Planned_features

Statistical Comparisons

• Paired or unpaired t tests. Reports P values and confidence intervals.

• Automatically generate volcano plot (difference vs. P value) from multiple t test analysis.

• Nonparametric Mann-Whitney test, including confidence interval of difference of medians.

• Kolmogorov-Smirnov test to compare two groups.

• Wilcoxon test with confidence interval of median.

• Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further.

• 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.

• 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)

• Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.

• Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do 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, Bonferroni, Holm-Sidak, or Fisher’s 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).

• 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).

• Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend).

• Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model).

Nonlinear Regression

• Fit one of our 105 built-in equations, or enter your own. Now including family of growth equations: exponential growth, exponential plateau, Gompertz, logistic, and beta (growth and then decay).

• 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.

• Accept automatic initial estimated values or enter your own.

• Automatically graph curve over specified range of X values.

• Quantify precision of fits with SE or CI of parameters. Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate).

• Quantify symmetry of imprecision with Hougaard’s skewness.

• Plot confidence or prediction bands.

• Test normality of residuals.

• Runs or replicates test of adequacy of model.

• Report the covariance matrix or set of dependencies.

• Easily interpolate points from the best fit curve.

• Fit straight lines to two data sets and determine the intersection point and both slopes.

Column Statistics

• Calculate descriptive statistics: min, max, quartiles, mean, SD, SEM, CI, CV, skewness, kurtosis.

• Mean or geometric mean with confidence intervals.

• Frequency distributions (bin to histogram), including cumulative histograms.

• Normality testing by four methods (new: Anderson-Darling).

• Lognormality test and likelihood of sampling from normal (Gaussian) vs. lognormal distribution.

• Create QQ Plot as part of normality testing.

• One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.

• Identify outliers using Grubbs or ROUT method.

• Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify "significant" findings or discoveries.

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 in four different ways (including QQ plot).

• Compare slopes and intercepts of two or more regression lines.

• Interpolate new points along the standard curve.

• Pearson or Spearman (nonparametric) correlation.

• Multiple linear regression (including Poisson regression) using the new multiple variables data table.

Clinical (Diagnostic) Lab Statistics

• Bland-Altman plots.

• Receiver operator characteristic (ROC) curves.

• Deming regression (type II linear regression).

Simulations

• Simulate XY, Column or Contingency tables.

• Repeat analyses of simulated data as a Monte-Carlo analysis.

• Plot functions from equations you select or enter and parameter values you choose.

Other Calculations

• Area under the curve, with confidence interval.

• Transform data.

• Normalize.

• Identify outliers.

• Normality tests.

• Transpose tables.

• Subtract baseline (and combine columns).

• Compute each value as a fraction of its row, column or grand total.