NCSS PASS is one of the leading software tools for the design of medical trials and pharmaceutical or medical research in general. PASS provides the right methods for the power analysis of over 650 different statistical tests, confidence intervals and research scenarios.
Fast import of historical data – even in foreign data formats – and the simple interface help you focusing on the real problem: determining just the right sample size for your study.
Getting the right sample size involves just three steps:
 Choose the right study design from the navigator
 Enter the required parameters (typically: noise/standard deviation, relevant effect)
 Interpretation of results (Power, sample size)
That’s how easy NCSS PASS makes power calculation. All results will be visualized by highlevel scientific graphs. All graphs are highly customizable and will add an additional level of clarity to your reports.
NCSS PASS offers:
 Calculation of sample size and power
 Validated procedures
 Easy to learn, easy to use
 Professional graphics
 Helpful documentation as part of the output
 Easy export to all commonly used text editors
Recommended products
NCSS
PASS
NCSS and PASS  bundled Power for your Success!
PASS has been finetuned for over 20 years, and has become the leading sample size software choice for clinical trial, pharmaceutical, and other medical research. It has also become a mainstay in all other fields where sample size calculation or evaluation is needed. PASS software performs power analysis and calculates sample sizes for over 680 statistical tests.
PASS Overview
PASS is a standalone system
Although it is integrated with NCSS, you do not have to own NCSS to run it. You can use it with any statistical software you want.
PASS is accurate
It has been extensively verified using books and reference articles. Proof of the accuracy of each procedure is included in the extensive documentation.
PASS comes with complete help system documentation
That contains tutorials, examples, annotated output, references, formulas, validation, and complete instructions on each procedure. All procedures are validated with published articles or books.
Choosing A Procedure 
Enter The Values 
NCSS  Statistical Analysis System
Comprehensive, Easy to Use, Statistical Software running under Windows 8, Windows 7, Vista, XP (32bit and 64bit). NCSS software provides a complete and easytouse collection of hundreds of statistical and graphics tools to analyze and visualize your data. From using NCSS you will benefit in several ways:
 Comprehensive and accurate.
 Inexpensive
 Includes over 150 statistical and graphical tools.
 Easy to learn and use.
 Fully compatible to 32bit and 32bit versions of Windows XP/Vista, Windows 7, 8 and Windows 10!
 Imports/exports major spreadsheet, database, and statistical file formats.
 Sharp, flexible graphics.
 NCSS output is easily transferred to popular word processors and presentation software such as PowerPoint.
 Processes large data files (over 1,000 variables and 200,000 rows)
Discover NCSS
Choosing A Procedure 
Further Information
 PASS and NCSS Homepage from the producer NCSS
Minimum System Requirements for PASS
In order to run PASS, your computer must meet the following minimum standards:
 Processor:
 450 MHz or faster processor
 32bit (x86) or 64bit (x64) processor
 RAM:
 256 MB (512 MB recommended)
 Operating Systems:
 Windows 10 or later
 Windows 8.1
 Windows 8
 Windows 7
 Windows Vista with Service Pack 2 or higher
 Windows Server 2016 or later
 Windows Server 2012 R2
 Windows Server 2012
 Windows Server 2008 SP2/R2
 Privileges:
 Administrative rights required during installation only
 Third Party Software:
 Microsoft .NET 4.6 (Comes preinstalled with Windows 10 or later and Windows Server
2016 or later. Installation required on Windows 8.1 or earlier and Windows Server
2012 R2 or earlier. For systems where .NET 4.6 installation is required, a .NET 4.6
download helper will start automatically when you run the PASS setup file.)  Microsoft Windows Installer 3.1 or higher
 Adobe Reader® 7 or higher (required for the Help System only)
 Microsoft .NET 4.6 (Comes preinstalled with Windows 10 or later and Windows Server
 Hard Disk Space:
 220 MB for PASS (plus space for Microsoft .NET 4.6 if not already installed)
 Printer:
 Any Windowscompatible inkjet or laser printer
Pass unter MAC OS X
Für eine Nutzung der Software auf einem MACBetriebssystem benötigen Sie einen Windows Emulator, wie z.B. Parallels!
