### 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

Quadratic. Fits a second-order polynomial function to the points in the 3D scatterplot.

Quality. The term *Quality* in the context of correspondence analysis pertains to the quality of representation of the respective row point in the coordinate system defined by the respective numbers of dimensions, as chosen by the user. The quality of a point is defined as the ratio of the squared distance of the point from the origin in the chosen number of dimensions, over the squared distance from the origin in the space defined by the maximum number of dimensions (remember that the metric in the typical correspondence analysis is *Chi-square*). By analogy to *Factor Analysis*, the quality of a point is similar in its interpretation to the communality for a variable in factor analysis.

A low quality means that the current number of dimensions does not represent well the respective row (or column).

Quality Control. In all production processes, the extent to which products meet quality specifications must be monitored. In the most general terms, there are two "enemies" of product quality: (1) deviations from target specifications, and (2) excessive variability around target specifications. During the earlier stages of developing the production process, designed experiments are often used to optimize these two quality characteristics (see *Experimental Design*); the methods discussed in the Quality Control chapter are *on-line* or *in-process* quality control procedures to monitor an on-going production process.

The general approach to on-line quality control is straightforward: We simply extract samples of a certain size from the ongoing production process. We then produce line charts of the variability in those samples, and consider their closeness to target specifications. If a trend emerges in those lines, or if samples fall outside pre-specified limits, then we declare the process to be out of control and take action to find the cause of the problem. These types of charts are sometimes also referred to as Shewhart control charts (named after W. A. Shewhart who is generally credited as being the first to introduce these methods; see Shewhart, 1931).

For more information, see the Quality Control Charts chapter.

Quantiles. The quantile (this term was first used by Kendall, 1940) of a distribution of values is a number *x _{p}* such that a proportion

*p*of the population values are less than or equal to

*x*. For example, the .25 quantile (also referred to as the 25th percentile or lower quartile) of a variable is a value (

_{p}*x*) such that 25% (

_{p}*p*) of the values of the variable fall below that value.

Similarly, the .75 quantile (also referred to as the 75th percentile or upper quartile) is a value such that 75% of the values of the variable fall below that value and is calculated accordingly.

See also, Quantile-Quantile Plots

Quantile-Quantile Plots. You can visually check for the fit of a theoretical distribution to the observed data by examining the *quantile-quantile* (or Q-Q) plot (also called *Quantile Plot*).

In this plot, the observed values of a variable are plotted against the theoretical quantiles. A good fit of the theoretical distribution to the observed values would be indicated by this plot if the plotted values fall onto a straight line. To produce a Q-Q plot, the program will first sort the *n* observed data points into ascending order, so that:

x_{1} x_{2} ... x_{n}

These observed values are plotted against one axis of the graph; on the other axis the plot will show:

F^{-1}((i-r_{adj}) / (n+n_{adj}))

where *i* is the rank of the respective observation, *r _{adj}* and

*n*are adjustment factors (0.5) and

_{adj}*F*denotes the inverse of the probability integral for the respective standardized distribution. The resulting plot (see below) is a scatterplot of the observed values against the (standardized) expected values, given the respective distribution. Note also that the adjustment factors

^{-1}*r*and

_{adj}*n*ensure that the p-value for the inverse probability integral will fall between 0 and 1, but not including 0 and 1 (see Chambers, Cleveland, Kleiner, and Tukey, 1983.

_{adj}Quantile-Quantile Plots - Categorized. In this graph, you can visually check for the fit of a theoretical distribution to the observed data by examining each *quantile-quantile* (or Q-Q) plot (also called *Quantile Plot*, see Quantile-Quantile Plots) for the respective level of the grouping variable (or user-defined subset of data).

In this plot, the observed values of a variable are plotted against the theoretical quantiles. A good fit of the theoretical distribution to the observed values would be indicated by this plot if the plotted values fall onto a straight line. One component graph is produced for each level of the grouping variable(or user-defined subset of data) and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories). (See *Quantile-Quantile Plots* for more details on how to produce a Q-Q plot.)

Quartile Range. The quartile (this term was first used by Galton, 1882) range of a variable is calculated as the value of the 75th percentile minus the value of the 25th percentile. Thus it is the width of the range about the median that includes 50% of the cases.

