### Glossary Index

###### 2

- 2D Bar/Column Plots
- 2D Box Plots
- 2D Box Plots - Box Whiskers
- 2D Box Plots - Boxes
- 2D Box Plots - Columns
- 2D Box Plots - Error Bars
- 2D Box Plots - Whiskers
- 2D Categorized Detrended Probability Plots
- 2D Categorized Half-Norm. Probability Plots
- 2D Categorized Normal Probability Plots
- 2D Detrended Probability Plots
- 2D Histograms
- 2D Histograms - Hanging Bars
- 2D Histograms - Double-Y
- 2D Line Plots
- 2D Line Plots - Aggregated
- 2D Line Plots - Double-Y
- 2D Line Plots - Multiple
- 2D Line Plots - Regular
- 2D Line Plots - XY Trace
- 2D Range Plots - Error Bars
- 2D Matrix Plots
- 2D Matrix Plots - Columns
- 2D Matrix Plots - Lines
- 2D Matrix Plots - Scatterplot
- 2D Normal Probability Plots
- 2D Probability-Probability Plots
- 2D Probability-Probability Plots-Categorized
- 2D Quantile-Quantile Plots
- 2D Quantile-Quantile Plots - Categorized
- 2D Scatterplot
- 2D Scatterplot - Categorized Ternary Graph
- 2D Scatterplot - Double-Y
- 2D Scatterplot - Frequency
- 2D Scatterplot - Multiple
- 2D Scatterplot - Regular
- 2D Scatterplot - Voronoi
- 2D Sequential/Stacked Plots
- 2D Sequential/Stacked Plots - Area
- 2D Sequential/Stacked Plots - Column
- 2D Sequential/Stacked Plots - Lines
- 2D Sequential/Stacked Plots - Mixed Line
- 2D Sequential/Stacked Plots - Mixed Step
- 2D Sequential/Stacked Plots - Step
- 2D Sequential/Stacked Plots - Step Area
- 2D Ternary Plots - Scatterplot

###### 3

- 3D Bivariate Histogram
- 3D Box Plots
- 3D Box Plots - Border-style Ranges
- 3D Box Plots - Double Ribbon Ranges
- 3D Box Plots - Error Bars
- 3D Box Plots - Flying Blocks
- 3D Box Plots - Flying Boxes
- 3D Box Plots - Points
- 3D Categorized Plots - Contour Plot
- 3D Categorized Plots - Deviation Plot
- 3D Categorized Plots - Scatterplot
- 3D Categorized Plots - Space Plot
- 3D Categorized Plots - Spectral Plot
- 3D Categorized Plots - Surface Plot
- 3D Deviation Plots
- 3D Range Plot - Error Bars
- 3D Raw Data Plots - Contour/Discrete
- 3D Scatterplots
- 3D Scatterplots - Ternary Graph
- 3D Space Plots
- 3D Ternary Plots
- 3D Ternary Plots - Categorized Scatterplot
- 3D Ternary Plots - Categorized Space
- 3D Ternary Plots - Categorized Surface
- 3D Ternary Plots - Categorized Trace
- 3D Ternary Plots - Contour/Areas
- 3D Ternary Plots - Contour/Lines
- 3D Ternary Plots - Deviation
- 3D Ternary Plots - Space
- 3D Trace Plots

###### A

- Aberration, Minimum
- Abrupt Permanent Impact
- Abrupt Temporary Impact
- Accept-Support Testing
- Accept Threshold
- Activation Function (in Neural Networks)
- Additive Models
- Additive Season, Damped Trend
- Additive Season, Exponential Trend
- Additive Season, Linear Trend
- Additive Season, No Trend
- Adjusted means
- Aggregation
- AID
- Akaike Information Criterion (AIC)
- Algorithm
- Alpha
- Anderson-Darling Test
- ANOVA
- Append a Network
- Append Cases and/or Variables
- Application Programming Interface (API)
- Arrow
- Assignable Causes and Actions
- Association Rules
- Asymmetrical Distribution
- AT&T Runs Rules
- Attribute (attribute variable)
- Augmented Product Moment Matrix
- Autoassociative Network
- Automatic Network Designer

###### B

- B Coefficients
- Back Propagation
- Bagging (Voting, Averaging)
- Balanced ANOVA Design
- Banner Tables
- Bar/Column Plots, 2D
- Bar Dev Plot
- Bar Left Y Plot
- Bar Right Y Plot
- Bar Top Plot
- Bar X Plot
- Bartlett Window
- Basis Functions
- Batch algorithms in
*STATISTICA Neural Net* - Bayesian Information Criterion (BIC)
- Bayesian Networks
- Bayesian Statistics
- Bernoulli Distribution
- Best Network Retention
- Best Subset Regression
- Beta Coefficients
- Beta Distribution
- Bimodal Distribution
- Binomial Distribution
- Bivariate Normal Distribution
- Blocking
- Bonferroni Adjustment
- Bonferroni Test
- Boosting
- Boundary Case
- Box Plot/Medians (Block Stats Graphs)
- Box Plot/Means (Block Stats Graphs)
- Box Plots, 2D
- Box Plots, 2D - Box Whiskers
- Box Plots, 2D - Boxes
- Box Plots, 2D - Whiskers
- Box Plots, 3D
- Box Plots, 3D - Border-Style Ranges
- Box Plots, 3D - Double Ribbon Ranges
- Box Plots, 3D - Error Bars
- Box Plots, 3D - Flying Blocks
- Box Plots, 3D - Flying Boxes
- Box Plots, 3D - Points
- Box-Ljung Q Statistic
- Breakdowns
- Breaking Down (Categorizing)
- Brown-Forsythe Homogeneity of Variances
- Brushing
- Burt Table

