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
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
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 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
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
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
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 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
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
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
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
Jacobian Matrix Jogging Weights Johnson Curves Join Joining Networks (in Neural Networks) JPEG JPG
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
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
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 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
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-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
Quadratic Quality Quality Control Quantiles Quantile-Quantile Plots Quantile-Quantile Plots - Categorized Quartile Range Quartiles Quasi-Newton Method QUEST Quick Propagation Quota Sampling
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)
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 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
Unconfounding, Maximum Unequal N HSD Uniform Distribution Unimodal Distribution Unit Penalty Unit Types (in Neural Networks) Unsupervised Learning (in Neural Networks) Unweighted Means
Variance Variance Components (in Mixed Model ANOVA) Variance Inflation Factor (VIF) V-fold Cross-validation Voronoi Voronoi Scatterplot Voting
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
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)
Yates Corrected Chi-square Year 2000 Compatibility