JMP® Means Business: Statistical Models for Management

JMP® Means Business: Statistical Models for Management

by Josef Schmee, Jane Oppenlander
Released July 2010
Publisher(s): SAS Institute
ISBN: 9781599942995

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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.

Table of contents

  1. Copyright
  2. Preface
  3. Acknowledgments
  4. Praise from the Experts
  5. 1. About Data
    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
        1. Sources
        2. Uses of Existing Data
      3. 1.3.3 New Data
        1. Focus Groups
        2. Surveys
        3. Census
        4. Sample Surveys
        5. Designed Experiments
        6. Observational Studies
        7. Computer Simulation
    4. 1.4 Data Scales
      1. 1.4.1 Ratio Scale
      2. 1.4.2 Interval Scale
      3. 1.4.3 Ordinal Scale
      4. 1.4.4 Nominal Scale
      5. 1.4.5 Likert Scale
    5. 1.5 Summary
    6. 1.6 Problems
    7. 1.7 Case Study: Green's Gym–Part 1
        1. Business Goals
        2. Strategy
        3. Tasks
  6. 2. Data Collection in Surveys
    1. 2.1 Introduction
    2. 2.2 Questionnaires
      1. 2.2.1 Layers of a Questionnaire
        1. Words
        2. Questions
        3. Format
        4. Hypothesis
      2. 2.2.2 How to Ask Questions
        1. Personal Interviews
        2. Telephone Interviews
        3. Self-Administered Questionnaires
        4. Mailed Questionnaires
        5. Online Surveys
        6. Direct Observation
      3. 2.2.3 Questionnaire Design
      4. 2.2.4 Guidelines for Writing Questions
        1. Questions to Avoid
        2. Open versus Closed Responses
        3. Question by Type of Response
    3. 2.3 Sampling
      1. 2.3.1 Sampling Concepts
        1. Example: Customer Satisfaction after On-Board Program in a Bank
      2. 2.3.2 Probability Samples
        1. Sampling Errors
        2. Non-Sampling Errors
      3. 2.3.3 Probability versus Non-Probability Samples
      4. 2.3.4 Common Types of Probability Sampling
        1. Simple Random Sampling
        2. Systematic Sampling
        3. Stratified Sampling
        4. Cluster 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
        1. Business Goals
        2. Strategy
        3. Tasks
  7. 2.7 References
  8. 3. Describing Data from a Single Variable
    1. 3.1 Introduction
      1. 3.1.1 Graphs for Continuous Data
      2. 3.1.2 Numerical Summaries for Continuous Data
      3. 3.1.3 Data Descriptions for Nominal and Ordinal Data
    2. 3.2 Example: Order Processing in an Herbal Tea Mail Order Business
    3. 3.3 Descriptive Statistics with the JMP Distribution Platform
      1. 3.3.1 Describing a Continuous Variable
      2. 3.3.2 Describing a Nominal Variable
    4. 3.4 Interpretation of Descriptive Statistics
      1. 3.4.1 Histogram
      2. 3.4.2 Box Plot
      3. 3.4.3 CDF Plot
      4. 3.4.4 Quantiles
      5. 3.4.5 Moments
    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
        1. Data Sets Are Small
        2. Data Are Rounded or Have Very Few Distinct Values
        3. The Distribution of Data Is Highly Asymmetric
    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
      1. 4.1.1 Building Statistical Models
      2. 4.1.2 Examining Examples of Statistical Models
      3. 4.1.3 Evaluating Statistical Models
    2. 4.2 Classification of Statistical Models
      1. 4.2.1 Models with a Single Y-Variable and No X-Variables
      2. 4.2.2 Models with a Single Y-Variable and a Single X-Variable
      3. 4.2.3 Models with a Single Y-Variable and Multiple X-Variables
      4. 4.2.4 Models with Multiple Y-Variables and No X-Variables
      5. 4.2.5 Models with Continuous Multiple Y-Variables and Some X-Variables
      6. 4.2.6 Other Approaches to Models
      7. 4.2.7 Other Models
    3. 4.3 Model Validation
      1. 4.3.1 Pitfalls of Model Building
      2. 4.3.2 Model Validation
      3. 4.3.3 Residuals in Model Validation
    4. 4.4 Summary
    5. 4.5 Problems
    6. 4.6 Case Study: Models of Advertising Effectiveness
