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-Normal 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 - Aggregrated
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 Temporary Impact
Abrupt Permanent 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
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)
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 Networks
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 Test for Homogeneity of Variances
Brushing
Burt Table


C
Canonical Correlation
Cartesian Coordinates
Casewise MD Deletion
Categorical Dependent Variable
Categorical Predictor
Categorized Graphs
Categorized Plots, 2D - Detrended Probability Plots
Categorized Plots, 2D - Half-Normal Probability Plots
Categorized Plots, 2D - Normal Probability Plots
Categorized Plots, 2D - Probability-Probability Plots
Categorized Plots, 2D - Quantile-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 Data)
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 Error Function (in Neural Networks)
City-block (Manhattan) distance
Classification
Classification (in Neural Networks)
Classification and Regression Trees
Classification by Labeled Exemplars (in Neural Networks)
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)
Codes
Coding Variable
Coefficient of Determination
Column Sequential/Stacked Plot
Columns (Box Plot)
Columns (Icon Plot)
Common Causes
Communality
Complex Numbers
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 Networks)
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
Cpk, Cp, Cr
CRISP
Cross Entropy (in Neural Networks)
Cross Verification (in Neural Networks)
Cross-Validation
Crossed Factors
Crosstabulations
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
Deleted residual
Delta-Bar-Delta (in Neural Networks)
Degrees of Freedom
Denominator Synthesis
Dependent t-test
Dependent vs. Independent Variables
Deployment
Derivative-free Function Minimization Algorithms
Design, Experimental
Design Matrix
Desirability Profiles
Detrended Probability Plots
Deviance
Deviance Residuals
Deviation
Deviation Assignment Algorithms (in Neural Networks)
Deviation Plot (Ternary Graph)
Deviation Plots, 3D
DFFITS
DIEHARD Suite of Tests and Random Number Gereration
Differencing (in Time Series)
Dimensionality Reduction
Discrepancy Function
Discriminant Function Analysis
Distribution Function
DOE
Double-Y Histograms
Double-Y Line Plots
Double-Y Scatterplot
Drill-Down Analysis
Drilling-down (Categorizing)
DV
Duncan's test
Dunnett's test


E
Effective Hypothesis Decomposition
Efficient score statistic
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)
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 Average Line
Extrapolation
Extreme Values (in Box Plots)
Extreme Value Distribution


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


G
g2 Inverse
Gain Chart
Gamma coefficient
Gamma Distribution
Gaussian Distribution
Gauss-Newton
General ANOVA/MANOVA
General Linear Model
Generalization (in Neural Networks)
Generalized Additive Models
Generalized Inverse
Generalized Linear Model
Generalized Regression Neural Network (GRNN)
Genetic Algorithm
Genetic Algorithm Input Selection
Geometric Distribution
Geometric Mean
Gibbs Sampler
Gompertz Distribution
Gradient
Gradient Descent
Gradual Permanent Impact
GRNN (Generalized Regression Neural Network)
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
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
Incremental (vs. Non-Incremental Learning Algorithms)
Independent t-test
Independent vs. Dependent Variables
Industrial Experimental Design
Inertia
Interactions
Interpolation
Interval Scale
Ishikawa Chart
Intraclass Correlation Coefficient
Invariance Under a Constant Scale Factor (ICSF)
Invariance Under Change of Scale (ICS)
Isotropic Deviation Assignment
Item and Reliability Analysis
IV


J

Jacobian Matrix
Jogging Weights
Johnson Curves
Join
Joining Networks (in Neural Networks)
JPEG
JPG


K
Kernel functions
K-Means algorithm (in Neural Networks)
K-Nearest algorithm
Kendall Tau
Kohonen Algorithm (in Neural Networks)
Kohonen Networks
Kohonen Training
Kolmogorov-Smirnov test
Kronecker Product
Kruskall-Wallis test
Kurtosis


L
Lack of Fit
Lambda Prime
Laplace Distribution
Latent Variable
Layered Compression
Learned Vector Quantization (in Neural Networks)
Learning Rate (in Neural Networks)
Least Squares (2D graphs)
Least Squares (3D graphs)
Least Squares Estimator
Least Squares Means
Left Censoring
Levenberg-Marquardt algorithm (in Neural Networks)
Levene's Test for Homogeneity of Variances
Leverage values
Life Table
Life, Characteristic
Lift Charts
Lilliefors test
Line Plots, 2D
Line Plots, 2D - Aggregrated
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
Loss Matrix (in Neural Networks)
LOWESS Smoothing


