### 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

X11 Output: A 1. Original Series. This *X-11* table will show the original series, prior to any initial user-defined or trading-day adjustment. Note that for quarterly series, no prior adjustment factors can be specified, and the original series will be shown as table B 1.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: A 2. Prior Monthly Adjustment Factors. For *X-11* monthly series, the user may specify a second series that contains prior monthly adjustment factors, for example, in order to adjust for an unusual holiday etc. The factors specified here will be subtracted from the original series for additive models, or will be used to divide the original series if multiplicative seasonal adjustment was requested (thus, the values in this series must be unequal to zero in that case).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: A 3. Original Series Adjusted by Prior Monthly Adjustment Factors. In this *X-11* monthly series, the factors specified in A 2 will be subtracted from the original series (additive adjustment) or they will be used to divide the values in the original series (multiplicative adjustment). The resulting adjusted series is shown in this table.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: A 4. Prior Trading-Day Adjustment Factors. This *X-11* table is only available (applicable) when prior trading-day adjustment factors and a multiplicative model were specified. The user may specify a weight for each day (Monday through Friday); those weights are then proportionately adjusted so that they add to 7. The series (A 1 or A 3) is then divided by monthly calendar factors that are computed based on the number of the respective days in the respective month. Note that by default, the calendar factors are also adjusted for different lengths of different months; however, the length of month variability can also be included in the calendar factors (in which case a constant length of month of 30.4375 is used).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 1. Prior Adjusted Series or Original Series. This *X-11* table shows the original series, or the initial adjusted series, depending on whether or not prior monthly adjustment factors and/or trading day adjustment factors where specified (for quarterly *X-11*, B 1 is always the original series).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 2. Trend-Cycle. The initial trend-cycle estimate is computed as a centered 12-term moving average of B 1.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 3. Unmodified S-I Differences or Ratios. An initial estimate of the combined irregular and seasonal component is obtained by subtracting B 2 from B 1 (additive model) or dividing B 1 by B 2 (multiplicative model).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 4. Replacement Values for Extreme S-I Differences (Ratios). First a preliminary estimate of the *X-11* seasonal component is computed by applying a weighted 5-term moving average separately to the B 3 values for each month. Then a centered 12-term moving average of the preliminary factors for the entire series is computed, and the resulting values are adjusted to sum to zero (additive model) or 12.0 (multiplicative model) within each year. Next an initial estimate of the irregular component is obtained by subtracting from the S-I differences (additive model) or dividing the S-I ratios by the initial estimate for the seasonal component. For the resulting initial estimate of the irregular component, a 5-year sliding standard deviation (s, sigma) is computed, and extreme values in the central year that are beyond 2.5*s are removed. The 5-year sliding s is then recomputed and the process repeated; however, this time a zero weight is assigned to irregular values beyond 2.5*s, a full weight is assigned for values within 1.5*s, and linearly graduated weights between zero and one are assigned for values between 1.5 and 2.5 * s. Values receiving less than full weights are then recomputed as the average of the respective value times its weight and the nearest two full- weight values preceding and following the respective value in that month. Table B 4 shows the final replaced (re-computed) values, and the sliding 5-year s's.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 5. Seasonal Factors. The extreme values in the B 3 series are replaced by the values shown in B 4. From this *X-11* series, preliminary seasonal factors are derived by applying a 5-term moving average to each month separately; then a 12-term moving average is computed for the entire series, and the resulting values adjusted to sum to zero (additive model) or 12.0 (multiplicative model) within each year.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 6. Seasonally Adjusted Series. The preliminary seasonally adjusted series is obtained by subtracting from B 1 (additive model) or dividing B 1 (multiplicative model) by the seasonal factors in B 5.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 7. Trend-Cycle. The *X 11* seasonally adjusted series (B 6) is smoothed via a variable moving average procedure (see Shiskin, Young, & Musgrave, 1967, for details). Optionally, extremes can be removed from the smoothed series by a process analogous to that described under B 4. In general, the so- called Henderson curve moving average is applied, which is a weighted moving average with the magnitudes of the weights following a bell-shaped curve (see, for example, Makridakis and Wheelwright, 1978, or Shiskin, Young, and Musgrave, 1967). The choice of the appropriate length of the moving average is an important issue in the seasonal decomposition (i.e., the computation of the trend-cycle component). The general idea is to choose a longer moving average when there is a lot of random fluctuation in the data relative to the trend-cycle component, and to choose a shorter moving average when there is only relative little random fluctuation. By default the program will select a moving average transformation automatically. Specifically, first a preliminary 13-term Henderson (weighted) moving average of the seasonally adjusted series is computed (without extending to the ends of the series). A preliminary estimate of the irregular component is then computed by subtracting this series from (additive model) or dividing it into (multiplicative model) the seasonally adjusted series. Next, the average month- to-month difference (percent change) without regard to sign is computed for both the estimated irregular and trend-cycle components. The ratio of the average month-to-month differences (percent changes) in the two series reflects the relative importance of the irregular variations relative to the movements in the trend-cycle component. Depending on the value of this ratio, either a 9-term Henderson moving average is selected (if the ratio is between 0.0 and .99), a 13- term Henderson moving average is selected (if the ratio is between 1.0 and 3.49) or a 23-term Henderson moving average is selected (if the ratio is greater than 3.5).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 8. Unmodified S-I Differences (Ratios). This *X-11* table is the same as B 3 except that it is based on the trend-cycle values computed in X-11 Census Method II Seasonal Adjustment.