###### C

- Canonical Correlation
- Cartesian Coordinates
- Casewise Missing Data Deletion
- Categorical Dependent Variable
- Categorical Predictor
- Categorized Graphs
- Categorized Plots, 2D-Detrended Prob. Plots
- Categorized Plots, 2D-Half-Normal Prob. Plots
- Categorized Plots, 2D - Normal Prob. Plots
- Categorized Plots, 2D - Prob.-Prob. Plots
- Categorized Plots, 2D - Quantile Plots
- Categorized Plots, 3D - Contour Plot
- Categorized Plots, 3D - Deviation Plot
- Categorized Plots, 3D - Scatterplot
- Categorized Plots, 3D - Space Plot
- Categorized Plots, 3D - Spectral Plot
- Categorized Plots, 3D - Surface Plot
- Categorized 3D Scatterplot (Ternary graph)
- Categorized Contour/Areas (Ternary graph)
- Categorized Contour/Lines (Ternary graph)
- Categorizing
- Cauchy Distribution
- Cause-and-Effect Diagram
- Censoring (Censored Observations)
- Censoring, Left
- Censoring, Multiple
- Censoring, Right
- Censoring, Single
- Censoring, Type I
- Censoring, Type II
- CHAID
- Characteristic Life
- Chernoff Faces (Icon Plots)
*Chi*-square Distribution- Circumplex
- City-Block (Manhattan) Distance
- Classification
- Classification (in Neural Networks)
- Classification and Regression Trees
- Classification by Labeled Exemplars (in NN)
- Classification Statistics (in Neural Networks)
- Classification Thresholds (in Neural Networks)
- Classification Trees
- Class Labeling (in Neural Networks)
- Cluster Analysis
- Cluster Diagram (in Neural Networks)
- Cluster Networks (in Neural Networks)
- Coarse Coding
- Codes
- Coding Variable
- Coefficient of Determination
- Coefficient of Variation
- Column Sequential/Stacked Plot
- Columns (Box Plot)
- Columns (Icon Plot)
- Common Causes
- Communality
- Complex Numbers
- Conditional Probability
- Conditioning (Categorizing)
- Confidence Interval
- Confidence Interval for the Mean
- Confidence Interval vs. Prediction Interval
- Confidence Limits
- Confidence Value (Association Rules)
- Confusion Matrix (in Neural Networks)
- Conjugate Gradient Descent (in Neural Net)
- Continuous Dependent Variable
- Contour/Discrete Raw Data Plot
- Contour Plot
- Control, Quality
- Cook's Distance
- Correlation
- Correlation, Intraclass
- Correlation (Pearson r)
- Correlation Value (Association Rules)
- Correspondence Analysis
- Cox-Snell Gen. Coefficient Determination
- Cpk, Cp, Cr
- CRISP
- Cross Entropy (in Neural Networks)
- Cross Verification (in Neural Networks)
- Cross-Validation
- Crossed Factors
- Crosstabulations
- C-SVM Classification
- Cubic Spline Smoother
- "Curse" of Dimensionality

###### D

- Daniell (or Equal Weight) Window
- Data Mining
- Data Preparation Phase
- Data Reduction
- Data Rotation (in 3D space)
- Data Warehousing
- Decision Trees
- Degrees of Freedom
- Deleted Residual
- Denominator Synthesis
- Dependent t-test
- Dependent vs. Independent Variables
- Deployment
- Derivative-Free Funct. Min. Algorithms
- Design, Experimental
- Design Matrix
- Desirability Profiles
- Detrended Probability Plots
- Deviance
- Deviance Residuals
- Deviation
- Deviation Assign. Algorithms (in Neural Net)
- Deviation Plot (Ternary Graph)
- Deviation Plots, 3D
- DFFITS
- DIEHARD Suite of Tests & Randm. Num. Gen.
- Differencing (in Time Series)
- Dimensionality Reduction
- Discrepancy Function
- Discriminant Function Analysis
- Distribution Function
- DOE
- Document Frequency
- Double-Y Histograms
- Double-Y Line Plots
- Double-Y Scatterplot
- Drill-Down Analysis
- Drilling-down (Categorizing)
- Duncan's test
- Dunnett's test
- DV

###### E

- Effective Hypothesis Decomposition
- Efficient Score Statistic
- Eigenvalues
- Ellipse, Prediction Area and Range
- EM Clustering
- Endogenous Variable
- Ensembles (in Neural Networks)
- Enterprise Resource Planning (ERP)
- Enterprise SPC
- Enterprise-Wide Software Systems
- Entropy
- Epoch in (Neural Networks)
- Eps
- EPSEM Samples
- ERP
- Error Bars (2D Box Plots)
- Error Bars (2D Range Plots)
- Error Bars (3D Box Plots)
- Error Bars (3D Range Plots)
- Error Function (in Neural Networks)
- Estimable Functions
- Euclidean Distance
- Euler's e
- Exogenous Variable
- Experimental Design
- Explained Variance
- Exploratory Data Analysis
- Exponential Distribution
- Exponential Family of Distributions
- Exponential Function
- Exponentially Weighted Moving Avg. Line
- Extrapolation
- Extreme Values (in Box Plots)
- Extreme Value Distribution

