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 without replacement, simulation