 # JMP Learning Library

## Basic Inference - Proportions and Means

### Hypothesis Test and CI for Proportions

Hypothesis testing and confidence intervals for proportions.

###### JMP features demonstrated:

Analyze > Distribution

### Chi-Square Test for a Two-Way Table

Using Fit Y by X for Chi-Square test of homogeneity and independence.

###### JMP features demonstrated:

Analyze > Fit Y by X

### Sample Size and Power for Testing Proportions

Calculate sample size and power for tests involving one or two sample proportions.

###### JMP features demonstrated:

DOE > Sample Size and Power

### One Sample t-test and CI

One sample t-Test and confidence interval for the mean.

###### JMP features demonstrated:

Analyze > Distribution

### Two Sample t-Test and CIs

Two sample t-Test and confidence intervals for two independent means.

###### JMP features demonstrated:

Analyze > Fit Y by X

### Paired t-Test and CI

Paired t-Test and confidence interval for the difference between paired means.

###### JMP features demonstrated:

Analyze > Matched Pairs

### One Way ANOVA

Confidence intervals for means, one way ANOVA, and multiple comparison procedures.

###### JMP features demonstrated:

Analyze > Fit Y by X

### Two-Way (Factorial) ANOVA

Two-Way (Factorial) ANOVA for testing the effects of two categorical variables (factors) and their interaction on one continuous (response) variable.

###### JMP features demonstrated:

Analyze > Fit Model

### Nonparametric Tests

This page describes how to perform single and two-group nonparametric tests in JMP.

###### JMP features demonstrated:

Distribution, Fit Y by X

### Sample Size and Power for Testing Means

Calculate sample size and power for tests involving means.

###### JMP features demonstrated:

DOE > Sample Size and Power

### Bootstrapping in JMP Pro

Re-sampling for estimating the sampling distribution of a statistic

###### JMP features demonstrated:

Bootstrapping, bootstrap confidence limits

### Randomization Testing in JMP Pro

This page provides information on randomization testing (also known as permutation testing), which is a resampling approach to significance testing.

###### JMP features demonstrated:

Resampling with replacement, simulation