        1. Business Problem
        2. Tasks
  10. 5. Discrete Probability Distributions
    1. 5.1 Introduction to Distributions
      1. 5.1.1 Random Variables
      2. 5.1.2 Independent Events
      3. 5.1.3 Discrete or Continuous Distributions
      4. 5.1.4 Characteristics of Distributions
      5. 5.1.5 General Shape of Distributions
    2. 5.2 Discrete Distributions
      1. 5.2.1 Probability Function p(y)
      2. 5.2.2 Cumulative Distribution Function F[y)
      3. 5.2.3 Mean, Variance, and Standard Deviation
    3. 5.3 Binomial Distribution
      1. 5.3.1 Binomial Distribution Characteristics
      2. 5.3.2 Calculating Binomial Probabilities in JMP
      3. 5.3.3 Practical Advice
    4. 5.4 Distributions of Two Discrete Random Variables (Y1, Y2)
      1. 5.4.1 Return of Two Stocks under Different Economic Conditions
      2. 5.4.2 Joint and Marginal Probability Functions
      3. 5.4.3 Covariance Cov[Y1, Y2] and Correlation Coefficient p12
      4. 5.4.4 Calculating E[Y1], E[Y1], E[Y12], E[Y22] and E[Y1,·Y2]
      5. 5.4.5 Variance and Standard Deviation of a Portfolio of Two Stocks
    5. 5.5 Summary
    6. 5.6 Problems
    7. 5.7 Case Study: Assessing Financial Investments
        1. Background
        2. Business Problem
  11. 6. Continuous Probability Distributions
    1. 6.1 Introduction to Continuous Distributions
    2. 6.2 Characteristics of Continuous Distributions
      1. 6.2.1 Density Function f(y)
      2. 6.2.2 Cumulative Distribution Function F[y)
      3. 6.2.3 General Shape of Continuous Distributions
      4. 6.2.4 Measures of Location and Spread
    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
        1. Standardizing Random Variables
        2. Example: Lifetime of Light Bulbs
      2. 6.4.2 Standard Normal Distribution
        1. Calculating Normal Probabilities in JMP
        2. Calculating Normal Distribution Quantiles in JMP
        3. Distribution of the Mean of Normally Distributed Outcomes
    5. 6.5 Central Limit Theorem
    6. 6.6 Sampling Distributions
      1. 6.6.1 Student's t-Distribution
      2. 6.6.2 Chi-Square Distribution
      3. 6.6.3 F-Distribution
    7. 6.7 Summary
    8. 6.8 Problems
    9. 6.9 Case Study: Julie's Lakeside Candy
        1. Business Problem
  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?
        1. Example: Estimating the Mean Number of Orders per
      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
      1. 7.3.1 What Is a Confidence Interval?
      2. 7.3.2 (1 - α) 100% Confidence Level
    4. 7.4 Detail Example: Package Delivery Times of Herbal Teas
    5. 7.5 JMP Analysis of Herbal Tea Package Delivery Times
      1. 7.5.1 Confidence Intervals for Mean and Standard Deviation
    6. 7.6 Prediction and Tolerance Intervals
      1. 7.6.1 Prediction Intervals
        1. Prediction Interval for a Single Future Observation
        2. Example: Delivery Times
        3. Prediction Intervals for k Future Observations
      2. 7.6.2 Tolerance Intervals
        1. Conclusion
    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
        1. Left-Sided Alternative Hypothesis
        2. Right-Sided Alternative Hypothesis
        3. Two-Sided Alternative Hypothesis
        4. Hypothesis Testing Steps
        5. Court Decisions and Hypothesis Testing
      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
        1. Left-Sided Alternative Hypothesis
        2. Right-Sided Alternative Hypothesis
        3. Two-Sided Alternative Hypothesis
      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
        1. Example: Herbal Tea Mail Order
    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)
        1. Standard Deviation σ Is Unknown–Use Sample Standard Deviation s
        2. Standard Deviation σ Is Known
        3. Confidence Interval for the Standard Deviation σ
      7. 8.2.7 Hypothesis Test for the Standard Deviation σ
        1. Normal Quantile Plot
    3. 8.3 Summary
    4. 8.4 Problems
    5. 8.5 Case Study: Traffic Speed Limit Change