###### F

- F Distribution
- FACT
- Factor Analysis
- Fast Analysis Shared Multidimensional Info. FASMI
- Feature Extraction (vs. Feature Selection)
- Feature Selection
- Feedforward Networks
- Fisher LSD
- Fixed Effects (in ANOVA)
- Free Parameter
- Frequencies, Marginal
- Frequency Scatterplot
- Frequency Tables
- Function Minimization Algorithms

###### G

- g2 Inverse
- Gains Chart
- Gamma Coefficient
- Gamma Distribution
- Gaussian Distribution
- Gauss-Newton Method
- General ANOVA/MANOVA
- General Linear Model
- Generalization (in Neural Networks)
- Generalized Additive Models
- Generalized Inverse
- Generalized Linear Model
- Genetic Algorithm
- Genetic Algorithm Input Selection
- Geometric Distribution
- Geometric Mean
- Gibbs Sampler
- Gini Measure of Node Impurity
- Gompertz Distribution
- Goodness of Fit
- Gradient
- Gradient Descent
- Gradual Permanent Impact
- Group Charts
- Grouping (Categorizing)
- Grouping Variable
- Groupware

###### H

- Half-Normal Probability Plots
- Half-Normal Probability Plots - Categorized
- Hamming Window
- Hanging Bars Histogram
- Harmonic Mean
- Hazard
- Hazard Rate
- Heuristic
- Heywood Case
- Hidden Layers (in Neural Networks)
- High-Low Close
- Histograms, 2D
- Histograms, 2D - Double-Y
- Histograms, 2D - Hanging Bars
- Histograms, 2D - Multiple
- Histograms, 2D - Regular
- Histograms, 3D Bivariate
- Histograms, 3D - Box Plots
- Histograms, 3D - Contour/Discrete
- Histograms, 3D - Contour Plot
- Histograms, 3D - Spikes
- Histograms, 3D - Surface Plot
- Hollander-Proschan Test
- Hooke-Jeeves Pattern Moves
- Hosmer-Lemeshow Test
- HTM
- HTML
- Hyperbolic Tangent (tanh)
- Hyperplane
- Hypersphere

###### I

- Icon Plots
- Icon Plots - Chernoff Faces
- Icon Plots - Columns
- Icon Plots - Lines
- Icon Plots - Pies
- Icon Plots - Polygons
- Icon Plots - Profiles
- Icon Plots - Stars
- Icon Plots - Sun Rays
- Increment vs Non-Increment Learning Algr.
- Independent Events
- Independent t-test
- Independent vs. Dependent Variables
- Industrial Experimental Design
- Inertia
- Inlier
- In-Place Database Processing (IDP)
- Interactions
- Interpolation
- Interval Scale
- Intraclass Correlation Coefficient
- Invariance Const. Scale Factor ICSF
- Invariance Under Change of Scale (ICS)
- Inverse Document Frequency
- Ishikawa Chart
- Isotropic Deviation Assignment
- Item and Reliability Analysis
- IV

###### J

###### K

###### L

- Lack of Fit
- Lambda Prime
- Laplace Distribution
- Latent Semantic Indexing
- Latent Variable
- Layered Compression
- Learned Vector Quantization (in Neural Net)
- Learning Rate (in Neural Networks)
- Least Squares (2D graphs)
- Least Squares (3D graphs)
- Least Squares Estimator
- Least Squares Means
- Left and Right Censoring
- Levenberg-Marquardt Algorithm (in Neural Net)
- Levene's Test for Homogeneity of Variances
- Leverage values
- Life Table
- Life, Characteristic
- Lift Charts
- Likelihood
- Lilliefors test
- Line Plots, 2D
- Line Plots, 2D - Aggregated
- Line Plots, 2D (Case Profiles)
- Line Plots, 2D - Double-Y
- Line Plots, 2D - Multiple
- Line Plots, 2D - Regular
- Line Plots, 2D - XY Trace
- Linear (2D graphs)
- Linear (3D graphs)
- Linear Activation function
- Linear Modeling
- Linear Units
- Lines (Icon Plot)
- Lines (Matrix Plot)
- Lines Sequential/Stacked Plot
- Link Function
- Local Minima
- Locally Weighted (Robust) Regression
- Logarithmic Function
- Logistic Distribution
- Logistic Function
- Logit Regression and Transformation
- Log-Linear Analysis
- Log-Normal Distribution
- Lookahead (in Neural Networks)
- Loss Function
- LOWESS Smoothing

