UNISTAT

UNISTAT Statistical Software

Unistat Stand-Alone | Unistat Excel Add-in | UNISTAT Light | Developer SDK

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What is UNISTAT ?

Unistat is a statistical data analysis tool featuring two modes of operation: The Unistat Stand-Alone user interface is a complete workbench for data input, analysis, and visualization while the Unistat Excel Add-in mode extends the features of the mainstream spreadsheet application with powerful analytical capabilities to Microsoft Excel.

 

Who Uses UNISTAT ?

Unistat's Is popular among  biomedicine researchers, social scientists, market researchers, government departments and students. This software enabling them to perform complex data analysis without the need for large manuals and scripting languages.

Unistat is used in both industry and academia. Unistat caters to the statistical analysis and visualization needs of a wide spectrum of users - from absolute beginners to the seasoned analytics experts, and six sigma black belts.

Do I need to be a statistical expert to use Unistat?

Absolutely not! Even if you are not a statistics expert, Unistat makes it easy for you to learn statistics by helping users focus on the analysis results and the impact of the result on a business / practical scenario, rather than spending time on learning formulas and performing complicated statistical calculations.

Which industries use Unistat?

Companies use Unistat to analyse their data, and help improve their processes, get deep insights, design experiments, and solve some of the most difficult problems. Few of the industries using Unistat:

Apparel Healthcare Manufacturing
Automotive Food and beverage
Pharmaceuticals
Banking & Insurance Government
Semi-Conductors
Chemicals Medical Devices Services
Energy & Resources Nonprofit

What are UNISTAT Main Features?

  • Unistat Excel Add-in
  • Unistat Stand-Alone
  • Graphics
  • Reporting
  • Developer SDK

UNISTAT Excel Add-In

Best-practice statistical data analysis in Microsoft Excel is difficult with existing add-ins and other software tools. UNISTAT is different, adding the power and accuracy of a full-featured statistical package to your existing analysis and visualization workflow.

 

UNISTAT as an Excel Add-in

Using UNISTAT as Excel addin is easy. All you need to do is select a block of data and then select a procedure from the UNISTAT menus. UNISTAT’s procedure dialogues are intuitive to use and you will not feel as if you are dealing with another program.

 

By default, UNISTAT will create its output in a new Excel worksheet. You will also have the option to send the same output to Word or HTML for the web.

 

All numeric results from UNISTAT have up to 15-digit precision, though you can choose to display them with only the desired number of digits. This prevents the build-up of rounding errors when output from one procedure is used as input for another procedure.

