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• 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. Age of empires 3 warchiefs crack torrent. • 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. • Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third). • Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend). Column statistics • 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. • Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify “significant” findings or discoveries. Nonlinear regression • Fit one of our 105 built-in equations, or enter your own. • Enter differential or implicit equations.
• Enter different equations for different data sets. • Global nonlinear regression – share parameters between data sets. • Robust nonlinear regression. • Automatic outlier identification or elimination. • Compare models using extra sum-of-squares F test or AICc.
• Compare parameters between data sets. • Apply constraints. • Differentially weight points by several methods and assess how well your weighting method worked.