M

Machine Learning
Mahalanobis distance
Mallow's CP
Mann-Scheuer-Fertig Test
Manifest Variable
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
MPatt 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
Multivariate Adaptive Regression Splines (MARSplines)
Multi-way Tables


N
n Point Moving Average Line
N-in-One encoding
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
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
Normal Probability Plots (Computation Note)
Normal Probability Plots
Normality tests
Normalization


O

ODBC
Odds Ratio
OLE DB
On-Line Analytic Processing (OLAP)
One-of-N Encoding (in Neural Networks)
One-Off (in Neural Networks)
One-Sample t-test
"One-sided" Ranges or Error Bars in 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
p-level (Statistical Significance)
Pairwise Deletion of Missing Data vs. Mean Substitution
Pairwise MD Deletion
Parametric Curve
Pareto Chart Analyses
Pareto Distribution
Part Correlation
Partial Correlation
Partial Least Squares Regression
Parzen Window
Pearson Correlation
Pearson Curves
Pearson Residuals
Penalty Functions
Percentiles
Perceptrons (in Neural Networks)
Pie Chart - Counts
Pie Chart - Multi-pattern Bar
Pie Chart - Values
Pies (Icon Plots)
PMML (Predictive Model Markup Language)
PNG files
PNN (Probabilistic Neural Networks)
Poisson Distribution
Polar Coordinates
Polygons (Icon Plots)
Polynomial
Portable Network Graphics files
Positive Correlation
Post hoc Comparisons
Post Synaptic Potential (PSP) Function
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
Probabilistic Neural Networks (PNN)
Probability Plots - Detrended
Probability Plots - Normal
Probability Plots - Half-Normal
Probability-Probability Plots
Probability-Probability Plots - Categorized
Probability Sampling
Probit Regression and Transformation
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 Value Decomposition (in Neural Networks)
PSP (Post Synaptic Potential) Function
Pure Error


Q
Quadratic
Quality
Quality Control
Quantiles
Quantile-Quantile Plots
Quantile-Quantile Plots - Categorized
Quartile Range
Quartiles
Quasi-Newton Method
QUEST
Quick Propagation
Quota Sampling


R
r (Pearson Correlation Coefficient)
Radial Basis Functions
Radial Sampling (in Neural Networks)
Random Effects (in Mixed Model ANOVA)
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 Operating Characteristic (ROC) Curve (in Neural Networks)
Regression
Regression (in Neural Networks)
Regression, Multiple
Regression Summary Statistics (in Neural Networks)
Regular Histogram
Regular Line Plots
Regular Scatterplot
Regularization (in Neural Networks)
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 (in Neural Networks)
Root Cause Analysis
Root Mean Square Standardized Effect (RMSSE)
Rosenbrock Pattern Search
Rotating Coordinates, Method of
Runs Tests (in Quality Control)


S
Sampling Fraction
S.D. Ratio
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 - Ternary Graph
Scatterplot Smoothers
Scheffe's test
Score Statistic
Scree Plot, Scree Test
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
Shapiro-Wilks' W test
Shewhart Control Charts
Short Run Control Charts
Shuffle data (in Neural Networks)
Shuffle, Back Propagation (in Neural Networks)
Sigma Restricted Model
Sigmoid function
Signal detection theory
Simple Random Sampling (SRS)
Simplex algorithm
Single Censoring
Singular Value Decomposition
Six Sigma (DMAIC)
Six Sigma Process
Skewness
Slicing (Categorizing)
Smoothing
SOFMs (Self-organizing feature maps; Kohonen Networks)
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 of the Mean
Standard Error of the Proportion
Standard residual value
Standardization
Standardized DFFITS
Standardized Effect (Es)
Stars (Icon Plots)
Stationary Series (in Time Series)
STATISTICA Enterprise-wide Data Analysis System
STATISTICA Data Miner
STATISTICA Enterprise-wide SPC System
STATISTICA Neural Networks
Statistical Power
Statistical Process Control (SPC)
Statistical Significance (p-level)
Steepest Descent Iterations
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
Student's t Distribution
Studentized Deleted Residuals
Studentized Residuals
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)
Suppressor Variable
Surface Plot (from Raw Data)
Survival Analysis
Survivorship Function
Sweeping
Symmetric Matrix
Symmetrical Distribution
Synaptic Functions (in Neural Networks)


T
t Distribution (Student's)
t-test (for independent and dependent samples)
Tables
Tapering
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
Transformation (Logit Regression)
Transformation (Probit Regression)
Trellis Graphs
Trimmed Means
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
Unconfounding, Maximum
Unequal N HSD
Uniform Distribution
Unimodal Distribution
Unit Penalty
Unit Types (in Neural Networks)
Unsupervised Learning (in Neural Networks)
Unweighted Means