Statistical analysis in Excel

3D Bar Chart Friedman Two-Way ANOVA Multinomial Regression Multiple Comparisons
Reliability Analysis
3D Histogram Gauge R&R Analysis Multiple Comparisons Multiple Discriminant Analysis Ribbon Chart
Analysis of Variance General Linear Model Multiple Discriminant Analysis Multisample Median Test
Sample Size & Power: ANOVA
Area Chart Heterogeneity of Regression Multisample Median Test Neumann Trend Test
Sample Size & Power: Correlation
ARIMA Hierarchical Cluster Analysis Neumann Trend Test Nonlinear Regression
Sample Size & Power: One Sample
Attribute Control Charts High-Low-Close Chart Nonlinear Regression Normal Probability Plot
Sample Size & Power: Phi Distribution
Bar Chart Histogram Normal Probability Plot Normality Tests
Sample Size & Power: Two Correlations
Binomial Proportion Holt’s Linear Forecasting Normality Tests Outlier Tests
Sample Size & Power: Two Proportions
Bland-Altman Plot Homogeneity of Variance Tests Outlier Tests Page’s L Trend
Sample Size & Power: Two Samples
Box-Cox Regression Hotelling’s T2 Analysis Page’s L Trend Paired Proportions
Sample Size & Power: Variance
Box-Whisker, Dot and Bar Plots Hotelling’s T2 Test Paired Proportions Paired Samples
Sample Statistics
Break-Down Icon Plots Paired Samples Parallel Line Method Scatter Diagram
Brown’s Exponential Forecasting Intraclass Correlation Coefficients Parallel Line Method Parametric Tests Matrix
Sequence Diagram
Canonical Correlations Inverse Fourier Transform Parametric Tests Matrix Pareto Chart
Slope Ratio Method
Chi-square Tests Jonckheere’s Trend Pareto Chart Partial Correlation Matrix
Spectral Diagram
Cochran’s Q K-Means Cluster Analysis Partial Correlation Matrix Pearson-Spearman-Kendall Matrix
Stem and Leaf Plot
Combination of Assays K-th Neighbour Cluster Analysis Pearson-Spearman-Kendall Matrix Pie Chart
Stepwise Regression
Confidence Intervals K-th Neighbour Discriminant Analysis Pie Chart Plot & Roots of Polynomials
Summary Statistics
Contingency Table Kaplan-Meier Analysis Plot & Roots of Polynomials Plot of 2D Functions
Survival Comparison Statistics
Correlation Coefficients Kappa Test Inter-Category Plot of 2D Functions Plot of 3D Functions t- and F-Tests
Cox Regression Kappa Test Inter-Observer Plot of 3D Functions Plot of Distribution Functions Table of Means
Critical Value Kendall’s Concordance Coeff Plot of Distribution Functions Poisson Regression
Unpaired Proportions
Cross-Tabulation Kolmogorov-Smirnov Tests Poisson Regression Polar Plot
Unpaired Samples
Cumulative Probability Kruskal-Wallis One-Way ANOVA Polar Plot Polynomial Regression
Variable Control Charts
Cylinder-Plate 5+1 Assay Ladder Plot Polynomial Regression Principal Components Weibull Analysis
Data Transformation Life Table Principal Components Probabilities and Critical Values
Winters’ Additive Seasonal Forecasting
Equivalence Test for Means Linear Regression Probabilities and Critical Values Process Capability Analysis
Winters’ Multiplicative Seasonal Forecasting
Expected Frequencies Logistic Regression Process Capability Analysis Quade Two-Way ANOVA X-Y Plots
Factor Analysis Logit / Probit / Gompit Quade Two-Way ANOVA Quantal Response Method X-Y-Z Grid Plot
Fan Grid Plot Matrix Plot Quantal Response Method Quantiles
X-Y-Z Scatter Plot
Four-Parameter Logistic Regression Matrix Statistics Meta Analysis Random Numbers X-Y-Z Spin Plot
Fourier Transform Meta Analysis Multidimensional Scaling
Rectangular Plot
Frequency Distributions Multidimensional Scaling Multinomial Regression
Regression with Replicates

UNISTAT Stand-Alone

The main advantages of using UNISTAT in stand-alone mode (i.e. when running independently of Excel) are:

  • it does not have the data size restrictions of Excel,
  • has advanced data export / import options that are not available in Excel,
  • its built-in spreadsheet (Data Processor) provides efficient data handling procedures like:
    • Aggregate: compresses large data sets according to the levels of a factor variable
    • Recode: assigns new values to the specified value ranges of a variable
    • Stack: stacks blocks of data columns and creates an additional factor variable to keep track
    • Unstack: separates blocks of data according to the levels of a factor variable

    and specialised statistical functions like:

    • Rank: computes ranks with ties
    • MdRk: computes exact median ranks using the binomial function
    • Freq: creates two new variables containing levels and frequencies of a factor variable
    • Level: generates a balanced factor variable
    • Dummy: generates n dummy variables for a factor variable with n levels
Spreadsheet

UNISTAT Spreadsheet
UNISTAT comes complete with a fully-featured Excel-like spreadsheet specialised for statistical data handling.
Data Processor can cater for different types of data such as numeric, date and time, short and long strings, missing and no-data codes.
In stand-alone mode UNISTAT has practically no limitations on the size of data files. You can handle files with millions of cases and thousands of variables. The only limiting factor is the PC’s memory. In Excel add-in mode the data capacity is limited only by Excel. The data capacity of Light Edition is limited by 50 columns and 1500 rows.
Advanced Data Export / Import

UNISTAT Import/Export

Files can be opened, merged or saved in Excel, Access, CSV and a wide range of text file formats.
In addition to the commonly used delimited text (CSV) file format, UNISTAT’s Data Processor supports ASCII by row (or by case, where the first cases of all variables are read first and then the second cases of all variables, etc.) and ASCII by column (or by variable, where all cases in the first variable are read first and then all cases in the second variable, etc.). For further information see User’s Guide section 3.1.0.5. Free Format ASCII Files.
ODBC and SQL

UNISTAT ODBC and SQL

It is possible to open any database for which drivers are installed in the Windows system. It is also possible to execute SQL statements to extract subsets from large databases.
The SQL dialogue allows construction of SQL statements to select a subsample of fields and cases from a database.