Minimum System Requirements for NCSS
These are the computer requirements in order to run NCSS 12 Statistical Analysis Software:
 Processor:
 450 MHz or faster processor
 32bit (x86) or 64bit (x64) processor
 RAM:
 256 MB (512 MB recommended)
 Operating Systems:
 Windows XP with Service Pack 2 or higher
 Windows Vista
 Windows 7
 Windows 8 and 8.1
 Windows 10 or later
 Windows Server 2003
 Windows Server 2008
 Windows Server 2008 R2
 Windows Server 2012
 Windows Server 2012 R2 or later
 Privileges:
 Administrative rights required during installation only
 Third Party Software:
 Microsoft .NET 3.5 SP1 (included with NCSS CD, comes preinstalled with Windows 7 and Windows Server 2008 R2, feature activation required on Windows 8, 8.1, 10 and Windows Server 2012 and 2012 R2)
 Microsoft Windows Installer 3.1 or higher
 Adobe Reader® 7 or higher (required for the Help System only)
 Hard Disk Space:
 160 MB for NCSS (plus space for Microsoft .NET 3.5 SP1 if not already installed)
 Printer:
 Any Windowscompatible inkjet or laser printer
NCSS unter MAC OS X
Für den Betrieb von NCSS unter Mac OS X ist ein Windows Emulator, wie z.B. Parallels notwendig!
What's New in PASS 2020?
PASS adds 38 new sample size procedures and 33 updated or improved procedures. Among the new and updated procedures are those for

 GroupSequential Tests for Hazard Rates, Means, and Proportions (Superiority and NonInferiority)
 GEE Tests for Means, Proportions, and Poisson Rates in a ClusterRandomized Design
 PostMarketing Surveillance for Poisson Rates
 Tests for Means and Proportions a SplitMouth Design
 Confidence Intervals in Cluster and Stratified Designs
 Tests for Means and Proportions in ClusterRandomized Designs
 Tests for Multiple Proportions and Poisson Rates
 Tests for One Exponential Hazard Rate
 Equivalence Tests for the Ratio of Two Means (Normal Data)
 Updated Randomization Lists (Block Randomization and Stratified Lists)
 Updated Conditional Power and Sample Size Reestimation of Means, Proportions, 2×2 CrossOver Designs, and Logrank Tests
 Updated OneWay ANOVA
 Updated Simplified Simulation Procedures for One Mean, Paired Means, Two Means, and MannWhitney Tests
 Updated McNemar Test
 Updated CochranMantelHaenszel Test
For the 11 new groupsequential sample size procedures in PASS , there are corresponding groupsequential analysis and samplesize reestimation procedures in NCSS
What's New in NCSS 2020?
 GroupSequential Analysis for Hazard Rates, Means, and Proportions (Superiority and NonInferiority)
 Simple and Stratified Dataset Random Sampling Tools
 AtRisk Tables Added to Applicable Plots in All Survival/Reliability Procedures
 Heatmaps Added to Factor Analysis and PCA Procedures
 TukeyPairwise and Dunnett’s ManytoOne (Control) Multiple Comparisons Tests for Proportions Added to Contingency Tables (Crosstabs / ChiSquare Test)
 Block Randomization and Stratified Lists Added to the Randomization Lists Procedure
 Data Window Improvements (Now Requires up to 70% Less Memory to Load the Same Amount of Data)
 Improved Algorithms for the Standardized Range Probability Distribution
More Procedures NCSS
Addition of new procedures and tests:
 Paired TTest for Superiority by a Margin
 OneSample TTest for NonInferiority
 OneSample TTest for Superiority by a Margin
 OneSample TTest for Equivalence
 TwoSample TTest for Superiority by a Margin
 –
 Analysis of 2×2 CrossOver Designs using TTests for NonInferiority
 Analysis of 2×2 CrossOver Designs using TTests for Superiority by a Margin
 Analysis of 2×2 CrossOver Designs using TTests for Equivalence
 –
 One Proportion – NonInferiority Tests
 One Proportion – Superiority by a Margin Tests
 One Proportion – Equivalence Tests
 –
 TwoSample NonInferiority Tests for Survival Data using Cox Regression
 TwoSample Superiority by a Margin Tests for Survival Data using Cox Regression
 TwoSample Equivalence Tests for Survival Data using Cox Regression
 –
 Cluster Randomization – Create Cluster Means Dataset
 Cluster Randomization – Create Cluster Proportions Dataset
 Cluster Randomization – Create Cluster Rates Dataset
 –
 General Linear Models (GLM) for Fixed Factors
 –
 OneWay Analysis of Covariance (ANCOVA)
 Analysis of Covariance (ANCOVA) with Two Groups
 –
 Clustered Heat Maps (Double Dendrograms)
Examples of new plots:
 Clustered Heat Map (Double Dendrogram)
What’s New in PASS ?