###### M

- Machine Learning
- Mahalanobis Distance
- Mallow's CP
- Manifest Variable
- Mann-Scheuer-Fertig Test
- MANOVA
- Marginal Frequencies
- Markov Chain Monte Carlo (MCMC)
- Mass
- Matching Moments Method
- Matrix Collinearity
- Matrix Ill-Conditioning
- Matrix Inverse
- Matrix Plots
- Matrix Plots - Columns
- Matrix Plots - Lines
- Matrix Plots - Scatterplot
- Matrix Rank
- Matrix Singularity
- Maximum Likelihood Loss Function
- Maximum Likelihood Method
- Maximum Unconfounding
- MD (Missing data)
- Mean
- Mean/S.D. Algorithm (in Neural Networks)
- Mean, Geometric
- Mean, Harmonic
- Mean Substitution of Missing Data
- Means, Adjusted
- Means, Unweighted
- Median
- Meta-Learning
- Method of Matching Moments
- Minimax
- Minimum Aberration
- Mining, Data
- Missing values
- Mixed Line Sequential/Stacked Plot
- Mixed Step Sequential/Stacked Plot
- Mode
- Model Profiles (in Neural Networks)
- Models for Data Mining
- Monte Carlo
- Multi-Pattern Bar
- Multicollinearity
- Multidimensional Scaling
- Multilayer Perceptrons
- Multimodal Distribution
- Multinomial Distribution
- Multinomial Logit and Probit Regression
- Multiple Axes in Graphs
- Multiple Censoring
- Multiple Dichotomies
- Multiple Histogram
- Multiple Line Plots
- Multiple Scatterplot
- Multiple R
- Multiple Regression
- Multiple Response Variables
- Multiple-Response Tables
- Multiple Stream Group Charts
- Multiplicative Season, Damped Trend
- Multiplicative Season, Exponential Trend
- Multiplicative Season, Linear Trend
- Multiplicative Season, No Trend
- Multivar. Adapt. Regres. Splines MARSplines
- Multi-way Tables

###### N

- Nagelkerke Gen. Coefficient Determination
- Naive Bayes
- Neat Scaling of Intervals
- Negative Correlation
- Negative Exponential (2D graphs)
- Negative Exponential (3D graphs)
- Neighborhood (in Neural Networks)
- Nested Factors
- Nested Sequence of Models
- Neural Networks
- Neuron
- Newman-Keuls Test
- N-in-One Encoding
- Noise Addition (in Neural Networks)
- Nominal Scale
- Nominal Variables
- Nonlinear Estimation
- Nonparametrics
- Non-Outlier Range
- Nonseasonal, Damped Trend
- Nonseasonal, Exponential Trend
- Nonseasonal, Linear Trend
- Nonseasonal, No Trend
- Normal Distribution
- Normal Distribution, Bivariate
- Normal Fit
- Normality Tests
- Normalization
- Normal Probability Plots
- Normal Probability Plots (Computation Note)
- n Point Moving Average Line

###### O

- ODBC
- Odds Ratio
- OLE DB
- On-Line Analytic Processing (OLAP)
- One-Off (in Neural Networks)
- One-of-N Encoding (in Neural Networks)
- One-Sample t-Test
- One-Sided Ranges Error Bars Range Plots
- One-Way Tables
- Operating Characteristic Curves
- Ordinal Multinomial Distribution
- Ordinal Scale
- Outer Arrays
- Outliers
- Outliers (in Box Plots)
- Overdispersion
- Overfitting
- Overlearning (in Neural Networks)
- Overparameterized Model

###### P

- Pairwise Del. Missing Data vs Mean Subst.
- Pairwise MD Deletion
- Parametric Curve
- Pareto Chart Analysis
- Pareto Distribution
- Part Correlation
- Partial Correlation
- Partial Least Squares Regression
- Partial Residuals
- Parzen Window
- Pearson Correlation
- Pearson Curves
- Pearson Residuals
- Penalty Functions
- Percentiles
- Perceptrons (in Neural Networks)
- Pie Chart
- Pie Chart - Counts
- Pie Chart - Multi-Pattern Bar
- Pie Chart - Values
- Pies (Icon Plots)
- PMML (Predictive Model Markup Language)
- PNG Files
- Poisson Distribution
- Polar Coordinates
- Polygons (Icon Plots)
- Polynomial
- Population Stability Report
- Portable Network Graphics Files
- Positive Correlation
- Post hoc Comparisons
- Post Synaptic Potential (PSP) Function
- Posterior Probability
- Power (Statistical)
- Power Goal
- Ppk, Pp, Pr
- Prediction Interval Ellipse
- Prediction Profiles
- Predictive Data Mining
- Predictive Mapping
- Predictive Model Markup Language (PMML)
- Predictors
- PRESS Statistic
- Principal Components Analysis
- Prior Probabilities
- Probability
- Probability Plots - Detrended
- Probability Plots - Normal
- Probability Plots - Half-Normal
- Probability-Probability Plots
- Probability-Probability Plots - Categorized
- Probability Sampling
- Probit Regression and Transformation
- PROCEED
- Process Analysis
- Process Capability Indices
- Process Performance Indices
- Profiles, Desirability
- Profiles, Prediction
- Profiles (Icon Plots)
- Pruning (in Classification Trees)
- Pseudo-Components
- Pseudo-Inverse Algorithm
- Pseudo-Inverse-Singular Val. Decomp. NN
- PSP (Post Synaptic Potential) Function
- Pure Error
- p-Value (Statistical Significance)