        1. Business Problem
        2. Strategy
        3. Tasks
  15. 9. Comparing Two Means
    1. 9.1 Introduction
      1. 9.1.1 Hypotheses for Comparing Two Means
      2. 9.1.2 Paired t-Test versus Two-Sample t-Test
    2. 9.2 Two-Sample t-Test
      1. 9.2.1 Detail Example: Comparing Processor Speeds of Two Brands
      2. 9.2.2 Two-Sample t-Test in JMP
      3. 9.2.3 Interpretation of Two-Sample Results
    3. 9.3 Paired t-Test
      1. 9.3.1 Detail Example: Advertising Effect in Test Markets
      2. 9.3.2 Paired t-Test in JMP
      3. 9.3.3 Interpretation of Paired t-Test Results
    4. 9.4 Paired t-test versus Two-Sample t-test on the Same Data
      1. 9.4.1 Example: Abrasion Resistance
      2. 9.4.2 Summary of Differences between the Paired and Two-Sample t-test
    5. 9.5 Summary
    6. 9.6 Problems
    7. 9.7 Case Study: Westville Meat Processing Plant
        1. Business Problem
        2. Strategy
        3. Task
  16. 9.8 References
  17. 10. Comparing Several Means with One-Way ANOVA
    1. 10.1 Introduction
      1. 10.1.1 Fixed-Effect One-Way ANOVA
      2. 10.1.2 One-Way ANOVA Model
      3. 10.1.3 One-Way ANOVA Hypotheses
      4. 10.1.4 Sources of Variation
    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
        1. Each Pair, Student's t Confidence Intervals
        2. Other Compare Means in Fit Y by X
        3. Compare Means in Fit Model
        4. Crosstab Report of Fit Model
    4. 10.4 Checking Assumptions of ANOVA Model
      1. 10.4.1 Residuals
      2. 10.4.2 Example: Stock Indices by Industry
      3. 10.4.3 Side-by-Side Box Plots
      4. 10.4.4 Tests for Unequal Variances
      5. 10.4.5 Normal Quantile Plot of Residuals
    5. 10.5 Summary
    6. 10.6 Problems
    7. 10.7 Case Study: Carpal Tunnel Release Surgery
        1. Business Problem
        2. Task
  18. 11. Two-Way ANOVA for Comparing Means
    1. 11.1 Introduction
    2. 11.2 Two-Way ANOVA without Replications
      1. 11.2.1 Model and Sources of Variation
      2. 11.2.2 Hypotheses for Each Model Term
      3. 11.2.3 Detail Example: Prices of Wireless Mouse Devices by Brand at Different Stores
      4. 11.2.4 JMP Analysis of the Wireless Mouse Example Using the Fit Model Platform
    3. 11.3 Two-Way ANOVA with Equally Replicated Data
      1. 11.3.1 Model and Sources of Variation
      2. 11.3.2 Hypotheses for Each Model Term
      3. 11.3.3 What Is an Interaction Effect?
      4. 11.3.4 Detail Example: Profitability of Futures Trading Customers of a Financial Institution (Equal Replications)
      5. 11.3.5 JMP Analysis of Profitability of Futures Trading Customers
    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
        1. Business Problem
        2. Tasks
  19. 12. Proportions
    1. 12.1 Introduction
    2. 12.2 Proportions from a Single Sample
      1. 12.2.1 Introduction to Proportions
      2. 12.2.2 Detail Example: A Survey to Evaluate Brand Preference
      3. 12.2.3 Sample Size Considerations with Single Proportions
    3. 12.3 Chi-Square Test for Equality of k Proportions
      1. 12.3.1 Introduction to Chi-Square
      2. 12.3.2 Detail Example: Default Rates on a Specific Type of Loan (CBA Loan) by Credit Rating
      3. 12.3.3 The Pearson Chi-Square Statistic
    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
        1. Statistical Hypotheses and Chi-Square Statistic for Testing Independence
      4. 13.1.4 Example 1: Executive Transfers
        1. Analysis of Executive Transfer in JMP
    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
        1. Analysis of Consumer Preference of Two Cola Brands in JMP
      3. 13.2.3 Example 2: On-Time Performance of Package Delivery Companies
        1. Analysis of On Time Performance and Package Companies in JMP
        2. Stratified Analysis by Regular and Express Mail Packages
      4. 13.2.4 Example 3: Mortality after a High-Risk Procedure in Two Hospitals
        1. Analysis of Mortality after High Risk Procedure in JMP
    3. 13.3 Summary
    4. 13.4 Problems