We are pleased to announce the release of PASS. PASS adds 55 new sample size procedures, including new procedures for the odds ratio in logistic regression, generalized estimating equation (GEE) tests, repeated measures design tests, crossover design proportions tests, tests for two Poisson rates in crossover designs, ordinal data tests in crossover designs, pairwise proportion differences in a Williams crossover design, tests for comparing two or more timeaveraged differences, multiple group slope tests, mixed models tests for two means/proportions/slopes in hierarchical designs, tests for multiple correlated proportions, and more.
Installation Qualification (IQ) and Operational Qualification (OQ) tools were added in PASS.
Report section options give the user flexibility to enhance output readability.
New Procedures in PASS
Logistic Regression
 Tests for the Odds Ratio in Logistic Regression with One Normal X (Wald Test)
 Tests for the Odds Ratio in Logistic Regression with One Normal X and Other Xs (Wald Test)
 Tests for the Odds Ratio in Logistic Regression with One Binary X and Other Xs (Wald Test)
Repeated Measures Slopes (GEE)
 GEE Tests for the Slope of Two Groups in a Repeated Measures Design (Continuous Outcome)
 GEE Tests for the Slope of Two Groups in a Repeated Measures Design (Binary Outcome)
 GEE Tests for the Slope of Two Groups in a Repeated Measures Design (Count Outcome)
 –
 GEE Tests for the Slope of Multiple Groups in a Repeated Measures Design (Continuous Outcome)
 GEE Tests for the Slope of Multiple Groups in a Repeated Measures Design (Count Outcome)
Repeated Measures TimeAveraged Differences (GEE)
 GEE Tests for the TAD of Two Groups in a Repeated Measures Design (Continuous Outcome)
 GEE Tests for the TAD of Two Groups in a Repeated Measures Design (Binary Outcome)
 GEE Tests for the TAD of Two Groups in a Repeated Measures Design (Count Outcome)
 –
 GEE Tests for the TAD of Multiple Groups in a Repeated Measures Design (Continuous Outcome)
 GEE Tests for the TAD of Multiple Groups in a Repeated Measures Design (Binary Outcome)
 GEE Tests for the TAD of Multiple Groups in a Repeated Measures Design (Count Outcome)
Hierarchical Design Comparisons using Mixed Models
 Mixed Models Tests for Two Means in a 2Level Hierarchical Design (Level2 Randomization)
 Mixed Models Tests for Two Means in a 2Level Hierarchical Design (Level1 Randomization)
 –
 Mixed Models Tests for Two Proportions in a 2Level Hierarchical Design (Level2 Randomization)
 Mixed Models Tests for Two Proportions in a 2Level Hierarchical Design (Level1 Randomization)
 –
 Mixed Models Tests for the Slope Difference in a 2Level Hierarchical Design with Fixed Slopes
 Mixed Models Tests for the Slope Difference in a 2Level Hierarchical Design with Random Slopes
 –
 Mixed Models Tests for Two Means in a 3Level Hierarchical Design (Level3 Randomization)
 Mixed Models Tests for Two Means in a 3Level Hierarchical Design (Level2 Randomization)
 Mixed Models Tests for Two Means in a 3Level Hierarchical Design (Level1 Randomization)
 –
 Mixed Models Tests for Two Proportions in a 3Level Hierarchical Design (Level3 Randomization)
 Mixed Models Tests for Two Proportions in a 3Level Hierarchical Design (Level2 Randomization)
 Mixed Models Tests for Two Proportions in a 3Level Hierarchical Design (Level1 Randomization)
 –
 Mixed Models Tests for the Slope Diff. in a 3Level Hier. Design with Fixed Slopes (Level2 Rand.)
 Mixed Models Tests for the Slope Diff. in a 3Level Hier. Design with Random Slopes (Level2 Rand.)
 Mixed Models Tests for the Slope Diff. in a 3Level Hier. Design with Fixed Slopes (Level3 Rand.)
 Mixed Models Tests for the Slope Diff. in a 3Level Hier. Design with Random Slopes (Level3 Rand.)
