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  • Advanced Linear Regression and Correlation Assessment
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  • Nonlinear regression with many options
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  • Paired and unpaired T-tests using the mean period

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Key Features:

Statistical Comparisons:

  • Paired or unpaired t-test. Reports the P-value and the confidence interval.
  • Automatically generates volcano graphs (differences from P values) from multiple t-test analyzes.
  • Nonparametric Mann-Whitney test, including confidence intervals for differences in medians.
  • Kolmogorov-Smirnov test to compare two groups.
  • Wilcoxon test with median confidence intervals.
  • Perform multiple t-tests at once, using the false discovery rate (or Bonferroni’s multiple comparisons) to choose which ratio is a discovery for further study.
  • The usual or repeated ANOVA measurements are followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni, or Holm-Sidak double comparison test, the trend post-test, or the Fisher’s Least Significant test.
  • One-way ANOVA without assuming that the population with the same standard deviation uses Brown-Forsythe and Welch ANOVA, followed by an appropriate comparison test (Games-Howell, Tamhane T2, Dunnett T3)
  • Many multiple comparison tests were accompanied by confidence intervals and P values ​​adjusted for variability.
  • Greenhouse-Geisser correction so that repeated measurements of one, two, and three-factor ANOVA do not necessarily assume roundness. If this option is selected, the multiple comparison test does not assume roundness either.
  • Kruskal-Wallis or Friedman unidirectional nonparametric ANOVA with Dunn’s post-test.
  • Fisher’s exact test or chi-square test. Calculate the relative risk and the ratio of opportunities with confidence intervals.
  • Two-way ANOVA, even with missing values ​​with multiple post-tests.
  • Bidirectional ANOVA, with repeated measurements on one or both factors. The multiple LSD comparisons of Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher tested the main and simple effects.
  • Three-way ANOVA (limited to two levels in two factors and several levels in the third).
  • Analysis of repeated measurement data (one, two, and three directions) using a mixed-effects model (similar to repeated measures ANOVA, but capable of handling missing data).
  • Kaplan-Meier survival analysis. Compare the curves with log-rank tests (including trend tests).
  • Compare data from nested data tables using a nested t-test or a one-way nested ANOVA (using a mixed-effects model).

Nonlinear Regression:

  • Match one of our 105 built-in equations or enter your own. It now belongs to the family of growth equations: exponential growth, exponential plateau, Gompertz, logistics, and beta (growth then decrease).
  • Enter a differential or implicit equation.
  • Enter different equations for different sets of data.
  • Global nonlinear regression – parameters shared between datasets.
  • Strong nonlinear regression.
  • Automatic identification or elimination of outliers.
  • Compare the patterns using the F or AICc test for the number of additional squares.
  • Compare the settings between the datasets.
  • Apply limits.
  • Give different weighting points by several methods and assess the effectiveness of your weighting method.
  • Accept an automatic initial estimate or enter your own.
  • Automatically curves over the specified range of X values.
  • Calculate the accuracy of the match to the SE or CI parameters. Confidence intervals can be symmetric (like traditional) or skewed (which is more precise).
  • Calculate the symmetrical imprecision with the slope of the Hougaard.
  • Plotting beliefs or prediction bands.
  • Residual normality test.
  • Run or replicate the model sufficiency tests.
  • Specify the covariance matrix or the set of dependencies.
  • Easily interpolate the points of the most suitable curve.
  • Fit the straight lines to the two data sets and determine the point of intersection and the two slopes.

Principal Component Analysis (PCA):

  • Selection of components via parallel analysis (Monte Carlo simulation), Kaiser criteria (threshold eigenvalues), the proportion of variance thresholds, etc.
  • Scree plots, loading plots, biplots, and many more are generated automatically.
  • Use the results in downstream applications such as principal component regression.

Multiple Variable Graphing:

  • Define the variables that determine the coordinates, color, and size of the axis.
  • Create a bubble chart.

Column Statistics:

  • Calculate descriptive statistics: min, max, quartiles, mean, SD, SEM, CI, CV, asymmetry, kurtosis.
  • The geometric mean or mean with confidence intervals.
  • Frequency distribution (bin to histogram) including a cumulative histogram.
  • Normality test using four methods (new: Anderson-Darling).
  • Lognormality test and sampling probability of normal (Gaussian) vs. lognormal.
  • Create a QQ diagram as part of the normality tests.
  • A sample t-test or Wilcoxon test to compare the means (or medians) of the columns with the theoretical values.
  • Identification of contours using the Grubbs or ROUT method.
  • Analysis of the p-value stack, using Bonferroni’s multiple comparisons or the FDR approach to identify “significant” results.

Simple Linear Regression and Correlation:

  • Calculate the slope and intercept with confidence intervals.
  • Force the regression line through a specific point.
  • Suitable for replicating a Y value or a Y average.
  • Test for deviation from linearity with the run test.
  • Calculate and graph the residuals in four different ways (including QQ plots).
  • Compare the slopes and intersections of two or more regression lines.
  • Interpolate new points along a standard curve.
  • Pearson or Spearman correlation (nonparametric).