    5. 13.5 Case Study: Financial Management Customer Satisfaction Survey
      1. Business Problem
      2. Task
  21. 13.6 References
  22. 14. Simple Regression Analysis
    1. 14.1 Introduction
      1. 14.1.1 General Simple Regression Problem and Use
      2. 14.1.2 Data Requirements
      3. 14.1.3 Basic Results
    2. 14.2 Detail Example: Yield in a Chemical Reactor
      1. 14.2.1 Situation, Research Question, Data Requirements, and Anticipated Results
      2. 14.2.2 Outputs Related to Problem Statement
    3. 14.3 JMP Analysis of the Yield in a Chemical Reactor Example
      1. 14.3.1 Simple Regression Results with the Fit Y by X Platform
      2. 14.3.2 Fit Y by X Results
      3. 14.3.3 Summary of Relevant Output
    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
        1. Yhat: Estimated Average or Predicted Value of Y at X0
        2. Obtaining Yhat in JMP
        3. Confidence Intervals for the Mean of Y at X0
        4. Obtaining Confidence Intervals in JMP using Fit Y by X
        5. Obtaining Confidence Intervals in JMP Using Fit Model
        6. Prediction Intervals for a Single Future Y at X0
    5. 14.5 How Good Is the Regression Line?
      1. 14.5.1 Scatterplot
      2. 14.5.2 Significance of Slope Coefficient
      3. 14.5.3 RMSE Relative to Std Dev[Mean]
      4. 14.5.4 RSquare: Coefficient of Determination R2
      5. 14.5.5 Plots to Verify Simple Regression Assumptions
      6. 14.5.6 Residual Plots
    6. 14.6 Important Considerations
      1. 14.6.1 Slope Estimates b. Are Sensitive to Outliers
      2. 14.6.2 Hazards of Extrapolation Beyond the Range of the Data
      3. 14.6.3 Confidence Intervals for b0 and b1
    7. 14.7 Summary
    8. 14.8 Problems
    9. 14.9 Case Study: Lost Time Occupational Injuries
      1. Business Problem
      2. Strategy
      3. Tasks
  23. 15. Simple Regression Extensions
    1. 15.1 Simple Correlation
      1. 15.1.1 Introduction
        1. Sample Correlation Coefficient and Sample Covariance
      2. 15.1.2 Example: Correlation of Financial Indices
        1. Correlation Coefficients in JMP
        2. Covariances in JMP
        3. Testing Significance of a Correlation Coefficient
      3. 15.1.3 Data Patterns and Correlation Coefficients
        1. Relation of Correlation Coefficient r with Simple Regression R2 and Slope b1
    2. 15.2 Regression and Stock Market Returns
      1. 15.2.1 Introduction
        1. Example: Comparing the Risk of General Electric, SP500 and 13-week Treasury Bill
        2. Asset Variability as a Measure of Total Risk
      2. 15.2.2 Capital Asset Pricing Model (CAPM)
        1. Expected Return on Market
        2. Expected Return on Individual Stock
        3. Example: GE and Its Three-Month Daily Beta for 2006
        4. Interpreting the Slope Coefficients
        5. A Hypothesis Test Associated with Beta
        6. Using Covariance and Variance of Returns to Estimate Beta
    3. 15.3 Curvilinear Regression
      1. 15.3.1 Introduction
      2. 15.3.2 Quadratic Regression with Fit Polynomial
        1. Example: Detecting Possible Voting Irregularities
      3. 15.3.3 Fitting Curves with Fit Special
      4. 15.3.4 Detail Example: Price Elasticity of Demand
        1. Point Price Elasticity EP
        2. Meaning of EP
        3. Elasticity and Regression
        4. Elasticity under Unitary Demand
        5. Example: Demand for Chicken
    4. 15.4 Summary
    5. 15.5 Problems
      1. 15.5.1 Correlation
      2. 15.5.2 Stock Market Returns
      3. 15.5.3 Curvilinear Regression
    6. 15.6 Case Studies
      1. 15.6.1 Case Study 1: Comparing Correlation between Individual Stocks and an Index
      2. 15.6.2 Case Study 2: Comparing the Risk of Two Market Sectors
      3. 15.6.3 Case Study 3: Experience Curve
      4. 15.6.4 Case Study 4: Engel's Law
  24. 16. Multiple Regression Analysis
    1. 16.1 Introduction
      1. 16.1.1 Data Requirements for Multiple Regression
      2. 16.1.2 Multiple Regression Analysis Process
      3. 16.1.3 Multiple Regression Model
