Prism Features | SciExperts

Prism

Analyze, graph and present scientific data faster than ever with Prism!

Statistical Comparisons

Paired or unpaired t tests. Reports P values and confidence intervals.
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 Discover Rate (or Bonferroni multiple comparisons)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.
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.
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.

Clinical (Diagnostic) Lab Statistics

Bland-Altman plots.
Receiver operator characteristic (ROC) curves.
Deming regression (type ll 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.