2×2 CrossOver Design – Odds Ratio
 Tests for the Odds Ratio of Two Proportions in a 2×2 CrossOver Design
 NonInferiority Tests for the Odds Ratio of Two Proportions in a 2×2 CrossOver Design
 Superiority by a Margin Tests for the Odds Ratio of Two Proportions in a 2×2 CrossOver Design
 Equivalence Tests for the Odds Ratio of Two Proportions in a 2×2 CrossOver Design
2×2 CrossOver Design – Proportion Difference
 Tests for the Difference of Two Proportions in a 2×2 CrossOver Design
 NonInferiority Tests for the Difference of Two Proportions in a 2×2 CrossOver Design
 Superiority by a Margin Tests for the Difference of Two Proportions in a 2×2 CrossOver Design
 Equivalence Tests for the Difference of Two Proportions in a 2×2 CrossOver Design
2×2 CrossOver Design – Ratio of Poisson Rates
 Tests for the Ratio of Two Poisson Rates in a 2×2 CrossOver Design
 NonInferiority Tests for the Ratio of Two Poisson Rates in a 2×2 CrossOver Design
 Superiority by a Margin Tests for the Ratio of Two Poisson Rates in a 2×2 CrossOver Design
 Equivalence Tests for the Ratio of Two Poisson Rates in a 2×2 CrossOver Design
2×2 CrossOver Design – Generalized Odds Ratio for Ordinal Data
 Tests for the Generalized Odds Ratio for Ordinal Data in a 2×2 CrossOver Design
 NonInferiority Tests for the Generalized Odds Ratio for Ordinal Data in a 2×2 CrossOver Design
 Superiority by a Margin Tests for the Gen. Odds Ratio for Ordinal Data in a 2×2 CrossOver Design
 Equivalence Tests for the Generalized Odds Ratio for Ordinal Data in a 2×2 CrossOver Design
Williams CrossOver Design – Pairwise Proportion Differences
 Tests for Pairwise Proportion Differences in a Williams CrossOver Design
 NonInferiority Tests for Pairwise Proportion Differences in a Williams CrossOver Design
 Superiority by a Margin Tests for Pairwise Proportion Differences in a Williams CrossOver Design
 Equivalence Tests for Pairwise Proportion Differences in a Williams CrossOver Design
Williams CrossOver Design – Pairwise Mean Differences
 Tests for Pairwise Mean Differences in a Williams CrossOver Design
 NonInferiority Tests for Pairwise Mean Differences in a Williams CrossOver Design
 Superiority by a Margin Tests for Pairwise Mean Differences in a Williams CrossOver Design
 Equivalence Tests for Pairwise Mean Differences in a Williams CrossOver Design
Multiple Correlated Proportions (McNemarBowker Test of Symmetry)
 Tests for Multiple Correlated Proportions (McNemarBowker Test of Symmetry)
Features of the software PASS
PASS 14 adds over 25 new power and sample size procedures. Over 45 procedures were updated and/or improved.
New Procedures
Means
 Equivalence Tests for the Difference Between Two Paired Means
 NonInferiority Tests for Two Means in a ClusterRandomized Design
 Equivalence Tests for Two Means in a ClusterRandomized Design
 Superiority by a Margin Tests for Two Means in a ClusterRandomized Design
 Tests for the Difference of Two Means in a HigherOrder CrossOver Design
 Tests for the Ratio of Two Means in a HigherOrder CrossOver Design
 Tests for Fold Change of Two Means
 MxM CrossOver Designs
 MPeriod CrossOver Designs using Contrasts
 OneWay Repeated Measures
 OneWay Repeated Measures Contrasts
 OneWay Analysis of Variance Contrasts
 Confidence Intervals for OneWay Repeated Measures Contrasts
Rates and Counts
 Tests for the Difference Between Two Poisson Rates
 Tests for the Difference Between Two Poisson Rates in a ClusterRandomized Design
 Tests for the Ratio of Two Negative Binomial Rates
Survival
 Logrank Tests in a ClusterRandomized Design
 OneSample Logrank Tests
 OneSample Cure Model Tests
Regression
 Reference Intervals for Clinical and Lab Medicine
 Tests for the Difference Between Two Linear Regression Slopes
 Tests for the Difference Between Two Linear Regression Intercepts
 Mendelian Randomization with a Binary Outcome
 Mendelian Randomization with a Continuous Outcome
Acceptance Sampling
