Confidence Interval Estimate of the Mean and Prediction 
Interval
E14.4 Computing the Coefficients of Partial Determination
E14.5 Creating Dummy Variables
E14.6 Creating Interaction Terms
15 Multiple Regression Model Building 
USING STATISTICS@WTT-TV
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
The Coefficient of Multiple Determination
15.2	Using Transformations in Regression Models
The Square-Root Transformation
The Log Transformation
15.3	Collinearity
15.4	Model Building
The Stepwise Regression Approach to Model Building
The Best-Subsets Approach to Model Building
Model Validation
15.5	Pitfalls in Multiple Regression and Ethical Issues
Pitfalls in Multiple Regression
Ethical Issues
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
The Mountain States Potato Company Case 
Web Case 
Excel Companion to Chapter 15
E15.1 Creating a Quadratic Term
E15.2 Creating Transformations
E15.3 Computing Variance Inflationary Factors
E15.4 Using Stepwise Regression
E15.5 Using Best-Subsets Regression
16 Time-Series Forecasting and Index Numbers 
Using Statistics@ THE PRINCIPLED
16.1	The Importance of Business Forecasting
16.2	Component Factors of the Classical Multiplicative Time-Series Model
16.3	Smoothing the Annual Time Series
Moving Averages
Exponential Smoothing
16.4	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
16.5	Autoregressive Modeling for Trend-Fitting and Forecasting
16.6	Choosing an Appropriate Forecasting Model
Performing a Residual Analysis
Measuring the Magnitude of the Residual Error through Squared or 
Absolute Differences
Principle of Parsimony
A Comparison of Four Forecasting Methods
16.7	Time-Series Forecasting of Seasonal Data
Least-Squares Forecasting with Monthly or Quarterly Data
16.8	Index Numbers
The Price Index
Aggregate Price Indexes
Weighted Aggregate Price Indexes
Some Common Price Indexes
16.9	Pitfalls Concerning Time-Series Forecasting
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Managing the Springville Herald 
Web Case 
Excel Companion to Chapter 16
E16.1 Computing Moving Averages
E16.2 Creating Time-Series Plots
E16.3 Creating Exponentially Smoothed Values
E16.4 Creating Coded X Variables
E16.5 Creating Quadratic and Exponential Terms
E16.6 Using Least-Squares Linear Trend Fitting
E16.7 Using Least-Squares Quadratic Trend Fitting
E16.8 Using Least-Squares Exponential Trend Fitting
E16.9 Creating Lagged Independent Variables
E16.10 Creating First-Order Autoregressive Models
E16.11 For Second-Order or Third-Order Autoregressive Models
E16.12 Computing the Mean Absolute Deviation (MAD)
E16.13 Creating Dummy Variables for Quarterly or Monthly Data
E16.14 Calculating Index Numbers
17 Decision Making 
Using Statistics@Reliable Fund
17.1	Payoff Tables and Decision Trees
17.2	Criteria for Decision Making
Expected Monetary Value
Expected Opportunity Loss
Return-to-Risk Ratio
17.3	Decision Making with Sample Information
17.4	Utility
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
Web Case 
Excel Companion to Chapter 17 
E17.1 Computing Opportunity Loss
E17.2 Computing Expected Monetary Value
18 Statistical Applications in Quality and Productivity Management 
Using Statistics@ BEACHCOMBER HOTEL
18.1	Total Quality Management
18.2	Six Sigma Management
18.3	The Theory of Control Charts
18.4	Control Chart for the Proportion-The p Chart
18.5	The Red Bead Experiment: Understanding Process Variability
18.6	Control Charts for the Range and the Mean
The R Chart
The Chart
18.7	Process Capability
Customer Satisfaction and Specification Limits
Capability Indexes
CPL, CPU, and Cpk
Summary 
Key Equations 
Key Terms 
Chapter Review Problems 
The Harnswell Sewing Machine Company Case 
Managing the Springville Herald 
Excel Companion to Chapter 18 
E18.1 Creating p Charts
E18.2 Creating R and Charts
Appendices 
A.	Review of Arithmetic, Algebra, and Logarithms 
B.	Summation Notation 
C.	Statistical Symbols and Greek Alphabet 
D.	CD-ROM Contents 
E.	Tables 
F.	Configuring Microsoft Excel and Installing PHStat 
Self-Test Solutions and Answers to Selected Even-Numbered Problems 
Index 
CD-ROM Topics (available as Adobe Reader .PDF files on the text CD)
4.5	Counting Rules 
5.6	Using the Poisson Distribution to Approximate the Binomial Distribution 
6.6	The Normal Approximation to the Binomial Distribution 
7.6	Sampling from Finite Populations 
8.7	Estimation and Sample Size Determination for Finite Populations 
9.7	The Power of a Test 
11.3	The Randomized Block Design 
12.7	Chi-Square Test for a Variance or Standard Deviation 

Library of Congress Subject Headings for this publication:

Microsoft Excel (Computer file).
Management -- Statistical methods.
Commercial statistics.
Electronic spreadsheets.
Management -- Statistical methods -- Computer programs.
Commercial statistics -- Computer programs.