X11 Output: B 9. Replacement Values for Extreme S-I Differences (Ratios). This *X-11* table is the same as B 4 except that the differences (ratios) in B 8 are used to which a 7 term moving average is applied (to estimate the seasonal factors).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 10. Seasonal Factors. After replacing extreme values by the corresponding B 9 values, a 7-term weighted moving average is applied to the S-I differences (ratios) in B 8. The resulting estimate of the seasonal factors is then adjusted so that the sum for each year is equal to zero (additive model) or 12.0 (multiplicative model).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 11. Seasonally Adjusted Series. This *X-11* table is the same as B 6, except that the seasonal factors in B 10 are used.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 13. Irregular Series. The trend-cycle estimates in B 7 are subtracted from the seasonally adjusted series in B 11 (additive model), or the B 7 values are used to divide the series in B 11 (multiplicative model). The resulting series is an improved estimate of the irregular series.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 14. Extreme Irregular Values Excluded from Trading-Day Regression. The months in the series are sorted into different groups, depending on the particular day when the month begins (30-day, 31-day months, and Februarys are treated separately). Then extreme values (beyond 2.5 * s; different s values can also be specified) are identified within each type of month in a two-step procedure. The final extreme values that will be excluded are shown in this *X-11* table.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 15. Preliminary Trading-Day Regression. After removing the B 14 extreme values from B 13, least squares estimates for the seven daily weights are computed.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 16. Trading-Day Adjustment Factors Derived from Regression Coefficients. From the trading-day regression weights, monthly adjustment factors are computed based on the number of particular trading days (i.e., Mondays, Tuesdays, etc.) in the respective months. These factors are printed in this *X-11* table, and are then used to adjust (i.e., subtracted from or divided into) the B 13 irregular series for trading-day variation.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 17. Preliminary Weights for Irregular Component. The estimates of the irregular component (in B 13 or adjusted by B 16, depending on whether or not a trading-day adjustment was performed) are further refined by computing graduated weights for extreme values, depending on their relative (in terms of a sliding 5-year s) distance from 0. Specifically, a process analogous to that described in B 4 above is used. This *X-11* table (B17) contains the resulting adjustment factors.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 18. Trading-Day Factors Derived from Combined Daily Weights. This *X-11* table contains the final trading day adjustment factors, computed from the least squares trading-day weights in B 15 and/or the prior trading-day weights in A 4.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: B 19. Original Series Adjusted for Trading-Day and Prior Variation. The values in B 18 are used to adjust the original (adjusted) series (in A 1, A 3, or B 1, depending on whether or not prior adjustment factors were specified). Specifically, the values in B 18 are subtracted from (additive model) or divided into (multiplicative model) the original series.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 1. Original Series Modified by Preliminary Weights and Adjusted for Trading-Day and Prior Variation. The series in B 19 (or B 1 if no trading-day adjustment was requested) is adjusted for extreme values by the weights computed in B 17 The resulting modified series in shown in this *X-11* table (C 1).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 2. Trend-Cycle. An estimate of the combined trend-cycle component is computed from C 1 by applying a centered 12-term moving average.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 4. Modified S-I Differences (Ratios). To obtain the refined S-I differences (ratios), the values in C 2 are subtracted from (additive model) or divided into (multiplicative model) the modified series in C 1.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 5. Seasonal Factors. These values are the same as those in B 5, except that the C 4 differences (ratios) are used.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 6. Seasonally Adjusted Series. The preliminary seasonally adjusted series is computed by subtracting C 5 from (or dividing C 5 into) C 1.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 7. Trend-Cycle. The seasonally adjusted series (C 6) is smoothed via a variable moving average procedure (the same procedure used for B 7, see also Shiskin, Young, & Musgrave, 1967, for details) to derive the preliminary estimate of the trend-cycle component.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 9. Modified S-I Differences (Ratios). The modified S-I differences (ratios) are computed by subtracting C 7 from (additive models) or dividing C 7 into (multiplicative models) the C 1 series.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 10. Seasonal Factors. The seasonal factors are computed analogously to B 10, but based on the C 9 S-I differences (ratios).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 11. Seasonally Adjusted Series. The refined seasonally adjusted series is computed by subtracting from B 1 (additive model) or dividing B 1 by (multiplicative model) the values in C 10.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 13. Irregular Series. The refined estimate of the irregular (random) component is computed by subtracting from C 11 (additive model) or dividing C 11 by (multiplicative model) the values in C 7.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 14. Extreme Irregular Values Excluded from Trading-Day Regression. This table is analogous to table B 14, and it shows the extreme irregular values (usually beyond 2.5 * s) after re-applying the trading-day routine (based on the monthly trading-day factors shown in B 16).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 15. Final Trading-Day Regression. This *X-11* table is the same as B 15, except that the computations are based on the values from table C 13.