## Book Description

JMP Means Business: Statistical Models for Management, by Josef Schmee and Jane Oppenlander, covers basic methods and models of classical statistics. Designed for business and MBA students, as well as industry professionals who need to use and interpret statistics, JMP Means Business covers data collection, descriptive statistics, distributions, confidence intervals and hypothesis tests, analysis of variance, contingency tables, simple and multiple regression, and exponential smoothing of time series. The easy-to-use format includes verbal and graphical explanations and promotes standard problem-solving techniques, with a limited use of formulas. Examples from business and industry serve to introduce each topic. Each example starts with a problem definition and data requirements, followed by a step-by-step analysis with JMP. Relevant output from this analysis is used to explain each method and to provide the basis for interpretation. Each chapter ends with a summary and a collection of problems for further study.

2. Preface
3. Acknowledgments
4. Praise from the Experts
1. 1.1 Introduction
2. 1.2 Why Data Are Needed
3. 1.3 Sources of Data
1. 1.3.1 Existing Data versus New Data
2. 1.3.2 Existing Data
3. 1.3.3 New Data
4. 1.4 Data Scales
5. 1.5 Summary
6. 1.6 Problems
7. 1.7 Case Study: Green's Gym–Part 1
6. 2. Data Collection in Surveys
1. 2.1 Introduction
2. 2.2 Questionnaires
1. 2.2.1 Layers of a Questionnaire
2. 2.2.2 How to Ask Questions
3. 2.2.3 Questionnaire Design
4. 2.2.4 Guidelines for Writing Questions
3. 2.3 Sampling
1. 2.3.1 Sampling Concepts
2. 2.3.2 Probability Samples
3. 2.3.3 Probability versus Non-Probability Samples
4. 2.3.4 Common Types of Probability Sampling
5. 2.3.5 Other Sampling Methods
4. 2.4 Summary
5. 2.5 Problems
6. 2.6 Case Study: Green's Gym–Part 2
7. 2.7 References
8. 3. Describing Data from a Single Variable
1. 3.1 Introduction
2. 3.2 Example: Order Processing in an Herbal Tea Mail Order Business
3. 3.3 Descriptive Statistics with the JMP Distribution Platform
4. 3.4 Interpretation of Descriptive Statistics
5. 3.5 Practical Advice and Potential Problems
1. 3.5.1 Features of Good Graphs
2. 3.5.2 Example: Izod Impact Strength of Two Suppliers
3. 3.5.3 Simple Improvements to Histograms
4. 3.5.4 Potential Problems with Box Plots and Histograms
6. 3.6 Summary
7. 3.7 Problems
8. 3.8 Case Study: New Web Software Testing
9. 4. Statistical Models
1. 4.1 Introduction
2. 4.2 Classification of Statistical Models
3. 4.3 Model Validation
4. 4.4 Summary
5. 4.5 Problems
6. 4.6 Case Study: Models of Advertising Effectiveness
10. 5. Discrete Probability Distributions
1. 5.1 Introduction to Distributions
2. 5.2 Discrete Distributions
3. 5.3 Binomial Distribution
4. 5.4 Distributions of Two Discrete Random Variables (Y1, Y2)
5. 5.5 Summary
6. 5.6 Problems
7. 5.7 Case Study: Assessing Financial Investments
11. 6. Continuous Probability Distributions
1. 6.1 Introduction to Continuous Distributions
2. 6.2 Characteristics of Continuous Distributions
3. 6.3 Uniform Distribution
4. 6.4 The Normal Distribution
1. 6.4.1 General Normal Distribution with Mean[Y] = μ and SD[Y] = σ
2. 6.4.2 Standard Normal Distribution
5. 6.5 Central Limit Theorem
6. 6.6 Sampling Distributions
7. 6.7 Summary
8. 6.8 Problems
9. 6.9 Case Study: Julie's Lakeside Candy
12. 7. Confidence Intervals
1. 7.1 Introduction
2. 7.2 Point Estimates of Mean and Standard Deviation
1. 7.2.1 What Is a Point Estimate?