UNISTAT Light Edition

UNISTAT Light is a scaled-down version of UNISTAT Statistical Package, offering exceptional value for money. You can find out exactly which features are included in UNISTAT Light from Products page. You will notice that UNISTAT Light Edition is far more comprehensive and more powerful than almost any other statistics add-ins available on the market, even including some of the additional modules they charge separately.

 

You can download and try UNISTAT Light now. Until you buy a licence, it will work in demo mode with the example files provided. When you buy a licence, we will activate the program by issuing a license key.

Difference between Light and Standard editions

UNISTAT Light is identical to the Standard Edition except:
  1. Data size is limited by 50 columns and 1500 rows
  2. Available only in the English language
  3. Macros and developer mode are not available
  4. The following procedures are not available: meta analysis, nonlinear, logit / probit / gompit, logistic, multinomial, Poisson, Box-Cox regressions, Multivariate Analysis (cluster, discriminant, mutidimensional scaling, principal components, factor, reliability) and Time Series Analysis (ARIMA, forecasting, quality control, survival, Fourier analysis) and the optional analysis of bioassays module.

 

UNISTAT Light has all advanced 2D / 3D scientific graphics features of UNISTAT. It also features the full suite of parametric, nonparametric and goodness of fit tests, correlation coefficients, contingency tables, ANOVA, General Linear Model, post-hoc tests, linear regression with model building facilities and sample size and power procedures.

UNISTAT Graphics Engine

UNISTAT features one of the best graphics subsystems that can be found in any statistical package.

 

UNISTAT supports full on-screen object editing of graphs. All text, legends, and the graph itself can be drag-dropped and resized and new text and shape objects added. The final configuration can be saved as a graphics template file for future use. All graphics attributes including fonts, colours, titles, legends, axes, frame, tick marks, line styles, line thickness can be controlled from the graphics menu.

 

The current chart or report can be sent to Word, Excel or to the web browser with a single click. Graphs can be also be exported to other applications via the clipboard or as a file in the form of bitmap images or Windows metafiles.

 

For further information see UNISTAT User’s Guide sections Graphics Editor and X-Y Plots.

Axis Scaling Options

UNISTAT can display axis titles and X-axis tick labels at 0°, 90°, 270° rotations or in top-to-bottom orientation, format the scale numbers and enter mathematical expressions for the minimum, maximum, minor and major tick values (e.g. 2*Pi()). Scale types available include linear, log base 10, log base e, log to any user-defined base, reciprocal, logit, probit, gompit and loglog. For further information see User’s Guide section Scale Type.

 

Linear scale Linear scale Log 10 scale Log 10 scale Log 10 scale Log 10 scale Log e log scale User-defined log scale User-defined log scale Reciprocal scale Logit scale Probit scale Gompit scale Gompit scale LogLog scale Time scale

 

Text Formatting

Richtext formatted axis title
Richtext formatted axis title

It is possible to mix different fonts and sub / superscripts in any text object.
You can copy and paste formatted text between UNISTAT text objects and Microsoft Word or other Windows applications.
Titles can be displayed at 0º, 90º, 270º rotations.
Multiple Y-Axes

Multiple Y-axes

Up to five y-axes can be displayed; one on the left and four on the right, allowing representation of diverse data sets on the same graph.
Each axis can be individually controlled with all available scaling options.
Means Plot

Means plot

X-Y Plots, Polar Plot, Bar Chart, Area Chart and Ribbon Chart procedures allow plotting the means of the data series with optional error bars. Error bar options include t- and Z-interval (at any confidence level), standard error and standard deviation (with user-defined multipliers) and variance. For further information see User’s Guide Section Means Plot.
It is also possible to select a continuous variable for the X-Axis, where one or more Y-Axis variables have multiple values corresponding to the same X-Axis variable value. A typical case is the dose-response plot where there are several response variable values for each dose level.
Means plot

Means plot

Error Bars

Asymmetric error bars

In addition to the error bar options for means plot, you can define any variable to be displayed as error bars.
This is a powerful feature allowing vertical / horizontal, symmetric / asymmetric error bars for each data point on 2D and 3D scatter plots, bar, area and ribbon charts. For further information see User’s Guide Section Error Bars.
Trend Lines

Trend lines with confidence intervals

You can display a line of best fit (linear least squares) for each data series separately.
Confidence interval curves for the mean of Y and / or actual Y values can be drawn at any confidence level.
When only one variable is plotted, it is also possible to fit another curve on the series.
Curve Fitting