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 16. Final Trading-Day Adjustment Factors Derived from Regression X11 Output: Coefficients. This *X-11* table is analogous to B 16, except that the factors are subtracted from (additive case) or divided into (multiplicative case) the values from table C 13.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 17. Final Weights for Irregular Component. This table is analogous to table B 17, except that it is computed based on the values in C 16 (or C 13 if no trading day adjustment is requested).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 18. Final Trading-Day Factors Derived from Combined Daily Weights. This *X-11* table is analogous to B 18, except that the final weights shown in C 15 are used in the computations.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: C 19. Original Series Adjusted for Trading-Day and Prior Variation. The values in C 18 are used to adjust the original (adjusted) series (in A 3 or B 1). Specifically, the values in C 18 are subtracted from (additive model) or divided into (multiplicative model) the original series.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 1. Original Series Modified by Final Weights and Adjusted for Trading-Day and Prior Variation. This X-11 table is analogous to C 1, except that the C 17 weights and C 19 adjusted series are used in the computations.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 2. Trend-Cycle. A 12-term moving average of D 1 is computed to estimate the trend-cycle component.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 4. Modified S-I Differences (Ratios). The modified S-I differences (ratios) are computed by subtracting D 2 from (additive model) or dividing D 2 into (multiplicative model) the values in D 1.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 5. Seasonal Factors. This X-11 table is computed analogously to B 5, except that the computations are based on the values in D 4.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 6. Seasonally Adjusted Series. The values in this table are computed by subtracting D 5 from D 1 (additive model) or dividing D 1 by D5 (multiplicative model).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 7. Trend-Cycle. The values in this *X-11* table are computed analogously to those in B 7, except that the computations are based on the values in D 6.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 8. Final Unmodified S-I Differences (Ratios). The values in the D 7 series are subtracted from (additive model) or divided into (multiplicative case) the values in C 19 (or B 1 if no adjustment for trading-day variation is applied). Then an analysis of variance by month (or quarter) is performed on this series, in order to test for the presence of stable significant seasonality.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 9. Final Replacement Values for Extreme S-I Differences (Ratios). The values in D 7 are subtracted from (additive model) or divided into (multiplicative model) D 1; values that are not identical to the corresponding entries in D 8 are then reported. Also, for each month, the year-to-year difference (additive model) or percent change (multiplicative mode) in the estimates of the irregular and the seasonal components and their ratio (called MSR, moving seasonality ratio) are computed. The MSR may be useful in order to determine the amount of moving seasonality present in each month.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 10. Final Seasonal Factors. This *X-11* table is computed analogously to the values in B 10, except that it is computed based on the values reported in D 8 and D 9.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 11. Final Seasonally Adjusted Series. The final seasonally adjusted series is computed by subtracting D 10 from C 19 (additive model) or dividing C 19 by D 10 (multiplicative model).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 12. Final Trend-cycle. These values are computed by subtracting D 10 from D 1 (additive model), or by dividing D 1 by D 10 (multiplicative model).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: D 13. Final Irregular. These values are computed by subtracting D 12 from D 11 (additive model), or by dividing D 11 by D 12 (multiplicative model).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: E 1. Modified Original Series. The values in this *X-11* table are computed by replacing in the original series extreme values (identified by a zero weight in C 17) by the values predicted from the final trend-cycle, seasonal, trading-day (if applicable), and prior adjustment (if applicable) components.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: E 2. Modified Seasonally Adjusted Series. These values are computed by replacing in the final seasonally adjusted series (D 11) extreme values (identified by a zero weight in C 17) with the D 12 final trend-cycle values.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: E 3. Modified Irregular Series. The values in this X-11 table are computed by replacing the values in D 13 with zero (additive model) or 1.0 (multiplicative model) if they were identified as extremes (i.e., assigned zero weight) in C 17.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: E 4. Differences (Ratios) of Annual Totals. These values are computed as the differences (additive model) or ratios (multiplicative model) of the annual totals of (a) the original series B 1 and the final seasonally adjusted series D 11, (b) the modified original series E 1 and the modified seasonally adjusted series E 2.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: E 5. Differences (Percent Changes) in Original Series. The values in this *X-11* table are computed as the month-to-month (quarter-to-quarter) differences (additive model) or percent changes (multiplicative model) in B 1.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: E 6. Differences (Percent Changes) in Final Seasonally Adjusted Series. These values are the month-to-month (quarter-to-quarter) differences (additive model) or percent changes (multiplicative model) in D 11.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: F 1. MCD (QCD) Moving Average. The values in this series are computed by applying an unweighted moving average to the final seasonally adjusted series (D 11). The width of the smoothing window is determined by the month (quarter) for cyclical dominance, or MCD (QCD) for short. The MCD (QCD) is computed as the average span at which the changes in the random component are equal to the changes in the trend-cycle component