2. 7.2.2 Parameters and Estimates
3. 7.2.3 Standard Error of Estimate
3. 7.3 Confidence Intervals for Mean and Standard Deviation
4. 7.4 Detail Example: Package Delivery Times of Herbal Teas
5. 7.5 JMP Analysis of Herbal Tea Package Delivery Times
6. 7.6 Prediction and Tolerance Intervals
1. 7.6.1 Prediction Intervals
2. 7.6.2 Tolerance Intervals
7. 7.7 Summary
8. 7.8 Problems
13. 7.9 References
14. 8. Hypothesis Tests for a Single Variable Y
1. 8.1 Introduction to Hypothesis Testing
1. 8.1.1 Accept or Reject Decisions for the Mean: H0 versus HA
2. 8.1.2 Significance Level α
3. 8.1.3 Test Statistic
4. 8.1.4 p-Value
5. 8.1.5 Decision Rule to Accept or Reject H0
6. 8.1.6 Example: Order Processing Times
7. 8.1.7 Test Statistic, Significance Level, Critical Value, and p-Value
8. 8.1.8 Example: Order Processing with JMP
9. 8.1.9 Confidence Intervals and Two-Sided Hypothesis Testing
2. 8.2 Sample Size Needed to Test H0: Mean = Mean0 versus HA: Mean = MeanA
1. 8.2.1 Introduction
2. 8.2.2 Example: Sample Size Calculations for Mama Mia's Pizza Parlor
3. 8.2.3 Sample Size in JMP
4. 8.2.4 Approximate Formulas for Sample Size
5. 8.2.5 Power Curves
6. 8.2.6 Sample versus Process Standard Deviation (s versus σ)
7. 8.2.7 Hypothesis Test for the Standard Deviation σ
3. 8.3 Summary
4. 8.4 Problems
5. 8.5 Case Study: Traffic Speed Limit Change
15. 9. Comparing Two Means
1. 9.1 Introduction
2. 9.2 Two-Sample t-Test
3. 9.3 Paired t-Test
4. 9.4 Paired t-test versus Two-Sample t-test on the Same Data
5. 9.5 Summary
6. 9.6 Problems
7. 9.7 Case Study: Westville Meat Processing Plant
16. 9.8 References
17. 10. Comparing Several Means with One-Way ANOVA
1. 10.1 Introduction
2. 10.2 Detail Example: Training Method and Time to Learn
3. 10.3 One-Way ANOVA in JMP
1. 10.3.1 One-Way ANOVA with Fit Y by X
2. 10.3.2 Interpretation of Results
3. 10.3.3 Means Comparisons
4. 10.4 Checking Assumptions of ANOVA Model
5. 10.5 Summary
6. 10.6 Problems
7. 10.7 Case Study: Carpal Tunnel Release Surgery
18. 11. Two-Way ANOVA for Comparing Means
1. 11.1 Introduction
2. 11.2 Two-Way ANOVA without Replications
3. 11.3 Two-Way ANOVA with Equally Replicated Data
4. 11.4 Two-Way ANOVA with Unequal Replications
5. 11.5 Summary
6. 11.6 Problems
7. 11.7 Case Study: Fish Catch near Oil Rig
19. 12. Proportions
1. 12.1 Introduction
2. 12.2 Proportions from a Single Sample
3. 12.3 Chi-Square Test for Equality of k Proportions
4. 12.4 Summary
5. 12.5 Problems
6. 12.6 Case Study: Incomplete Rebate Submissions
20. 13. Tests for Independence
1. 13.1 Statistical Independence of Two Nominal Variables
1. 13.1.1 Introduction
2. 13.1.2 Review of Conditional Probability and Independent Events
3. 13.1.3 Statistical Hypotheses of Independence of Two Nominal Variables
4. 13.1.4 Example 1: Executive Transfers
2. 13.2 Stratification in Cross-Classified Data
1. 13.2.1 What Is Stratification of Cross-Classified Data?
2. 13.2.2 Example 1: Consumer Preference of Two Cola Brands
3. 13.2.3 Example 2: On-Time Performance of Package Delivery Companies
4. 13.2.4 Example 3: Mortality after a High-Risk Procedure in Two Hospitals
3. 13.3 Summary
4. 13.4 Problems
5. 13.5 Case Study: Financial Management Customer Satisfaction Survey
21. 13.6 References
22. 14. Simple Regression Analysis
1. 14.1 Introduction
2. 14.2 Detail Example: Yield in a Chemical Reactor
3. 14.3 JMP Analysis of the Yield in a Chemical Reactor Example
4. 14.4 Interpretation of Basic Regression Outputs
1. 14.4.1 Estimates of Simple Regression Equation
2. 14.4.2 t-Ratios to Test Significance of b0 and b1
3. 14.4.3 Root Mean Square Error (RMSE)
4. 14.4.4 Additional Simple Regression Results
5. 14.5 How Good Is the Regression Line?