For more information, see *Nonparametrics*

Quartiles. The lower and upper quartiles (this term was first used by Galton, 1882; also referred to as the .25 and .75 quantiles) are the 25th and 75th percentiles of the distribution (respectively). The 25th percentile of a variable is a value such that 25% of the values of the variable fall below that value.

Similarly, the 75th percentile is a value such that 75% of the values of the variable fall below that value and is calculated accordingly.

Quasi-Newton Method (in Neural Networks). Quasi-Newton (Bishop, 1995; Shepherd, 1997) is an advanced method of training multilayer perceptrons. It usually performs significantly better than Back Propagation, and can be used wherever back propagation can be. It is the recommended technique for most networks with a small number of weights (less than a couple of hundred). If the network is a single output regression network and the problem has low residuals, then Levenberg-Marquardt may perform better.

Quasi-Newton is a batch update algorithm: whereas back propagation adjusts the network weights after each case, Quasi-Newton works out the average gradient of the error surface across all cases before updating the weights once at the end of the epoch.

For this reason, there is no shuffle option available with Quasi-Newton, since it would clearly serve no useful function. There is also no need to select learning or momentum rates for Quasi-Newton, so it can be much easier to use than back propagation. Additive noise would destroy the assumptions made by Quasi-Newton about the shape of search space, and so is also not available.

Quasi-Newton works by exploiting the observation that, on a quadratic (i.e. parabolic) error surface, one can step directly to the minimum using the Newton step - a calculation involving the Hessian matrix (the matrix of second partial derivatives of the error surface). Any error surface is approximately quadratic "close to" a minimum. Since, unfortunately, the Hessian matrix is difficult and expensive to calculate, and anyway the Newton step is likely to be wrong on a non-quadratic surface, Quasi-Newton iteratively builds up an approximation to the inverse Hessian. The approximation at first follows the line of steepest descent, and later follows the estimated Hessian more closely.

Quasi-Newton is the most popular algorithm in nonlinear optimization, with a reputation for fast convergence. It does, however, has some drawbacks - it is rather less numerically stable than, say, Conjugate Gradient Descent, it may be inclined to converge to local minima, and the memory requirements are proportional to the square of the number of weights in the network.

It is often beneficial to precede Quasi-Newton training with a short burst of Back Propagation (say 100 epochs), to cut down on problems with local minima.

If the network has many weights, you are advised to use Conjugate Gradient Descent instead. Conjugate Gradient Descent has memory requirements proportional only to the number of weights, not the square of the number of weights, and the training time is usually comparable with Quasi-Newton, if somewhat slower.

Technical Details. Quasi-Newton is batch-based; it calculates the error gradient as the sum of the error gradients on each training case.

It maintains an approximation to the inverse Hessian matrix, called *H* below. The direction of steepest descent is called *g* below. The weight vector on the ith epoch is referred to as *fi* below. *H* is initialized to the identity matrix, so that the first step is in the direction *g* (i.e. the same direction as that chosen by Back Propagation). On each epoch, a back tracking line search is performed in the direction:

d = – Hg

Subsequently, the search direction is updated using the BFGS (Broyden-Fletcher-Goldfarb-Shanno) formula:

This is "guaranteed" to maintain a positive-definite approximation (i.e. it will always indicate a descent direction), and to converge to the true inverse Hessian in W steps, where W is the number of weights, on a quadratic error surface. In practice, numerical errors may violate these theoretical guarantees and lead to divergence of weights or other modes of failure. In this case, run the algorithm again, or choose a different training algorithm.

QUEST. *QUEST* is a classification tree program developed by Loh and Shih (1997). For discussion of the differences of *QUEST* from other classification tree programs, see A Brief Comparison of Classification Tree Programs.

Quota Sampling. Quota sampling usually refers to the process whereby a researcher attempts to match in a sample the exact makeup of the population with regard to certain demographic characteristics deemed important (such as gender, age, race, income, etc.). For example, a researcher may strive to draw a sample from a population so that the sample consists of exactly 50% males and 50% females, certain percentages of persons from particular ethnic backgrounds, etc. The purpose of this practice usually is to achieve some kind of representative sample of the underlying population.

In general, only properly drawn probability samples such as EPSEM samples will guarantee that the population to which one wishes to generalize is properly "represented." Refer to, for example, Kish (1965) for a detailed discussion of the advantages and characteristics of probability samples (see also Representative Sample, Stratified Random Sampling, Probability Sampling).