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: F 2. Summary Measures. Several final summary *X-11* tables are computed:

- The average differences (additive model) or percent changes (multiplicative model) are computed without regard to sign across spans 1, 2, 3 ..., 12 months (or four quarters) for the following series: Original series A 1 (B 1), final seasonally adjusted series (D 11), final irregular series (D 13), final trend-cycle (D 12), final seasonal factors (D 10), final prior monthly adjustment factors (A 2, monthly
*X-11*only), final trading-day adjustment factors (C 18, monthly*X-11*only), modified original series (E 1), modified seasonally adjusted series (E 2), modified irregular series (E 3). - Next a table of relative contributions of the different components to the differences (additive model) or percent changes (multiplicative model) in the original series are computed.
- The next table reports the average duration of run (the average number of consecutive monthly changes in the same direction; "no change" is counted as a change in the same direction) for the following series: Final seasonally adjusted series (D 11), final irregular series (D 13), final trend-cycle (D 12), and the MCD (QCD) moving average (F 1).
- Finally, the means and standard deviations of differences (additive model) or percent changes (multiplicative model) are computed across different spans for each of the series mentioned above.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: G 1. Chart. This line graph will show the final seasonally adjusted series and final trend-cycle components (D 11 and D 12, respectively).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: G 2. Chart. This line graph will show the final S-I differences (additive model) or ratios (multiplicative model) with the extremes, the final S-I differences (ratios) *without* extremes, and the final seasonal factors (i.e., D 8, D 9, and D 10, respectively), categorized by month (X-11 monthly) or quarter (X-11 quarterly).

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: G 3. Chart. This plot shows the same values as G 2; however, this line plot shows those values in chronological order.

For more information, see X-11 Census Method II Seasonal Adjustment.

X11 Output: G 4. Chart. This is a line graph of the final irregular and final modified irregular series (D 13 and E 3, respectively).

For more information, see X-11 Census Method II Seasonal Adjustment.

XML (Extensible Markup Language). XML (short for Extensible Markup Language) is a specification language developed by the World Wide Web Consortium (W3C). XML is a language standard designed especially for Web documents, to allow programmers to create their own customized tags, thus enabling the definition, transmission, validation, and interpretation of data between applications and between organizations. A special version of .XML is PMML.