This video covers descriptive statistics (both numeric and visual) for a single numeric variable. Inference for a single population mean is also covered, i.e. confidence intervals for the population mean and hypothesis testing (t-test & Wilcoxon signed-rank test) are covered. (Datafile: Walleye Fish Lake.JMP)
This video covers descriptive statistics (both numeric and visual) for a single dichotomous (two-level) nominal variable. Inference for a single population proportion is also covered. The data file used in this tutorial is created during the video.
This video covers inferential methods for comparing two population means where the samples are drawn in a dependent fashion. This means either we looking at the same experimental unit twice (pre-test vs. post-test) or where experimental units in the two populations being compared are matched according to some criteria on one-to-one basis. (Datafile: Captopril.JMP)
This video covers inferential methods for comparing two population means where the samples are drawn independently. Both the pooled and non-pooled t-test are covered. In order to formally test the equality of variance assumption the F-test for comparing two population variances is covered. Also the Wilcoxon rank sum test (Mann-Whitney test) are briefly discussed. (Datafile: Cadmium-Hemo.JMP and BreastDiag.JMP)
This video covers inferential methods for comparing two popuation proportions (or percentages) where the samples are drawn independently. The results from the chi-square test, Fisher's exact test, and the large sample normal theory test (Wald test) are discussed. Confidence intervals for the difference in the population proportions (risk difference), the relative risk (RR), and the odds ratio (OR) are also discussed. (Datafiles: Breast Surgery.JMP and NC Births (n = 10000).JMP)
This video covers one-way ANOVA and multiple comparison procedures. It uses both the Fit Y by X approach and the Fit Model approach for fitting a one-way ANOVA, though the latter is covered very briefly. (Datafile: Anorexia.JMP)
This video covers conducting a one-way ANOVA where the effect of interest is random vs. fixed. For this situation we must use the Fit Model approach for conducting our one-way ANOVA. Variance component estimates will be found and discussed. (Datafile: Looms.JMP)
This video covers analysis of results from a randomzied complete block design experiement. (Datafile: Serum-Method.JMP)
This video covers two-way ANOVA in JMP. (Datafile: Capsule.JMP)
This video covers multi-factor ANOVA in JMP using a three-way ANOVA example. (Datafile: Body Armor.JMP)
This video covers a repeated measures MANOVA with two treatment groups. (Datafile: Minoxidil.JMP)
This video covers an example of MANOVA where the multiple responses in different scales and/or units are measured for each subject in k groups.
(Datafile: Pizza.JMP)
Another file you can use to explore these ideas is data coming from a study of the fatty acid content of Italian olive oils. (Datafile: Olive Oils.JMP)
This cover the basics of simple linear regression using Analyze > Fit Y by X to fit the model. (Datafile: CampLake.JMP)
This covers the basics of simple linear regression using Analyze > Fit Model to fit the SLR model. (Datafile: CampLake.JMP)
This cover the basics of fitting a multiple regression model. We will fit a basic multiple regression model using the Berkley Guidance Study data for girls only and examine how to check model assumptions and how to explore model effects using the prediction profiler in JMP. (Datafile: BGSgirls.JMP)
This covers stepwise methods for "automatic" model selection in multiple regression. (Datafile: Saratoga NY Homes.JMP)