 Acceptance Sampling for Attributes
 Operating Characteristic Curves for Acceptance Sampling for Attributes
Verbesserte oder Veränderte Prozeduren in PASS 14
Means
 Tests for Two Means using Ratios
 Tests for Two Means in a ClusterRandomized Design
 NonInferiority Tests for the Difference of Two Means in a HigherOrder CrossOver Design
 NonInferiority Tests for the Ratio of Two Means in a HigherOrder CrossOver Design
 Equivalence Tests for the Difference of Two Means in a HigherOrder CrossOver Design
 Equivalence Tests for the Ratio of Two Means in a HigherOrder CrossOver Design
 Superiority by a Margin Tests for the Difference of Two Means in a HigherOrder CrossOver Design
 Superiority by a Margin Tests for the Ratio of Two Means in a HigherOrder CrossOver Design
 OneWay Analysis of Variance FTests
Rates and Counts
 Tests for One Poisson Rate
 Tests for the Ratio of Two Poisson Rates
Proportions
 Tests for One Proportion
 NonInferiority Tests for One Proportion
 Equivalence Tests for One Proportion
 Superiority by a Margin Tests for One Proportion
 Tests for Two Proportions
 Tests for Two Proportions in a Repeated Measures Design
 NonInferiority Tests for the Difference Between Two Proportions
 NonInferiority Tests for the Ratio of Two Proportions
 NonInferiority Tests for the Odds Ratio of Two Proportions
 Equivalence Tests for the Difference Between Two Proportions
 Equivalence Tests for the Ratio of Two Proportions
 Equivalence Tests for the Odds Ratio of Two Proportions
 Superiority by a Margin Tests for the Difference Between Two Proportions
 Superiority by a Margin Tests for the Ratio of Two Proportions
 Superiority by a Margin Tests for the Odds Ratio of Two Proportions
 Confidence Intervals for the Difference Between Two Proportions
 Confidence Intervals for the Ratio of Two Proportions
 Confidence Intervals for the Odds Ratio of Two Proportions
 Tests for Two Correlated Proportions (McNemar Test)
 NonInferiority Tests for the Difference Between Two Correlated Proportions
 NonInferiority Tests for the Ratio of Two Correlated Proportions
 Equivalence Tests for the Difference Between Two Correlated Proportions
 Equivalence Tests for the Ratio of Two Correlated Proportions
 Tests for Two Proportions in a ClusterRandomized Design
 NonInferiority Tests for the Difference of Two Proportions in a ClusterRandomized Design
 NonInferiority Tests for the Ratio of Two Proportions in a ClusterRandomized Design
 Equivalence Tests for the Difference of Two Proportions in a ClusterRandomized Design
 Equivalence Tests for the Ratio of Two Proportions in a ClusterRandomized Design
 Superiority by a Margin Tests for the Difference of Two Proportions in a ClusterRandomized Design
 Superiority by a Margin Tests for the Ratio of Two Proportions in a ClusterRandomized Design
 GroupSequential Tests for Two Proportions (Simulation)
 GroupSequential NonInferiority Tests for the Difference of Two Proportions (Simulation)
 GroupSequential NonInferiority Tests for the Ratio of Two Proportions (Simulation)
 GroupSequential NonInferiority Tests for the Odds Ratio of Two Proportions (Simulation)
 GroupSequential Superiority by a Margin Tests for the Difference of Two Proportions (Simulation)
 GroupSequential Superiority by a Margin Tests for the Ratio of Two Proportions (Simulation)
 GroupSequential Superiority by a Margin Tests for the Odds Ratio of Two Proportions (Simulation)
Features of the software NCSS
Analysis of Variance
 OneWay Analysis of Variance
 BoxCox Transformation for Two or More Groups (TTest and OneWay ANOVA)
 Balanced Design Analysis of Variance
 General Linear Models (GLM)
 Repeated Measures Analysis of Variance
 Multivariate Analysis of Variance (MANOVA)
 Analysis of TwoLevel Designs
 NondetectsData Group Comparison
 Area Under Curve
Clustering
 Fuzzy Clustering
 Hierarchical Clustering / Dendrograms
 KMeans Clustering
 Medoid Partitioning
 Regression Clustering
Correlation