Curve fitting

Five different types of curves can be fitted on a bivariate plot, Neville, rational, polynomial, exponential and geometric.
R-squared and standard error values and estimated coefficients are displayed in the legend for polynomial, geometric and exponential fits. It is possible to display residual bars on the graph. For further information see User’s Guide Section Curve Fitting.
Surface Fitting

Surface fitting

It is possible to fit 3D surfaces, linear regression planes, polynomial and weighted average surfaces on X-Y-Z scatter plots.
Contour lines of the surface can be shown on either or both of the bottom and top planes. Alternatively, a separate 2D contour map can be drawn with extended annotation possibilities. For further information see User’s Guide Section Surface Fitting.
3D Options

3D viewpoint and perspective

All 3D graphics procedures allow rotation and selection of the view point, as well as selecting one of parallel, one-point, or three-point perspectives.
The Spin Plot procedure permits the drawing of an X-Y-Z scatter diagram of three variables and rotation of the plot in all directions in real time.
Interactive Graphics

Interactive graphics

You can display information about a data point by pressing the and holding the right-mouse button on it. 2D and 3D scatter diagrams, 3D spin, polar, normal probability and regression plots support this feature. In stand-alone mode, the row of the spreadsheet containing the point is also highlighted simultaneously.
In this way, it is easy to determine which points are outliers in the data set. In regression plots, if the <Delete> key is pressed while a point is highlighted, then this point will be omitted and the graph redrawn with a new regression equation.

UNISTAT Reporting Results

One of the most powerful aspects of UNISTAT Statistical Package is its ability to create presentation-quality reports in Microsoft Word and Excel.

 

When UNISTAT is run in stand-alone mode, by default, all output is sent to a WordPad-like window, which is an integral part of UNISTAT Statistical Package. In Excel add-in mode, the output is sent to a new worksheet within Excel by default. After this, it is possible to send the same output to a number of other applications, without having to run the procedure again. The Output Medium Toolbar allows you to send the output to Word, Excel, web browser or to the Windows system clipboard.

 

It is also possible to set any one of these output media as default, in which case, the output is created directly in the new default medium.

 

In this way, it is possible to use Output Window as the primary preview window and send only the final results to Word (or Excel or the web browser) for inclusion in a final report. In stand-alone mode, UNISTAT can also send its output tables to its own spreadsheet (the Data Processor) for further analysis.
Output to Excel

UNISTAT Output to Excel

When Output to Excel button is clicked, output is sent to a new worksheet in the active workbook. Output is formatted in the form of Excel tables directly within Excel and the graphics are in scaleable enhanced metafile format. You can edit the Excel styles created by UNISTAT to change colours and fonts.
All numeric results from UNISTAT have the full 15-digit precision, though you can choose to display them with only the desired number of digits. This prevents the build-up of rounding errors when output from one procedure is used as input for another procedure.
Output to Word

UNISTAT Output to Word

UNISTAT does not simply send the old style line printer output to Word. Instead, it re-formats its output fully utilising the specific formatting capabilities of Word. When the Output to Word button is clicked, UNISTAT will format its tables directly within Word, in the form of Word tables.
Output is sent to the current document in Word, at the current cursor position. The graphics are in scaleable enhanced metafile format.
You can change the number of digits displayed for numbers from UNISTAT’s Tools / Options dialogue.
Output to the Web

UNISTAT Output to Web

Output is sent to the default web browser in the form of HTML tables and PNG format bitmap images.
The output is styled using CSS, allowing easy integration into web sites, wikis, blogs and other web-based applications.
UNISTAT Output Window

UNISTAT Output Window

In stand-alone mode, by default, all output is sent to UNISTAT’s own Output Window. All text output is created in old style line printer format and displayed with a fixed-width font.
The graphic output is sent to this window in the form of an enhanced metafile object. The Output Window offers all the functionality of WordPad.

UNISTAT as Developer’s Tool

It is possible to run UNISTAT from another application without any part of UNISTAT appearing on the screen. If the developer has an application which requires the use of UNISTAT’s powerful data analysis and graphics procedures, then UNISTAT can be called from within the application in any programming language (C, C++, C#, VB, VBA, Pascal, FORTRAN, etc.), passing data and receiving results. This facility makes UNISTAT a powerful yet flexible programmer’s utility for corporate users and specialised scientific software developers.

When it is run in developers mode, each target PC requires a separate UNISTAT licence.

You can try the Developer SDK for UNISTAT Version while running UNISTAT in demo mode. Please contact Us for a copy.

