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Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values. One-way ANOVA without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3) Ordinary or repeated measures ANOVA followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni or Holm-Sidak multiple comparison tests, the post-test for trend, or Fisher’s Least Significant tests. Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further. Wilcoxon test with confidence interval of median.
Two-way ANOVA, even with missing values with some post tests. Calculate the relative risk and odds ratio with confidence intervals. Fisher's exact test or the chi-square test. Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn's post test. When this is chosen, multiple comparison tests also do not assume sphericity.
Kaplan-Meier survival analysis. Analysis of repeated measures data (one-, two-, and three-way) using a mixed effects model (similar to repeated measures ANOVA, but capable of handling missing data). Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third). Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher’s LSD multiple comparisons testing main and simple effects.
Differentially weight points by several methods and assess how well your weighting method worked. Compare models using extra sum-of-squares F test or AICc. Automatic outlier identification or elimination. Global nonlinear regression – share parameters between data sets.
Runs or replicates test of adequacy of model. Quantify symmetry of imprecision with Hougaard’s skewness. Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate). Quantify precision of fits with SE or CI of parameters. Automatically graph curve over specified range of X values.
Specify variables defining axis coordinates, color, and size. Use results in downstream applications like Principal Component Regression. Automatically generated Scree Plots, Loading Plots, Biplots, and more. Component selection via Parallel Analysis (Monte Carlo simulation), Kaiser criterion (Eigenvalue threshold), Proportion of Variance threshold, and more. Fit straight lines to two data sets and determine the intersection point and both slopes. Easily interpolate points from the best fit curve.
One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value. Create QQ Plot as part of normality testing. Lognormality test and likelihood of sampling from normal (Gaussian) vs. Normality testing by four methods (new: Anderson-Darling). Frequency distributions (bin to histogram), including cumulative histograms. Mean or geometric mean with confidence intervals.
Compare slopes and intercepts of two or more regression lines. Calculate and graph residuals in four different ways (including QQ plot). Test for departure from linearity with a runs test. Force the regression line through a specified point. Calculate slope and intercept with confidence intervals. Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify "significant" findings or discoveries.
Multiple linear regression (when Y is continuous). Generate models relating multiple independent variables to a single dependent variable using the new multiple variables data table. Pearson or Spearman (nonparametric) correlation.
Deming regression (type ll linear regression). Receiver operator characteristic (ROC) curves. Logistic regression (when Y is binary yes/no, pass/fail, etc.).
Subtract baseline (and combine columns). Area under the curve, with confidence interval. Plot functions from equations you select or enter and parameter values you choose. Repeat analyses of simulated data as a Monte-Carlo analysis.
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