 Linear Regression and Correlation
 BoxCox Transformation for Simple Linear Regression
 PointBiserial and Biserial Correlations
 Correlation Matrix
 Canonical Correlation
 Lin's Concordance Correlation Coefficient
 BlandAltman Plot and Analysis
Curve Fitting
 Curve Fitting  General
 MichaelisMenten Equation
 Ratio of Polynomials Fit  One Variable
 Ratio of Polynomials Search  One Variable
 Reference Intervals with a Covariate
 Sum of Functions Models
 Nonlinear Regression
 Ratio of Polynomials Fit  Many Variables
 Ratio of Polynomials Search  Many Variables
 Function Plots
 Scatter Plot Matrix for Curve Fitting
Descriptive Statistics
 Descriptive Statistics
 Descriptive Statistics  Summary Tables
 Contingency Tables (Crosstabs / ChiSquare Test)
 Frequency Tables
 BoxCox Transformation
 Data Screening
 Data Simulation
 Grubbs' Outlier Test
 Normality Tests
 StemandLeaf Plots
 BacktoBack StemandLeaf Plots
 Item Analysis
 Item Response Analysis
 Area Under Curve
 Circular Data Analysis
 Tolerance Intervals
Design of Experiments
 Randomization Lists
 Balanced Incomplete Block Designs
 Fractional Factorial Designs
 Latin Square Designs
 Response Surface Designs
 Screening Designs
 Taguchi Designs
 TwoLevel Designs
 Design Generator
 DOptimal Designs
 Analysis of TwoLevel Designs
 Response Surface Regression
Forecasting / Time Series
 ARIMA (BoxJenkins)
 Automatic ARMA
 Theoretical ARMA
 Autocorrelations
 CrossCorrelations
 Spectral Analysis
 Decomposition Forecasting
 Exponential Smoothing  Horizontal
 Exponential Smoothing  Trend
 Exponential Smoothing  Trend / Seasonal
 Harmonic Regression
 Analysis of Runs
 Time Series Plots
Mass Appraisal
 Appraisal Ratios
 Comparables  Sales Price
 Hybrid Appraisal Models
 Descriptive Statistics
 Descriptive Statistics  Summary Tables
 Multiple Regression
 Nonlinear Regression
MetaAnalysis
 MetaAnalysis of Correlated Proportions
 MetaAnalysis of Hazard Ratios
 MetaAnalysis of Means
 MetaAnalysis of Proportions
 Forest Plots
Mixed Models
 Mixed Models  General
 Mixed Models  No Repeated Measures
 Mixed Models  Repeated Measures
 Mixed Models  Random Coefficients
Multivariate
 Canonical Correlation
 Equality of Covariance
 Factor Analysis
 Principal Components Analysis
 Discriminant Analysis
 Hotelling's OneSample T2
 Hotelling's TwoSample T2
 Multivariate Analysis of Variance (MANOVA)
 Correspondence Analysis
 Multidimensional Scaling
Nonparametric
 Analysis of Runs
 Bootstrap Confidence Intervals (OneSample TTest)
 Bootstrap Confidence Intervals (Paired TTest)
 Bootstrap Confidence Intervals (TwoSample TTest)
 Cochran's Q Test
 Cumulative Incidence
 Friedman's Rank Test (Balanced Design ANOVA)
 KaplanMeier Curves (Logrank Tests)
 KolmogorovSmirnov Test (TwoSample TTest)
 KruskalWallis Test (OneWay ANOVA)
 MannWhitney U Test (TwoSample TTest)
 NondetectsData Group Comparison
 Randomization Test (OneSample TTest)
 Randomization Test (Paired TTest)
 Randomization Test (TwoSample TTest)
 ROC Curves
 Spearman Rank Correlation (Correlation Matrix, Linear Regression and Correlation)
 Wilcoxon SignedRank Test (OneSample TTest)
 Wilcoxon SignedRank Test (Paired TTest)
Operations Research
 Linear Programming
Proportions
 One Proportion
 Two Proportions
 Two Proportions  NonInferiority Tests
 Two Proportions  Superiority Tests
 Two Proportions  Equivalence Tests
 Two Proportions  TwoSided Tests vs. a Margin
 Two Correlated Proportions (McNemar Test)
 Two Correlated Proportions  NonInferiority Tests
 Two Correlated Proportions  Superiority Tests
 Two Correlated Proportions  Equivalence Tests
 Contingency Tables (Crosstabs / ChiSquare Test)
 Frequency Tables
 Cochran's Q Test
 Loglinear Models
 MantelHaenszel Test
 ROC Curves
 Item Analysis
 Item Response Analysis
 Binary Diagnostic Tests  Single Sample
 Binary Diagnostic Tests  Two Independent Samples
 Binary Diagnostic Tests  Paired Samples
 Binary Diagnostic Tests  Clustered Samples
