- Contents
- Learn about JMP
- Introduction to Basic Analysis
- Distributions
- Using the Distribution Platform
- Overview of the Distribution Platform
- Example of the Distribution Platform
- Launch the Distribution Platform
- The Distribution Report
- Distribution Platform Options
- Options for Categorical Variables
- Options for Continuous Variables
- Display Options Submenu for Continuous Variables
- Histogram Options Submenu for Continuous Variables
- Normal Quantile Plot
- Outlier Box Plot
- Quantile Box Plot
- Stem and Leaf
- CDF Plot
- Test Mean
- Test Std Dev
- Test Equivalence
- Confidence Intervals for Continuous Variables
- Save Commands for Continuous Variables
- Prediction Intervals
- Tolerance Intervals
- Capability Analysis
- Fit Distributions
- Additional Examples of the Distribution Platform
- Statistical Details for the Distribution Platform
- Standard Error Bars
- Quantiles
- Summary Statistics
- Normal Quantile Plot
- Wilcoxon Signed Rank Test
- Standard Deviation Test
- Normal Quantiles
- Saving Standardized Data
- Prediction Intervals
- Tolerance Intervals
- Capability Analysis
- Continuous Fit Distributions
- Discrete Fit Distributions
- Fitted Quantiles
- Fit Distribution Options
- Introduction to Fit Y by X
- Bivariate Analysis
- Examine Relationships between Two Continuous Variables
- Example of Bivariate Analysis
- Launch the Bivariate Platform
- The Bivariate Plot
- Fitting Commands and Options
- Histogram Borders
- Fit Mean
- Fit Line and Fit Polynomial
- Fit Special
- Flexible
- Fit Orthogonal
- Robust
- Density Ellipse
- Nonpar Density
- Group By
- Fitting Menus
- Additional Examples of the Bivariate Platform
- Statistical Details for the Bivariate Platform
- Oneway Analysis
- Examine Relationships between a Continuous Y and a Categorical X Variable
- Overview of Oneway Analysis
- Example of Oneway Analysis
- Launch the Oneway Platform
- The Oneway Plot
- Oneway Platform Options
- Quantiles
- Means/Anova and Means/Anova/Pooled t
- Analysis of Means Methods
- Compare Means
- Nonparametric Tests
- Unequal Variances
- Equivalence Test
- Robust
- Power
- Normal Quantile Plot
- CDF Plot
- Densities
- Matching Column
- Additional Examples of the Oneway Platform
- Example of an Analysis of Means Chart
- Example of an Analysis of Means for Variances Chart
- Example of the Each Pair, Student’s t Test
- Example of the All Pairs, Tukey HSD Test
- Example of the With Best, Hsu MCB Test
- Example of the With Control, Dunnett’s Test
- Example Contrasting All of the Compare Means Tests
- Example of the Nonparametric Wilcoxon Test
- Example of the Unequal Variances Option
- Example of an Equivalence Test
- Example of the Robust Fit Option
- Example of the Power Option
- Example of a Normal Quantile Plot
- Example of a CDF Plot
- Example of the Densities Options
- Example of the Matching Column Option
- Example of Stacking Data for a Oneway Analysis
- Statistical Details for the Oneway Platform
- Contingency Analysis
- Examine Relationships between Two Categorical Variables
- Example of Contingency Analysis
- Launch the Contingency Platform
- The Contingency Report
- Contingency Platform Options
- Mosaic Plot
- Contingency Table
- Tests
- Analysis of Means for Proportions
- Correspondence Analysis
- Cochran-Mantel-Haenszel Test
- Agreement Statistic
- Relative Risk
- Two Sample Test for Proportions
- Measures of Association
- Cochran Armitage Trend Test
- Exact Test
- Additional Examples of the Contingency Platform
- Example of Analysis of Means for Proportions
- Example of Correspondence Analysis
- Example of a Cochran Mantel Haenszel Test
- Example of the Agreement Statistic Option
- Example of the Relative Risk Option
- Example of a Two Sample Test for Proportions
- Example of the Measures of Association Option
- Example of the Cochran Armitage Trend Test
- Statistical Details for the Contingency Platform
- Logistic Analysis
- Examine Relationships between a Categorical Y and a Continuous X Variable
- Overview of Logistic Regression
- Example of Nominal Logistic Regression
- Launch the Logistic Platform
- The Logistic Report
- Logistic Platform Options
- Additional Examples of Logistic Regression
- Statistical Details for the Logistic Platform
- Tabulate
- Simulate
- Answer Challenging Questions with Parametric Resampling
- Overview of Simulate
- Examples That Use Simulate
- Launch the Simulate Window
- The Simulate Window
- The Simulate Table
- Bootstrapping
- Approximate the Distribution of a Statistic through Resampling
- Overview of Bootstrapping
- Example of Bootstrapping
- Bootstrapping Window Options
- Stacked Results Table
- Unstacked Bootstrap Results Table
- Analysis of Bootstrap Results
- Additional Example of Bootstrapping
- Statistical Details for Bootstrapping
- Text Explorer
- Explore Unstructured Text in Your Data
- Text Explorer Platform Overview
- Example of the Text Explorer Platform
- Launch the Text Explorer Platform
- The Text Explorer Report
- Text Explorer Platform Options
- Latent Class Analysis
- Latent Semantic Analysis (SVD)
- Topic Analysis
- Additional Example of the Text Explorer Platform
- Statistical Details
- References
- Index
Chapter 11
BootstrappingApproximate the Distribution of a Statistic through Resampling
Bootstrapping is available only in JMP Pro.
Bootstrapping is a resampling method for approximating the sampling distribution of a statistic. You can use bootstrapping to estimate the distribution of a statistic and its properties, such as its mean, bias, standard error, and confidence intervals. Bootstrapping is especially useful in the following situations:
• The theoretical distribution of the statistic is complicated or unknown.
• Inference using parametric methods is not possible because of violations of assumptions.
Note: Bootstrap is available only from a right-click in a report. It is not a platform command.
Figure 11.1 Bootstrapping Results for a Slope Parameter
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