UNISTAT Product Comparison Table

FEATURES Light Edition Standard Edition Standard Edition + Bioassays Module
  Data Capacity 50 x 1500 * unlimited * unlimited
  Modes of Running UNISTAT yes yes yes
    Stand-Alone Mode yes yes yes
    Excel Add-In Mode yes yes yes
    Developer SDK yes yes
  Variable Options yes yes yes
    Categorical Data Analysis yes yes yes
    Multiple Dependent Variables yes yes yes
    Creating Interaction Dummy and Lag/Lead Variables yes yes yes
  Output Medium yes yes yes
    UNISTAT Output Window yes yes yes
    Output to Word yes yes yes
    Output to Excel yes yes yes
    Output to Web Browser yes yes yes
    Macros yes yes
      Recording Macros yes yes
      Running Macros yes yes
      Combining Macros yes yes
      Macro Shortcut Buttons yes yes
    Log File yes yes
    Help yes yes yes
DATA PROCESSOR
  Spreadsheet Functions yes yes yes
    Information yes yes yes
    Transpose Matrix yes yes yes
    Column Sort yes yes yes
    Key Sort yes yes yes
    Matrix Sort yes yes yes
    Recode Column yes yes yes
    Aggregate yes yes yes
    Range Statistics yes yes yes
    Stack Columns yes yes yes
    Unstack Columns yes yes yes
    Format Columns yes yes yes
    Select Row yes yes yes
  Formula yes yes yes
    Mathematical Operators and Functions yes yes yes
    Trigonometric Functions 1 yes yes yes
    Trigonometric Functions 2 yes yes yes
    Scalar Functions yes yes yes
    Statistical Functions yes yes yes
    Special Functions yes yes yes
    Data Conversion Functions yes yes yes
    Date and Time Functions yes yes yes
    UNISTAT Functions yes yes yes
    Conditional Functions yes yes yes
    Constants yes yes yes
DATA AND FUNCTION PLOTS
  2D Plots yes yes yes
    X-Y Plots yes yes yes
      Data Series yes yes yes
      Line yes yes yes
      Symbols yes yes yes
      Error Bars yes yes yes
      Point Labels yes yes yes
      Right Y-Axes yes yes yes
      Area Enclosed yes yes yes
      Curve Fitting yes yes yes
      Means Plot yes yes yes
    Polar Plot yes yes yes
    Spectral Diagram yes yes yes
    Fan Grid Plot yes yes yes
  3D Plots yes yes yes
    X-Y-Z Scatter Plot yes yes yes
      Viewpoint yes yes yes
      Contours yes yes yes
      X-Y-Z Points yes yes yes
      Planes yes yes yes
      Surface Fitting yes yes yes
    X-Y-Z Grid Plot yes yes yes
    Spin Plot yes yes yes
  Charts yes yes yes
    Pie Chart yes yes yes
    Bar Chart yes yes yes
    Area Chart yes yes yes
    Ribbon Chart yes yes yes
    3D Bar Chart yes yes yes
    High-Low-Close Chart yes yes yes
  Plot of Functions yes yes yes
    Plot of 2D Functions yes yes yes
    Plot of 3D Functions yes yes yes
    Plot and Roots of Polynomials yes yes yes
    Plot of Distribution Functions yes yes yes
DESCRIPTIVE STATISTICS AND DISTRIBUTIONS
  Descriptive Statistics yes yes yes
    Summary Statistics yes yes yes
    Confidence Intervals yes yes yes
    Quantiles (Percentiles) yes yes yes
    Sample Statistics yes yes yes
    Frequency Distributions yes yes yes
    Stem and Leaf Plot yes yes yes
    Sequence Diagram yes yes yes
    Scatter Diagram yes yes yes
  Distribution Functions yes yes yes
    Cumulative Probability yes yes yes
    Critical Value yes yes yes
    Probabilities and Critical Values yes yes yes
    Random Numbers yes yes yes
    Expected Frequencies yes yes yes
  Descriptive Plots yes yes yes
    Box-Whisker, Dot and Bar Plots yes yes yes
      Box and Whisker Plot yes yes yes
      Dot Plot yes yes yes
      Error Bar Plot yes yes yes
    Normal Probability Plot yes yes yes
    Histogram yes yes yes
    3D Histogram yes yes yes
    Bland-Altman Plot yes yes yes
    Ladder Plot yes yes yes
STATISTICAL TESTS CORRELATIONS AND TABLES
  Parametric Tests yes yes yes
    t- and F-Tests yes yes yes
      One Sample t-Test yes yes yes
      Pooled Variance t-Test yes yes yes
      Separate Variance t-Test yes yes yes
      Paired t-Test yes yes yes