Generalized Linear Models (GLMs):

  • Build a model that connects multiple independent variables to a dependent variable using a data table of multiple new variables.
  • Multiple linear regression (when Y is continuous).
  • Poisson regression (when Y is counted; 0, 1, 2,…).
  • Logistic regression (if Y is binary; yes / no, pass / fail, etc.).

Clinical (Diagnostic) Lab Statistics:

  • The Bland-Altman plot.
  • Reception of the operator characteristic curve (ROC).
  • Deming regression (type II linear regression).


  • Simulate an XY, Column, or Contingency table.
  • Repeat the analysis of the simulated data as a Monte Carlo analysis.
  • The function plots the equation you choose or enter and the parameter values ​​you choose.

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Other Calculations:

  • The area under the curve, with confidence intervals.
  • Transform the data.
  • Standardization.
  • Identification of outliers.
  • Normality test.
  • Change the order of the table.
  • Subtract the baseline (and join the columns).
  • Count each value as a fraction of the row, column, or total.

Analysis Bug Fixes:

  • Fixed the issue in which Multiple Logistic Regression analysis results were incorrectly updated upon changing categorical Negative and Positive outcome values in the parameters dialog.
  • Fixed the issue in which Prism would crash while attempting to perform Nonlinear Regression with an implicit equation with no constraint for one of the parameters.
  • Fixed the issue in which the profile likelihood CI was incorrectly calculated for some models of the Nonlinear Regression analysis.
  • Fixed the issue in which Multiple Logistic Regression results were incorrectly updated after changing data for the dependent (outcome) categorical variable.
  • Fixed the issue in which results of the “Smooth, Differentiate, or Integrate Curve” analysis were incorrect when the source data contained missing Y values.
  • Fixed the issue in which the alert “This file was created by a new version of Prism” appeared in Prism 8 upon opening a Prism 9 project with t-test analysis results that did not contain an Estimation Plot.
  • Fixed the issue in which Prism would freeze while attempting to perform Nonlinear Regression with options to identify “unstable” parameters and an equation containing the “beta(j,k)” function.
  • Fixed the issue in which the floating note “The model has changed. Please, re-define” did not appear in the Multiple Linear and Logistic Regression results if variables used in the model were deleted from the input data.
  • Fixed the issue in which the “β0=(Undefined)” VIF constant of the Multiple Logistic Regression analysis was wrongly reported in the Hook constant dialog.
  • Fixed the issue in which Prism would crash when attempting to perform Multiple linear regression with 1023 interactions without an intercept term.
  • (Win) Fixed the issue in which it was impossible to switch to the results of Multiple logistic regression after changing variable types in the source data table.
  • (Win) Fixed the issue in which incorrect level names appeared in the “Negative/Positive” Outcomes dropdown menus in the Multiple Logistic Regression parameters dialog.
  • (Win) Fixed the issue in which the yellow iteration window did not appear when attempting to perform Multiple Logistic Regression for categorical variables with a huge number of levels.
  • (Mac) Fixed the issue in which it was possible to select the same value for both the Positive and Negative Outcome in the Multiple Logistic Regression parameters dialog.
  • (Mac) Fixed the issue in which the “Recalculate automatic reference levels” checkbox was unexpectedly enabled in the Reference Level tab of the Multiple Linear Regression parameters dialog if the “Define reference level” section was empty.
  • (Mac) Fixed the issue in which the text string “Unpaired (two-sample t-test)” unexpectedly appeared in the “Parameters: Multiple t-tests” dialog when selecting an Unpaired Experimental Design and the option for “nonparametric” under “Assume Gaussian distribution?”.
  • (Mac) Updated Prism to automatically select the first two data sets for the “Multiple t-tests” analysis invoked via the analysis toolbar button from a data table with three or more data set.

Other Bug Fixes:

  • (Mac) Added, “Save a Copy” and “Rename This File” items to the list of Save commands on the main toolbar.
  • (Mac) Fixed the crash that occurred when opening a Prism document containing a grouped bar graph with a vertical separating line generated from an analysis.
  • (Mac) Fixed a rare crash that occurred when opening a Prism project containing certain pairwise comparison lines on a graph.
  • (Mac) Fixed the issue in which the “Format Graph” and “Pairwise Comparisons” buttons on the toolbar unexpectedly appeared inactive (dimmed) for residual graphs.
  • (Mac) Fixed the issue in which the “Delete Sheet(s)” dialog appeared instead of the “Delete all [ ] sheets in the family?” error message after clicking on the “Family” folder and pressing the Delete key on the keyboard.
  • (Mac) [macOS Big Sur] Fixed the issue in which the “Order” column title on the “Reference Level” tab of the “Parameters: Multiple Logistic Regression” dialog was clipped.
  • (Mac) Fixed the typo in the Connecting Line Options tab of the Format Graph dialog for multiple variables graphs.

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System Requirements:

  • Operating System: Windows 7/8/10
  • Processor: Pentium IV or higher
  • RAM: 1 GB RAM (2 GB recommended)
  • Hard Disk Space: 200 MB or more

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