###### Q

###### R

- R Programming Language
- Radial Basis Functions
- Radial Sampling (in Neural Networks)
- Random Effects (in Mixed Model ANOVA)
- Random Forests
- Random Num. from Arbitrary Distributions
- Random Numbers (Uniform)
- Random Sub-Sampling in Data Mining
- Range Ellipse
- Range Plots - Boxes
- Range Plots - Columns
- Range Plots - Whiskers
- Rank
- Rank Correlation
- Ratio Scale
- Raw Data, 3D Scatterplot
- Raw Data Plots, 3D - Contour/Discrete
- Raw Data Plots, 3D - Spikes
- Raw Data Plots, 3D - Surface Plot
- Rayleigh Distribution
- Receiver Oper. Characteristic Curve
- Receiver Oper. Characteristic (in Neural Net)
- Rectangular Distribution
- Regression
- Regression (in Neural Networks)
- Regression, Multiple
- Regression Summary Statistics (in Neural Net)
- Regular Histogram
- Regular Line Plots
- Regular Scatterplot
- Regularization (in Neural Networks)
- Reject Inference
- Reject Threshold
- Relative Function Change Criterion
- Reliability
- Reliability and Item Analysis
- Representative Sample
- Resampling (in Neural Networks)
- Residual
- Resolution
- Response Surface
- Right Censoring
- RMS (Root Mean Squared) Error
- Robust Locally Weighted Regression
- ROC Curve
- ROC Curve (in Neural Networks)
- Root Cause Analysis
- Root Mean Square Stand. Effect RMSSE
- Rosenbrock Pattern Search
- Rotating Coordinates, Method of
- r (Pearson Correlation Coefficient)
- Runs Tests (in Quality Control)

###### S

- Sampling Fraction
- Scalable Software Systems
- Scaling
- Scatterplot, 2D
- Scatterplot, 2D-Categorized Ternary Graph
- Scatterplot, 2D - Double-Y
- Scatterplot, 2D - Frequency
- Scatterplot, 2D - Multiple
- Scatterplot, 2D - Regular
- Scatterplot, 2D - Voronoi
- Scatterplot, 3D
- Scatterplot, 3D - Raw Data
- Scatterplot, 3D - Ternary Graph
- Scatterplot Smoothers
- Scheffe's Test
- Score Statistic
- Scree Plot, Scree Test
- S.D. Ratio
- Semi-Partial Correlation
- SEMMA
- Sensitivity Analysis (in Neural Networks)
- Sequential Contour Plot, 3D
- Sequential/Stacked Plots, 2D
- Sequential/Stacked Plots, 2D - Area
- Sequential/Stacked Plots, 2D - Column
- Sequential/Stacked Plots, 2D - Lines
- Sequential/Stacked Plots, 2D - Mixed Line
- Sequential/Stacked Plots, 2D - Mixed Step
- Sequential/Stacked Plots, 2D - Step
- Sequential/Stacked Plots, 2D - Step Area
- Sequential Surface Plot, 3D
- Sets of Samples in Quality Control Charts
- Shapiro-Wilks' W test
- Shewhart Control Charts
- Short Run Control Charts
- Shuffle, Back Propagation (in Neural Net)
- Shuffle Data (in Neural Networks)
- Sigma Restricted Model
- Sigmoid Function
- Signal Detection Theory
- Simple Random Sampling (SRS)
- Simplex Algorithm
- Single and Multiple Censoring
- Singular Value Decomposition
- Six Sigma (DMAIC)
- Six Sigma Process
- Skewness
- Slicing (Categorizing)
- Smoothing
- SOFMs Self-Organizing Maps Kohonen Net
- Softmax
- Space Plots 3D
- SPC
- Spearman R
- Special Causes
- Spectral Plot
- Spikes (3D graphs)
- Spinning Data (in 3D space)
- Spline (2D graphs)
- Spline (3D graphs)
- Split Selection (for Classification Trees)
- Splitting (Categorizing)
- Spurious Correlations
- SQL
- Square Root of the Signal to Noise Ratio (f)
- Stacked Generalization
- Stacking (Stacked Generalization)
- Standard Deviation
- Standard Error
- Standard Error of the Mean
- Standard Error of the Proportion
- Standardization
- Standardized DFFITS
- Standardized Effect (Es)
- Standard Residual Value
- Stars (Icon Plots)
- Stationary Series (in Time Series)
- STATISTICA Advanced Linear/Nonlinear
- STATISTICA Automated Neural Networks
- STATISTICA Base
- STATISTICA Data Miner
- STATISTICA Data Warehouse
- STATISTICA Document Management System
- STATISTICA Enterprise
- STATISTICA Enterprise/QC
- STATISTICA Enterprise Server
- STATISTICA Enterprise SPC
- STATISTICA Monitoring and Alerting Server
- STATISTICA MultiStream
- STATISTICA Multivariate Stat. Process Ctrl
- STATISTICA PI Connector
- STATISTICA PowerSolutions
- STATISTICA Process Optimization
- STATISTICA Quality Control Charts
- STATISTICA Sequence Assoc. Link Analysis
- STATISTICA Text Miner
- STATISTICA Variance Estimation Precision
- Statistical Power
- Statistical Process Control (SPC)
- Statistical Significance (p-value)
- Steepest Descent Iterations
- Stemming
- Steps
- Stepwise Regression
- Stiffness Parameter (in Fitting Options)
- Stopping Conditions
- Stopping Conditions (in Neural Networks)
- Stopping Rule (in Classification Trees)
- Stratified Random Sampling
- Stub and Banner Tables
- Studentized Deleted Residuals
- Studentized Residuals
- Student's t Distribution
- Sum-Squared Error Function
- Sums of Squares (Type I, II, III (IV, V, VI))
- Sun Rays (Icon Plots)
- Supervised Learning (in Neural Networks)
- Support Value (Association Rules)
- Support Vector
- Support Vector Machine (SVM)
- Suppressor Variable
- Surface Plot (from Raw Data)
- Survival Analysis
- Survivorship Function
- Sweeping
- Symmetrical Distribution
- Symmetric Matrix
- Synaptic Functions (in Neural Networks)