      F-Test yes yes yes
      Levene’s F-Test yes yes yes
    Equivalence Test for Means yes yes yes
    Parametric Tests Matrix yes yes yes
    Hotelling’s T-Squared Test yes yes yes
  Correlations yes yes yes
    Correlation Coefficients yes yes yes
      Pearson Product Moment Correlation yes yes yes
      Spearman’s Rank Correlation yes yes yes
      Kendall’s Rank Correlation yes yes yes
      Point Biserial Correlation yes yes yes
    Pearson-Spearman-Kendall Correlations Matrix yes yes yes
    Partial Correlation Matrix yes yes yes
    Intraclass Correlation Coefficients yes yes yes
  Goodness of Fit Tests yes yes yes
    Chi-Square Tests yes yes yes
      One Sample Chi-Square Test yes yes yes
      Two Sample Chi-Square Test yes yes yes
    Kolmogorov-Smirnov Tests yes yes yes
      One Sample Kolmogorov-Smirnov Test: Uniform Distribution yes yes yes
      One Sample Kolmogorov-Smirnov Test: Normal Distribution yes yes yes
      Two Sample Kolmogorov-Smirnov Test yes yes yes
    Normality Tests yes yes yes
      Shapiro-Wilk Test yes yes yes
      Kolmogorov-Smirnov Test yes yes yes
      Cramer-von Mises Test yes yes yes
      Anderson-Darling Test yes yes yes
    Outlier Tests yes yes yes
      Dixon yes yes yes
      Grubbs yes yes yes
      ESD Generalised Extreme Studentised Deviate yes yes yes
  Nonparametric Tests with One or Two Samples yes yes yes
    Unpaired Samples yes yes yes
      Mann-Whitney U Test yes yes yes
      Hodges-Lehmann Estimator (Unpaired) yes yes yes
      Wald-Wolfowitz Runs Test yes yes yes
      Moses Extreme Reaction Test yes yes yes
      Two Sample Median Test yes yes yes
    Paired Samples yes yes yes
      Wilcoxon Signed Rank Test yes yes yes
      Hodges-Lehmann Estimator (Paired) yes yes yes
      Sign Test yes yes yes
      Table of Ranks yes yes yes
    Binomial Proportion yes yes yes
      Runs Test yes yes yes
      Binomial Test yes yes yes
      Noninferiority Test yes yes yes
      Superiority Test yes yes yes
      Equivalence Test for Binomial Proportion yes yes yes
    Unpaired Proportions yes yes yes
      Difference Between Unpaired Proportions yes yes yes
      Risk Ratio yes yes yes
      Odds Ratio and Relative Risks yes yes yes
    Paired Proportions yes yes yes
      Difference Between Paired Proportions yes yes yes
      Fisher’s Exact Test yes yes yes
      McNemar Test yes yes yes
      Odds Ratio (Paired) yes yes yes
      Tetrachoric Correlation yes yes yes
  Multisample Nonparametric Tests yes yes yes
    Kruskal-Wallis One-Way ANOVA yes yes yes
      Kruskal-Wallis ANOVA Test Results yes yes yes
      Kruskal-Wallis ANOVA Multiple Comparisons yes yes yes
    Jonckheere’s Trend yes yes yes
    Multisample Median Test yes yes yes
      Multisample Median Test Results yes yes yes
      Median Multiple Comparisons yes yes yes
    Friedman Two-Way ANOVA yes yes yes
      Friedman ANOVA Test Results yes yes yes
      Friedman ANOVA Multiple Comparisons yes yes yes
    Quade Two-Way ANOVA yes yes yes
      Quade ANOVA Test Results yes yes yes
      Quade ANOVA Multiple Comparisons yes yes yes
    Kendall’s Concordance Coefficient yes yes yes
    Page’s L Trend yes yes yes
    Cochran’s Q yes yes yes
    Kappa Test for Inter-Category Variation yes yes yes
    Kappa Test for Inter-Observer Variation yes yes yes
  Tables yes yes yes
    Contingency Table yes yes yes
    Cross-Tabulation yes yes yes
      Scores yes yes yes
      Tables yes yes yes
      R x C Table Statistics yes yes yes
      Chi-square Tests yes yes yes
      Fisher’s Exact Test yes yes yes
      Measures of Association yes yes yes
      Cochran-Armitage Trend Test yes yes yes
      Cohen’s Kappa yes yes yes
      2 x 2 Table Statistics yes yes yes
      Stratified Analysis yes yes yes
      Conditional Independence yes yes yes
      Common Odds Ratio and Relative Risks yes yes yes
      Homogeneity of Odds Ratio yes yes yes