Quality Control
 Xbar and R Charts
 Xbar and s Charts
 Xbar Charts
 R Charts
 s Charts
 CUSUM Charts
 Moving Average Charts
 EWMA Charts
 Individuals and Moving Range Charts
 LeveyJennings Charts
 P Charts
 NP Charts
 C Charts
 U Charts
 Capability Analysis
 R & R Study
 Tolerance Intervals
 Lag Plots
 Analysis of Runs
 Pareto Charts
Regression
 Linear Regression and Correlation
 BoxCox Transformation for Simple Linear Regression
 Deming Regression
 Harmonic Regression
 Mixed Models  Random Coefficients
 PointBiserial and Biserial Correlations
 Multiple Regression
 Multiple Regression with Serial Correlation
 Nondetects Data Regression
 Principal Components Regression
 Response Surface Regression
 Ridge Regression
 Robust Regression
 Cox Regression
 Parametric Survival (Weibull) Regression
 Logistic Regression
 Discriminant Analysis
 Poisson Regression
 Probit Analysis
 Nonlinear Regression
Regression (Variable Selection)
 All Possible Regressions
 Stepwise Regression
 Subset Selection in Multiple Regression
 Subset Selection in Multivariate Y Multiple Regression
 Cox Regression
 Discriminant Analysis
 Logistic Regression
 Poisson Regression
Survival / Reliability
 Cumulative Incidence
 KaplanMeier Curves (Logrank Tests)
 LifeTable Analysis
 Cox Regression
 Parametric Survival (Weibull) Regression
 Beta Distribution Fitting
 Distribution (Weibull) Fitting
 Gamma Distribution Fitting
 MantelHaenszel Test
 Probit Analysis
 Time Calculator
 Tolerance Intervals
 Survival Parameter Conversion Tool
 Survival Plots
TTests
 OneSample TTest
 Paired TTest
 Paired TTest for NonInferiority
 Paired TTest for Equivalence
 TwoSample TTest
 TwoSample TTest from Means and SD's
 BoxCox Transformation for Two or More Groups (TTest and OneWay ANOVA)
 Testing NonInferiority with Two Independent Samples
 Testing Equivalence with Two Independent Samples
 BlandAltman Plot and Analysis
 Hotelling's OneSample T2
 Hotelling's TwoSample T2
 Analysis of TwoLevel Designs
 CrossOver Analysis Using TTests
Graphics Procedures
Bar Charts
 Bar Charts
 Bar Charts (2 Factors)
 3D Bar Charts
 3D Bar Charts (2 Factors)
 Pareto Charts
BlandAltman Plot
 BlandAltman Plot and Analysis
Box Plots
 Box Plots
 Box Plots (2 Factors)
Circular Data Plots
 Circular Data Analysis (Rose Plots)
 Pie Charts
Combo Charts
 Combo Charts
 Combo Charts (2 Factors)
Contour Plots
 Contour Plots
Curve Fitting
 Curve Fitting  General
 MichaelisMenten Equation
 Function Plots
 Scatter Plot Matrix for Curve Fitting
Dendrograms
 Hierarchical Clustering (Dendrograms)
Density Plots
 Density Plots
 Density Plots (2 Factors)
Dot Plots
 Dot Plots
 Dot Plots (2 Factors)
ErrorBar Charts
 ErrorBar Charts
 ErrorBar Charts (2 Factors)
Forecasting / Time Series
 ARIMA (BoxJenkins)
 Automatic ARMA
 Theoretical ARMA
 Autocorrelations
 CrossCorrelations
 Spectral Analysis
 Decomposition Forecasting
 Exponential Smoothing  Horizontal
 Exponential Smoothing  Trend
 Exponential Smoothing  Trend / Seasonal
 Lag Plots
 Analysis of Runs
Forest Plots
 MetaAnalysis of Correlated Proportions
 MetaAnalysis of Hazard Ratios
 MetaAnalysis of Means
 MetaAnalysis of Proportions
Function Plots
 Function Plots
Histograms
 Histograms
 Comparative Histograms
 Comparative Histograms (2 Factors)
 Rose Plots
KaplanMeier Curves (Survival)
 KaplanMeier Curves
Line Charts
 Line Charts
 Line Charts (2 Factors)
 3D Line Charts
 3D Line Charts (2 Factors)
Mosaic Plots
 Mosaic Plots
Percentile Plots
 Percentile Plots
 Percentile Plots (2 Factors)
Pie Charts
 Pie Charts
Probability Plots
 Normal Probability Plots
 Weibull Probability Plots
 LogNormal Probability Plots
 Gamma Probability Plots
 Exponential Probability Plots
 ChiSquare Probability Plots
 Uniform Probability Plots
 HalfNormal Probability Plots
 Probability Plot Comparison
Quality Control Charts
 Xbar and R Charts
 Xbar and s Charts
 Xbar Charts
 R Charts
 s Charts
 CUSUM Charts
 Moving Average Charts
 EWMA Charts