###### T

- Tables
- Tapering
- t Distribution (Student's)
- Tau, Kendall
- Ternary Plots, 2D - Scatterplot
- Ternary Plots, 3D
- Ternary Plots, 3D - Categorized Scatterplot
- Ternary Plots, 3D - Categorized Space
- Ternary Plots, 3D - Categorized Surface
- Ternary Plots, 3D - Categorized Trace
- Ternary Plots, 3D - Contour/Areas
- Ternary Plots, 3D - Contour/Lines
- Ternary Plots, 3D - Deviation
- Ternary Plots, 3D - Space
- Text Mining
- THAID
- Threshold
- Time Series
- Time Series (in Neural Networks)
- Time-Dependent Covariates
- Tolerance (in Multiple Regression)
- Topological Map
- Trace Plots, 3D
- Trace Plot, Categorized (Ternary Graph)
- Training/Test Error/Classification Accuracy
- Transformation (Probit Regression)
- Trellis Graphs
- Trimmed Means
- t-Test (independent & dependent samples)
- Tukey HSD
- Tukey Window
- Two-State (in Neural Networks)
- Type I, II, III (IV, V, VI) Sums of Squares
- Type I Censoring
- Type II Censoring
- Type I Error Rate

###### U

###### V

###### W

###### X

###### Y

###### Z

Nagelkerke Generalized Coefficient of Determination. In order to achieve a maximum value of one, Nagelkerke adjusted the Cox-Snell coefficient of determination.

Naive Bayes. A statistical method based on Bayesian theorem that is primarily used for classification tasks.

Neat Scaling of Intervals. The term *neat scaling* is used to refer to the manner in which ranges of values are divided into intervals, so that the resulting interval boundaries and steps between those boundaries are intuitive and readily interpretable (or "understood").

For example, suppose we want to create a histogram for data values in the range from 1 to 10. It would be inefficient to use interval boundaries for the histogram at values such as 1.3, 3.9, 6.5, etc., i.e., to use as a minimum boundary value 1.3, and then a step size of 2.6. A much more intuitive way to divide the range of data values would be to use boundaries like 1, 2, 3, 4, and so on, i.e., a minimum boundary at 1, with step size of 1; or we could use 2, 4, 6, etc, i.e., a minimum boundary of 2 and step size 2.

In general, *neat* in this context means that category boundaries will be round values ending either in 0, 2, or 5 (e.g., boundaries may be 0.1, 0.2, 0.3, etc.; or 50, 100, 150, etc.). To achieve this, any user-requested lower limit, upper limit, and number of categories will only be approximated.

Negative Correlation. The relationship between two variables is such that as one variable's values tend to increase, the other variable's values tend to decrease. This is represented by a negative correlation coefficient. See also, Correlations - Introductory Overview.

Negative Exponential (2D Graphs). A curve is fitted to the *XY* coordinate data according to the negative exponentially-weighted smoothing procedure (the influence of individual points decreases exponentially with the horizontal distance from the respective points on the curve).

Negative Exponential (3D Graphs). A surface is fitted to the *XYZ* coordinate data according to the negative exponentially- weighted smoothing procedure (the influence of individual points decreases exponentially with the horizontal distance from the respective points on the surface).

Neighborhood (in Neural Networks). In Kohonen training, a square set of units focused around the "winning" unit and simultaneously updated by the training algorithm.

Nested Factors. In nested designs the levels of a factor are nested (the term was first used by Ganguli, 1941) within the levels of another factor. For example, if we were to administer four different tests to four high school classes (i.e., a between-groups factor with 4 levels), and two of those four classes are in high school A, whereas the other two classes are in high school B, then the levels of the first factor (4 different tests) would be nested in the second factor (2 different high schools). See also, ANOVA/MANOVA.

Nested Sequence of Models. In Structural Equation Modeling, a set of models M(1), M(2), ... M(k) form a nested sequence if model M(i) is a special case of M(i+1) for i=1 to k-1. Thus, each model in the sequence becomes increasingly more general, but includes all previous models as special cases. As an example, consider one factor, two factor, and three factor models for 10 variables. The two factor model includes the one factor model as a special case (simply let all the loadings on the second factor be 0). Similarly, the three factor model contains the two and one factor models as special cases.

Neural Networks. *Neural Networks* are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data.

For more information, see Neural Networks; see also Data Mining, and *STATISTICA Automated Neural Networks*.

