-Paired or unpaired t tests. Reports P values and confidence intervals.
-Nonparametric Mann-Whitney test, including confidence interval of difference of medians.
-Wilcoxon test with confidence interval of median.
-Perform many t tests at once, using False Discover Rate to choose which comparisons are discoveries to study further.
-Ordinary or repeated measures one-way 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.
-Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.
-Greenhouse-Geisser correction so repeated measures one-way ANOVA does not have to assume sphericity. When this is chosen, multiple comparison tests also do not assume sphericity.
-Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn's post test.
-Fisher's exact test or the chi-square test. Calculate the relative risk and odds ratio with confidence intervals.
-Two-way ANOVA, even with missing values with some post tests.
-Two-way ANOVA, with repeated measures in one or both factors. Tukey, Newman-Keuls, Dunnett, Bonferron, Holm-Sidak, or Fishers LSD multiple comparisons testing main and simple effects.
-Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend).
-Calculate min, max, quartiles, mean, SD, SEM, CI, CV,
-Mean or geometric mean with confidence intervals.
-Frequency distributions (bin to histogram), including cumulative histograms.
-Normality testing by three methods.
-One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.
-Skewness and Kurtosis.
-Identify outliers using Grubbs or ROUT method.
Linear regression and correlation
-Calculate slope and intercept with confidence intervals.
-Force the regression line through a specified point.
-Fit to replicate Y values or mean Y.
-Test for departure from linearity with a runs test.
-Calculate and graph residuals.
-Compare slopes and intercepts of two or more regression lines.
-Interpolate new points along the standard curve.
-Pearson or Spearman (nonparametric) correlation.
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