 Individuals and Moving Range Charts
 LeveyJennings Charts
 P Charts
 NP Charts
 C Charts
 U Charts
 Capability Analysis
 Lag Plots
 Analysis of Runs
 Pareto Charts
ROC Curves
 ROC Curves
Scatter Plots
 Scatter Plots
 3D Scatter Plots
 Scatter Plot Matrix
 Scatter Plot Matrix for Curve Fitting
 Lag Plots
StemandLeaf Plots
 StemandLeaf Plots
 BacktoBack StemandLeaf Plots
Surface and Contour Plots
 Contour Plots
 3D Surface Plots
3D
 3D Scatter Plots
 3D Surface Plots
 3D Bar Charts
 3D Bar Charts (2 Factors)
 3D Line Charts
 3D Line Charts (2 Factors)
Operations
 Data Window
 Importing Data
 Exporting Data
 Filters
 Transformations
 Stacking Data
 Unstacking Data
 Creating Contrast Variables
Procedures
 BoxCox Transformation
 BoxCox Transformation for Two or More Groups (TTest and OneWay ANOVA)
 BoxCox Transformation for Simple Linear Regression
 Data List
 Data Screening
 Data Simulation
 Merging Two Datasets
 Data Matching  Greedy
 Data Matching  Optimal
 Data Stratification
 Time Calculator
Calculators
 Probability Calculator
 ChiSquare Effect Size Calculator
 Odds Ratio and Proportions Calculator
 Standard Deviation Calculator
 Survival Parameter Conversion Tool
New Features in NCSS
New Procedures and Tests
 Conditional Logistic Regression
 Multiple Regression – Basic
 Negative Binomial Regression
 ZeroInflated Negative Binomial Regression
 ZeroInflated Poisson Regression
 Geometric Regression
 Fractional Polynomial Regression
 PassingBablok Regression for Method Comparison
 Robust Linear Regression (PassingBablok MedianSlope)
 Logistic Regression (including confidence intervals for AUC)
 TwoStage Least Squares
 Scatter Plots with Error Bars
 Scatter Plots with Error Bars from Summary Data
 ErrorBar Charts from Summary Data
 ErrorBar Charts from Summary Data (2 Factors)
 Descriptive Statistics – Summary Tables
 Descriptive Statistics – Summary Lists
 One ROC Curve and Cutoff Analysis
 Comparing Two ROC Curves – Independent Groups Design
 Comparing Two ROC Curves – Paired Design
 Single Sample Binary Diagnostic Test Analysis
 Correlation
 Circular Data Correlation
 Reference Intervals
 Appraisal Ratio Studies
 Comparables Appraisal
 Hybrid Appraisal Models
 Multiple Regression for Appraisal
 Acceptance Sampling for Attributes
 Operating Characteristic Curves for Acceptance Sampling for Attributes
 Linear Programming with Bounds
 Mixed Integer Programming
 Quadratic Programming
 Transportation
 Assignment
 Minimum Spanning Tree
 Shortest Route
 Maximum Flow
 Minimum Cost Capacitated Flow
 Transshipment
 DwassSteelCritchlowFligner MC Test (in the OneWay Analysis of Variance Procedure)
Enhancements
 Data Simulation Procedure
The Data Simulation procedure was enhanced to include a much larger selection of distributions.  OneWay ANOVA Residuals
The OneWay Analysis of Variance procedure now gives the ability to store residuals.  Contingency Tables Table Entry
The table entry in the Contingency Tables (Crosstabs / ChiSquare Test) procedure was improved.  One Proportion Data Entry
A Database Data Entry option is now available in the One Proportion procedure.  Export Tool
The Export tool now has the ability to select variables for export.  Output Titles
Software version titles are given in the output.  ErrorBar Plots
The errorbar plots procedures now give the options for Confidence Interval and Range.  Spreadsheet Controls
The Spreadsheet controls have been updated.  3D Charting
The 3D charting control has been updated for additional processing and display speed.  Column Selection Tools
The column selection tools have been dramatically enhanced.  AutoScaling of Ticks
The AutoScaling of the ticks of the numeric axis of plots has been improved.  Filters and Missing Values in Time Series and Forecasting
Filter and missing values options are now available in all Forecasting and Time Series procedures.  Procedure Menus
The procedure menus were enhanced to be more intuitive.