Neuron. A unit in a neural network.

Newman-Keuls Test. This post hoc test can be used to determine the significant differences between group means in an analysis of variance setting. The *Newman-Keuls* test, like Duncan's test, is based on the range statistic (for a detailed discussion of different post hoc tests, see Winer, Michels, & Brown (1991). For more details, see General Linear Models. See also, Post Hoc Comparisons. For a discussion of statistical significance, see Elementary Concepts.

N-in-One Encoding. For nominal variables with more than two states, the practice of representing the variable using a single unit with a range of possible values (actually implemented using minimax, explicit or none). See also, Neural Networks.

Noise Addition (in Neural Networks). A practice (used in neural networks) designed to prevent overlearning during back propagation training, by adding random *noise* to input patterns during training (and so "blurring" the position of the training data). See, Neural Networks.

Nominal Scale. This is a categorical (i.e., quantitative and not qualitative) scale of measurement where each value represents a specific category that the variable's values fall into (each category is "different" than the others but cannot be quantitatively compared to the others).

See also, Elementary Concepts.

Nominal Variables. Variables that take on one of a set of discrete values, such as *Gender*={*Male*, *Female*}. In neural networks, *nominal output variables* are used to distinguish classification tasks from regression tasks. See also, Grouping (or Coding) Variable and Measurement scales.

Nonlinear Estimation. In the most general terms, *Nonlinear estimation* involves finding the best fitting relationship between the values of a dependent variable and the values of a set of one or more independent variables (it is used as either a hypothesis testing or exploratory method). For example, we may want to compute the relationship between the dose of a drug and its effectiveness, the relationship between training and subsequent performance on a task, the relationship between the price of a house and the time it takes to sell it, etc. Research issues in these examples are commonly addressed by such techniques as multiple regression (see, *Multiple Regression*) or analysis of variance (see, *ANOVA/MANOVA*). In fact, we can think of *Nonlinear estimation* as a *generalization* of those methods. Specifically, multiple regression (and ANOVA) assumes that the relationship between the independent variable(s) and the dependent variable is *linear* in nature. *Nonlinear Estimation* leaves it up to us to specify the nature of the relationship; for example, we may specify the dependent variable to be a logarithmic function of the independent variable(s), an exponential function, a function of some complex ratio of independent measures, etc. (However, if all variables of interest are categorical in nature, or can be converted into categorical variables, we may also consider *Correspondence Analysis* as an alternative analysis technique.)

For more information, see Nonlinear Estimation.

Nonparametrics. *Nonparametric* methods were developed to be used in cases when the researcher does not know the parameters of the distribution of the variable of interest in the population (hence the name *nonparametric*). In more technical terms, *nonparametric* methods do not rely on the estimation of parameters (such as the mean or the standard deviation) describing the distribution of the variable of interest in the population. Therefore, these methods are also sometimes (and more appropriately) called *parameter-free* methods or *distribution-free* methods.

For more information, see Nonparametrics Introductory, see also Elementary Concepts.

Non-Outlier Range. The non-outlier range is the range of values in the 2D Box Plots, 3D Sequential Graphs - Box Plots, or Categorized Box Plots, which fall below the upper outlier limit (for example, +1.5 * the height of the box) and above the lower outlier limit (for example, -1.5 * the height of the box).

Nonseasonal, Damped Trend. In this Time Series model, the simple exponential smoothing forecasts are "enhanced" by a damped trend component (independently smoothed with parameters for the trend, and for the damping effect). For example, suppose we wanted to forecast from month to month the percentage of households that own a particular consumer electronics device (e.g., a VCR). Every year, the proportion of households owning a VCR will increase, however, this trend will be damped (i.e., the upward trend will slowly disappear) over time as the market becomes saturated.

To compute the smoothed value (forecast) for the first observation in the series, both estimates of *S _{0}* and

*T*(initial trend) are necessary. By default, these values are computed as:

_{0}T_{0} = (1/)*(X_{n}-X_{1})/(N-1)

where

N is the number of cases in the series,

is the smoothing parameter for the damped trend

and S_{0} = X_{1}-T_{0}/2

Nonseasonal, Exponential Trend. In this Time Series model, the simple exponential smoothing forecasts are "enhanced" by an exponential trend component (smoothed with parameter ). For example, suppose we wanted to predict the overall monthly costs of repairs to a production facility. There could be an exponential trend in the cost, that is, from year to year the costs of repairs may increase by a certain percentage or factor, resulting in a gradual exponential increase in the absolute dollar costs of repairs.

To compute the smoothed value (forecast) for the first observation in the series, both estimates of *S _{0}* and

*T*(initial trend) are necessary. By default, these values are computed as:

_{0}T_{0} = (X_{2}/X_{1})

and

S_{0} = X_{1}/T_{0}^{1/2}

Nonseasonal, Linear Trend. In this Time Series model, the simple exponential smoothing forecasts are "enhanced" by a linear trend component that is smoothed independently via the (gamma) parameter (see discussion of trend smoothing parameters). This model is also referred to as *Holt's two parameter* method. This model would, for example, be adequate when producing forecasts for spare parts inventories. The need for particular spare parts may slowly increase or decrease over time (the trend component), and the trend may slowly change as different machines etc. age or become obsolete, thus affecting the trend in the demand for spare parts for the respective machines.