    Break-Down yes yes yes
  Sample Size and Power Estimation yes yes yes
    One Sample yes yes yes
      Sample Size in Estimating the Population Mean yes yes yes
      Sample Size in Tests Concerning the Mean yes yes yes
      Minimum Detectable Difference for One Sample yes yes yes
      Power of the Test for One Sample yes yes yes
    Two Samples yes yes yes
      Sample Size in Estimating Two Population Means yes yes yes
      Sample Size in Tests Concerning Two Means yes yes yes
      Minimum Detectable Difference for Two Samples yes yes yes
      Power of the Test for Two Samples yes yes yes
    Variance yes yes yes
      Sample Size for Variance yes yes yes
      Power of the Test for Variance yes yes yes
    Correlation yes yes yes
      Sample Size for Correlation yes yes yes
      Power of the Test for Correlation yes yes yes
    Two Correlations yes yes yes
      Sample Size for Two Correlations yes yes yes
      Power of the Test for Two Correlations yes yes yes
    Two Proportions yes yes yes
      Sample Size for Two Proportions yes yes yes
      Power of the Test for Two Proportions yes yes yes
    ANOVA yes yes yes
      Sample Size for ANOVA yes yes yes
      Maximum Number of Groups for ANOVA yes yes yes
      Minimum Detectable Difference for ANOVA yes yes yes
      Power of the Test for ANOVA yes yes yes
    Phi Distribution yes yes yes
      Inverse Phi Distribution yes yes yes
  Meta Analysis yes yes
REGRESSION AND ANALYSIS OF VARIANCE
  Matrix Statistics yes yes yes
  Regression Analysis yes yes yes
    Linear Regression yes yes yes
    Polynomial Regression yes yes yes
    Stepwise Regression yes yes yes
    Nonlinear Regression yes yes
    Logit / Probit / Gompit / Loglog yes yes
    Logistic Regression yes yes
    Multinomial Regression yes yes
    Poisson Regression yes yes
    Box-Cox Regression yes yes
  Analysis of Variance and General Linear Model yes yes yes
    Analysis of Variance yes yes yes
    General Linear Model yes yes yes
      Randomised Block Designs yes yes yes
      Repeated Measures Designs yes yes yes
      Repeated Measures over all Factors yes yes yes
      Repeated Measures over some Factors yes yes yes
      Latin Squares Designs yes yes yes
      Graeco Latin Squares Designs yes yes yes
      Split-Plot Designs yes yes yes
      Nested Designs yes yes yes
      Crossover Designs yes yes yes
  Tests for ANOVA yes yes yes
    Table of Means yes yes yes
    Homogeneity of Variance Tests yes yes yes
    Multiple Comparisons yes yes yes
      Student-Newman-Keuls yes yes yes
      Tukey-HSD yes yes yes
      Tukey-B yes yes yes
      Duncan yes yes yes
      Scheffe yes yes yes
      Least Significant Difference (LSD) yes yes yes
      Bonferroni (Modified LSD) yes yes yes
      Dunnett yes yes yes
    Regression with Replicates yes yes yes
    Heterogeneity of Regression yes yes yes
MULTIVARIATE ANALYSIS
  Cluster Analysis yes yes
    Hierarchical Cluster Analyses yes yes
      Distance Measures yes yes
      Distance Matrix yes yes
      Hierarchical Methods yes yes
    K-th Neighbour Cluster Analysis yes yes
    K-Means Cluster Analysis yes yes
  Discriminant Analysis yes yes
    Multiple Discriminant Analysis yes yes
      Stepwise Discriminant Analysis yes yes
      Linear Discriminant Analysis yes yes
      Canonical Discriminant Analysis yes yes
    K-th Neighbour Discriminant Analysis yes yes
  Multidimensional Scaling yes yes
    Classical Multidimensional Scaling yes yes
    Ordinal Multidimensional Scaling yes yes
  Principal Components Analysis yes yes
  Factor Analysis yes yes
    Factoring Methods yes yes
    Rotations yes yes
  Canonical Correlations yes yes
  Reliability Analysis yes yes
  Multivariate Plots yes yes
    X-Y Matrix Plot yes yes
    X-Y Rectangular Plot yes yes
    Icon Plots yes yes
TIME SERIES ANALYSIS
  Box-Jenkins ARIMA yes yes
    Differencing Input Options yes yes