V
Variance
Variance Components (in Mixed Model ANOVA)
Variance Inflation Factor (VIF)
V-fold Cross-validation
Voronoi
Voronoi Scatterplot
Voting



W
Wald Statistic
Warehousing, Data
WebSTATISTICA Server applications
Weibull Distribution
Weigend Regularization (in Neural Networks)
Weighted Least Squares
Wilcoxon test
Win Frequencies (in Neural Networks)
Wire


X
X11 output: A 1.  Original Series
X11 output: A 2.  Prior Monthly Adjustment Factors
X11 output: A 3.  Original Series Adjusted by Prior Monthly Adjustment Factors
X11 output: A 4.  Prior Trading Day Adjustment Factors
X11 output: B 1.  Prior Adjusted Series or Original Series
X11 output: B 2.  Trend-cycle
X11 output: B 3.  Unmodified S-I Differences or Ratios
X11 output: B 4.  Replacement Values for Extreme S-I Differences (Ratios)
X11 output: B 5.  Seasonal Factors
X11 output: B 6.  Seasonally Adjusted Series
X11 output: B 7.  Trend-cycle
X11 output: B 8.  Unmodified S-I Differences (Ratios)
X11 output: B 9.  Replacement Values for Extreme S-I Differences (Ratios)
X11 output: B 10.  Seasonal Factors
X11 output: B 11.  Seasonally Adjusted Series
X11 output: B 13.  Irregular Series
X11 output: B 14.  Extreme Irregular Values Excluded from Trading-day Regression
X11 output: B 15.  Preliminary Trading-day Regression
X11 output: B 16.  Trading-day Adjustment Factors Derived from Regression Coefficients
X11 output: B 17.  Preliminary Weights for Irregular Component
X11 output: B 18.  Trading-day Factors Derived from Combined Daily Weights
X11 output: B 19.  Original Series Adjusted for Trading-day and Prior Variation
X11 output: C 1.  Original Series Modified by Preliminary Weights and Adjusted for Trading-day and Prior Variation
X11 output: C 2.  Trend-cycle
X11 output: C 4.  Modified S-I Differences (Ratios)
X11 output: C 5.  Seasonal Factors
X11 output: C 6.  Seasonally Adjusted Series
X11 output: C 7.  Trend-cycle
X11 output: C 9.  Modified S-I Differences (Ratios)
X11 output: C 10.  Seasonal Factors
X11 output: C 11.  Seasonally Adjusted Series
X11 output: C 13.  Irregular Series
X11 output: C 14.  Extreme Irregular Values Excluded from Trading-day Regression
X11 output: C 15.  Final Trading-day Regression
X11 output: C 16.  Final Trading-day Adjustment Factors Derived from Regression X11 output: Coefficients
X11 output: C 17.  Final Weights for Irregular Component
X11 output: C 18.  Final Trading-day Factors Derived From Combined Daily Weights
X11 output: C 19.  Original Series Adjusted for Trading-day and Prior Variation
X11 output: D 1.  Original Series Modified by Final Weights and Adjusted for Trading-day and Prior Variation
X11 output: D 2.  Trend-cycle
X11 output: D 4.  Modified S-I Differences (Ratios)
X11 output: D 5.  Seasonal Factors
X11 output: D 6.  Seasonally Adjusted Series
X11 output: D 7.  Trend-cycle
X11 output: D 8.  Final Unmodified S-I Differences (Ratios)
X11 output: D 9.  Final Replacement Values for Extreme S-I Differences (Ratios)
X11 output: D 10.  Final Seasonal Factors
X11 output: D 11.  Final Seasonally Adjusted Series
X11 output: D 12.  Final Trend-cycle
X11 output: D 13.  Final Irregular
X11 output: E 1.  Modified Original Series
X11 output: E 2.  Modified Seasonally Adjusted Series
X11 output: E 3.  Modified Irregular Series
X11 output: E 4.  Differences (Ratios) of Annual Totals
X11 output: E 5.  Differences (Percent Changes) in Original Series
X11 output: E 6.  Differences (Percent Changes) in Final Seasonally Adjusted Series
X11 output: F 1.  MCD (QCD) Moving Average
X11 output: F 2.  Summary Measures
X11 output: G 1.  Chart
X11 output: G 2.  Chart
X11 output: G 3.  Chart
X11 output: G 4.  Chart
XML (Extensible Markup Language)


Y
Yates Corrected Chi-square
Year 2000 Compatibility