6. 14.6 Important Considerations
7. 14.7 Summary
8. 14.8 Problems
9. 14.9 Case Study: Lost Time Occupational Injuries
23. 15. Simple Regression Extensions
1. 15.1 Simple Correlation
1. 15.1.1 Introduction
2. 15.1.2 Example: Correlation of Financial Indices
3. 15.1.3 Data Patterns and Correlation Coefficients
2. 15.2 Regression and Stock Market Returns
1. 15.2.1 Introduction
2. 15.2.2 Capital Asset Pricing Model (CAPM)
3. 15.3 Curvilinear Regression
1. 15.3.1 Introduction
2. 15.3.2 Quadratic Regression with Fit Polynomial
3. 15.3.3 Fitting Curves with Fit Special
4. 15.3.4 Detail Example: Price Elasticity of Demand
4. 15.4 Summary
5. 15.5 Problems
6. 15.6 Case Studies
24. 16. Multiple Regression Analysis
1. 16.1 Introduction
2. 16.2 Detail Example: Profits of Bank Branches
3. 16.3 JMP Analysis of Bank Branch Profits Example
1. 16.3.1 Preliminary Fitting of Single X-Variables to Y
2. 16.3.2 Fitting Several X-Variables to Y
3. 16.3.3 What Do t-Ratios Measure?
4. 16.3.4 Conclusions from the Three X-Variable Model
5. 16.3.5 Regression Model Using Total Sales per Year = X1 and Total Sqft = X2
4. 16.4 Evaluating Model Assumptions and Goodness of Fit
5. 16.5 Model Interpretation
1. 16.5.1 Leverage Plots and the Importance of X
2. 16.5.2 Standardized Beta and the Importance of X
3. 16.5.3 Understanding the Role of X-Variables: Column Diagnostics
6. 16.6 Summary
7. 16.7 Problems
8. 16.8 Case Study: Forbes Global 2000 High Performers
25. 16.9 References
26. 17. Multiple Regression with Nominal Variables
1. 17.1 Introduction
2. 17.2 Detail Example: Loan Amount versus Sales Revenues
3. 17.3 Difference of Intercepts of Two Parallel Lines
4. 17.4 Regression Models Including Nominal Variables with Three or More Levels
5. 17.5 Both Intercept and Slope of Two Lines Are Different
6. 17.6 Summary
7. 17.7 Problems
8. 17.8 Case Study: Coffee Sales
27. 18. Finding a Good Multiple Regression Model
1. 18.1 Introduction
2. 18.2 Detail Example: Profit of Bank Branches
3. 18.3 All Possible Regression Models
4. 18.4 Stepwise Regression
1. 18.4.1 Stepwise Regression Algorithms
2. 18.4.2 Stepwise Regression in JMP
5. 18.5 Candidate Models
6. 18.6 Model Recommendation
1. 18.6.1 Recommended Model for the Bank Branch Profits Example
2. 18.6.2 Other Criteria for Including or Excluding X-Variables
7. 18.7 Summary
8. 18.8 Problems
9. 18.9 Case Studies
1. 18.9.1 Case Study 1: Real Estate Appraisal
2. 18.9.2 Case Study 2: Discrimination in Compensation?
28. 19. Exponential Smoothing Models for Time Series Data
1. 19.1 Introduction
2. 19.2 Detail Example: 10-Year Treasury Note Closing Prices
3. 19.3 Smoothing Models
1. 19.3.1 Simple Moving Averages
2. 19.3.2 Exponential Smoothing Models
3. 19.3.3 Simple Exponential Smoothing (SES)
4. 19.3.4 Double Exponential Smoothing (DES) For Linear Trend
5. 19.3.5 Winters' Additive Seasonal Method
4. 19.4 Summary
5. 19.5 Problems
6. 19.6 Case Study: Lockheed Martin Stock in Changing Times
29. 19.7 References

## Product Information

• Title: JMP® Means Business: Statistical Models for Management
• Author(s):
• Release date: July 2010
• Publisher(s): SAS Institute
• ISBN: 9781599942995