    Differencing Output Options yes yes
    Model Fitting yes yes
      Seasonal and Nonseasonal Operators yes yes
      Model Fitting Parameters yes yes
    Model Output Options yes yes
    Forecasting yes yes
  Forecasting and Smoothing yes yes
    Exponential Weights Moving Average yes yes
    Brown’s Exponential yes yes
    Holt’s Linear yes yes
    Winter’s Additive Seasonal yes yes
    Winter’s Multiplicative Seasonal yes yes
    Neumann Trend Test yes yes
  Quality Control yes yes
      Operating Characteristic Curve yes yes
    Variable Control Charts yes yes
      R Chart yes yes
      S Chart yes yes
      Variance Chart yes yes
      X Bar Chart yes yes
      Moving Average Charts yes yes
      Standard Moving Average Charts yes yes
      Exponential Weights Moving Average Chart yes yes
      CUSUM Chart yes yes
      Table of Values yes yes
      X Chart (Levey-Jennings) yes yes
    Attribute Control Charts yes yes
      C Chart yes yes
      U Chart yes yes
      Np Chart yes yes
      P Chart yes yes
    Pareto Chart yes yes
    Hotelling’s T-Squared Analysis yes yes
    Weibull Analysis yes yes
      Two-Parameter Maximum Likelihood Estimation yes yes
      Two-Parameter OLS Estimation yes yes
      Three-Parameter OLS Estimation yes yes
    Process Capability Analysis yes yes
      Performance yes yes
      Capability Indices with Overall Standard Deviation yes yes
      Capability Indices with Pooled Standard Deviation yes yes
      Nonparametric Capability Indices yes yes
      Capability Histogram yes yes
    Data Transformation yes yes
      Johnson Transformation yes yes
      Box-Cox Transformation yes yes
    Gauge / Gage R&R Analysis yes yes
      Gage R&R Average and Range Method yes yes
      Gage R&R ANOVA Method yes yes
      Gage R&R Charts yes yes
  Survival Analysis yes yes
    Survival Analysis Variable Selection yes yes
    Life Table yes yes
    Kaplan-Meier Analysis yes yes
      Product Limit Survival Table yes yes
      Quantiles of Survival Function yes yes
      Kaplan-Meier Plots yes yes
    Survival Comparison Statistics yes yes
      Wilcoxon Tests: Gehan (Lee Desu) Breslow yes yes
      Logrank Test: Mantel-Haenszel (Peto) yes yes
    Cox Regression yes yes
  Fourier Analysis yes yes
    Fourier Transform yes yes
    Inverse Fourier Transform yes yes
ANALYSIS OF BIOASSAYS
  Parallel Line Method yes
    Validity of Data yes
      Summary Statistics yes
      Normality Tests yes
      Homogeneity of Variance Tests yes
      Outlier Tests yes
      Box-Whisker, Dot and Bar Plots yes
      Normality Plots yes
    Validity of Assay yes
      Completely Randomised Design yes
      Randomised Block Design yes
      Latin Squares Design yes
      Twin Crossover Design yes
      Triple Crossover Design yes
    Regression yes
    Comparison of Slopes yes
    Potency yes
    Plot of Treatment Means yes
  Slope Ratio Method yes
    Validity of Data yes
    Validity of Assay yes
    Regression yes
    Potency yes
    Plot of Treatments yes
  Quantal Response Method yes
    Regression yes
      Logit yes
      Probit yes
      Gompit yes
      Loglog yes
    Validity of Assay yes
    Effective Dose (or Lethal Dose) yes
    Potency yes
    Plot of Treatments yes
  Four-Parameter Logistic Regression yes
    4PL EP yes
    Full Model USP yes
      Effective Dose yes
      Predictions with Confidence and Prediction Intervals yes
      Measures of Variability yes
      Equivalence Tests yes
      Outlier Plot yes
      Homogeneity of Variance Plot yes
      Dose-Response Plot yes
    Reduced Model USP yes
      Parallelism Tests yes
      Potency yes
      Dose-Response Plot yes
  Combination of Assays yes
    Homogeneity Tests yes
    Combined Potency EP yes
    Combined Potency USP yes
    Plot of Potencies yes
  Specific Assays yes
    Cylinder-Plate 5+1 Assay yes

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