    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
        1. Simple Correlations
        2. Scatterplot Matrix
        3. Simple Regression Analysis with Individual Independent Variables
      2. 16.3.2 Fitting Several X-Variables to Y
        1. Multiple Regression Model with All Three Independent Variables
        2. Summary of Fit from Fit Model
        3. Parameter Estimates Table
      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
        1. Conclusions from Two X-Variable Model
    4. 16.4 Evaluating Model Assumptions and Goodness of Fit
      1. 16.4.1 Residuals ei
      2. 16.4.2 Residuals ei and RMSE
      3. 16.4.3 Residuals and R2
      4. 16.4.4 R2 and RMSE
      5. 16.4.5 R2adj
      6. 16.4.6 Plot of Residuals versus Predicted Y (Yhat) or versus Time Order
      7. 16.4.7 Durbin-Watson Test for the Independence of Residuals
        1. Example: Dow Jones 30 Industrials Adjusted Close
    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
        1. Check the Slopes of the Equation
        2. Check for Multi-Collinearity Variance Inflation Factors (VIFs)
        3. Global Statistical Test in Multiple Regression
    6. 16.6 Summary
    7. 16.7 Problems
    8. 16.8 Case Study: Forbes Global 2000 High Performers
      1. Tasks
  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
      1. 17.3.1 Using JMP for Regression Analysis with a Nominal Variable
      2. 17.3.2 Interpretation of Parameter Estimates and Associated Hypothesis Tests
    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
      1. 17.5.1 Detail Example: Growth versus General Equity Fund
      2. 17.5.2 Using JMP to Model Two Slopes and Two Intercepts
    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
      1. 18.3.1 R2 Criterion
      2. 18.3.2 RMSE Criterion
    4. 18.4 Stepwise Regression
      1. 18.4.1 Stepwise Regression Algorithms
      2. 18.4.2 Stepwise Regression in JMP
        1. Choice of Prob to Enter and Prob to Leave
        2. Forward Stepwise Regression
        3. Backward Stepwise Regression
        4. Mixed Stepwise Regression
    5. 18.5 Candidate Models
        1. Evaluating Candidate Models
        2. Model with Five Variables from Forward Stepwise Regression
        3. Model with Four Variables from Backward Stepwise Regression
        4. Model with Three Variables from All Possible 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
        1. Non-Statistical Reasons
        2. Overriding Statistical Criteria
    7. 18.7 Summary
    8. 18.8 Problems
    9. 18.9 Case Studies
      1. 18.9.1 Case Study 1: Real Estate Appraisal
        1. Tasks
      2. 18.9.2 Case Study 2: Discrimination in Compensation?
  28. 19. Exponential Smoothing Models for Time Series Data
    1. 19.1 Introduction
        1. Data Requirements for Exponential Smoothing
    2. 19.2 Detail Example: 10-Year Treasury Note Closing Prices
        1. Describing Time Series Data in JMP
        2. Other Time Series Methods
        3. Differences
    3. 19.3 Smoothing Models
      1. 19.3.1 Simple Moving Averages
        1. Simple Moving Average Calculation
        2. Weights of Observations in a MA(k)
        3. Average Age of Observations in a MA(k)
        4. Example of MA(3): EDO Stock Price
      2. 19.3.2 Exponential Smoothing Models
        1. Estimates and Smoothing Weights
      3. 19.3.3 Simple Exponential Smoothing (SES)
        1. SES Weights
        2. Simple Exponential Smoothing in JMP
      4. 19.3.4 Double Exponential Smoothing (DES) For Linear Trend
        1. DES Example: ABC Stock Price
        2. SES and DES Behavior When the Level Changes
      5. 19.3.5 Winters' Additive Seasonal Method
        1. Example: Monthly Sales of High End Audio Components
        2. Winters' Additive Method in JMP
    4. 19.4 Summary
    5. 19.5 Problems
    6. 19.6 Case Study: Lockheed Martin Stock in Changing Times
        1. Business Problem
        2. Strategy
        3. Tasks
  29. 19.7 References

Product information

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