In order to compute the smoothed value (forecast) for the first observation in the series, both estimates of *S _{0}* and

*T*(initial trend) are necessary. By default, these values are computed as:

_{0}T_{0} = (X_{n}-X_{1})/(N-1)

where

*N* is the length of the series,

and S_{0} = X_{1}-T_{0}/2

Nonseasonal, No Trend. This Time Series model is equivalent to the simple exponential smoothing model. Note that, by default, the first smoothed value will be computed based on an initial *S _{0}* value equal to the overall mean of the series.

Normal Distribution. The normal distribution (the term first used by Galton, 1889) function is determined by the following formula:

f(x) = 1/[2*)^{1/2}*] * e**{-1/2*[(x-µ)/]^{2}}

- < x <

where

µ is the mean

is the standard deviation

e is the base of the natural logarithm, sometimes called Euler's e (2.71...)

is the constant Pi (3.14...)

See also, Bivariate Normal Distribution, Elementary Concepts (Normal Distribution), Basic Statistics - Tests of Normality

Normal Fit. The normal/observed histogram represents the most common graphical test of normality. When this fit is selected, a normal curve will be overlaid on the frequency distribution. The normal function fitted to histograms is defined as:

f(x) = NC * step * normal(x, mean, std.dev)

The normal function fitted to cumulative histograms is defined as:

f(x) = NC * inormal(x, mean, std.dev.)

where

NC is the number of cases.

step is the categorization step size

(e.g., the integral categorization step size is 1).

normal is the normal function.

inormal is the integral of the normal function.

See also, Normal Distribution, and Bivariate Normal Distribution.

Normality Tests. A common application for distribution fitting procedures is when we want to verify the assumption of normality before using some parametric test (see Basic Statistics and Nonparametric Statistics). A variety of statistics for testing normality are available including the Kolmogorov-Smirnov test for normality, the Shapiro-Wilks' W test, and the Lilliefors test. Additionally, review probability plots and normal probability plots to assess whether the data are accurately modeled by a normal distribution.

Normalization. Adjusting a series (vector) of values (typically representing a set of measurements, e.g., a variable storing heights of people, represented in inches) according to some transformation function in order to make them comparable with some specific point of reference (for example, a unit of length or a sum). For example, dividing these values by 2.54 will produce metric measurements of the height. Normalization of data is:

(a) required when the incompatibility of the measurement units across variables may affect the results (e.g., in calculations based on cross products) without carrying any interpretable information, and

(b) recommended whenever the final reports could benefit from expressing the results in specific meaningful/compatible units (e.g., reaction time data will be easier to interpret when converted into milliseconds from the CPU cycles of different computers that were used to measure RT's - as originally registered in a medical experiment).

Note that this term is unrelated to the term normal distribution; see also standardization.

Normal Probability Plots. This type of graph is used to evaluate the normality of the distribution of a variable, that is, whether and to what extent the distribution of the variable follows the normal distribution. The selected variable will be plotted in a scatterplot against the values "expected from the normal distribution."

The standard normal probability plot is constructed as follows. First, the deviations from the mean (residuals) are rank ordered. From these ranks the program computes z values (i.e., standardized values of the normal distribution) based on the assumption that the data come from a normal distribution (see Computation Note). These z values are plotted on the *Y*-axis in the plot. If the observed residuals (plotted on the *X*-axis) are normally distributed, then all values should fall onto a straight line. If the residuals are not normally distributed, then they will deviate from the line. Outliers may also become evident in this plot. If there is a general lack of fit, and the data seem to form a clear pattern (e.g., an *S* shape) around the line, then the variable may have to be transformed in some way .

See also, Normal Probability Plots (Computation Note).

Normal Probability Plots (Computation Note). The following formulas are used to convert the ranks into expected normal probability values, that is, the respective normal *z* values.

**Normal probability plot. **The normal probability value *z _{j}* for the

*j*th value (rank) in a variable with

*N*observations is computed as:

z _{j} = ^{-1} [(3*j-1)/(3*N+1)]

where ^{-1} is the inverse normal cumulative distribution function (converting the normal probability *p* into the normal value *z*).

**Half-normal probability plot. **Here, the half-normal probability value *z _{j}* for the

*j*th value (rank) in a variable with

*N*observations is computed as:

z _{j} = ^{-1} [3*N+3*j-1)/(6*N+1)]

where ^{-1} is again the inverse normal cumulative distribution function.

**Detrended normal probability plot. **In this plot each value (*x _{j}*) is standardized by subtracting the mean and dividing by the respective standard deviation (

*s*). The detrended normal probability value

*z*for the

_{j}*j*th value (rank) in a variable with

*N*observations is computed as:

z _{j} = ^{-1} [(3*j-1)/(3*N+1)] - (x _{j}-mean)/s

where ^{-1} is again the inverse normal cumulative distribution function.

n Point Moving Average Line. Each point on this moving average line represents the average of the respective sample and the *n-1* number of preceding samples. Thus, this line will *smooth* the pattern of means across samples, allowing the quality control engineer to detect trends. You can specify the number of samples (*n*) that are to be averaged for each point in the plot. For more information, see Time Series.