#
Etymologie, Etimología, Étymologie, Etimologia, Etymology

US Vereinigte Staaten von Amerika, Estados Unidos de América, États-Unis d'Amérique, Stati Uniti d'America, United States of America

Statistik, Estadística, Statistique, Statistica, Statistics

## A

### jeff560

Long Words

Long Place Names

Long Chemical Names

(E?)(L1) http://jeff560.tripod.com/words11.html

(E?)(L1) http://jeff560.tripod.com/words12.html

(E?)(L1) http://jeff560.tripod.com/words14.html

## B

## C

### census

U.S. Census Bureau

(E?)(L?) http://www.census.gov/

- People & Households: 2010 Census | 2000 Census · American Community Survey · Estimates · Projections · Housing · Income | State Median Income · Poverty · Health Insurance · International · Genealogy · More
- Business & Industry: Economic Census · Get Help with Your Form · Economic Indicators · NAICS · Survey of Business Owners · Government · E-Stats · Foreign Trade | Export Codes · Local Employment Dynamics · More
- Geography: Maps · TIGER · Gazetteer · More
- Newsroom: Releases · Facts for Features · Product Schedule · Multimedia Gallery · Embargoed Releases · More
- Special Topics: Fraudulent Activity & Scams · Census Bureau Data and Emergency Preparedness · Events Calendar · Census In Schools · Training · Statistical Abstract · FedStats · USA.gov · Recovery Act at the Census Bureau

(E?)(L?) http://factfinder2.census.gov/

Your source for population, housing, economic, and geographic data

(E?)(L?) http://factfinder.census.gov/home/en/epss/glossary_a.html

Census Glossary

(E?)(L?) http://www.census.gov/main/www/popclock.html

U.S. & World Population Clocks

Erstellt: 2011-10

## D

## E

## F

### flowingdata.com

Datenbildner

Statistische Auswertungen in ungewöhnlicher Darstellung

(E?)(L?) http://www.flowingdata.com/

(E?)(L?) http://book.flowingdata.com/

About

A book by Nathan Yau who writes for FlowingData, Visualize This is a practical guide on visualization and how to approach real-world data. The book is published by Wiley and is available on Amazon and other major online booksellers.

...

Erstellt: 2013-01

## G

### google

Etymology-Search-Trend

(E?)(L?) http://www.google.com/trends

(E?)(L?) http://www.google.com/trends?q=Etymology

With Google Trends, you can compare the world's interest in your favorite topics. Enter up to five topics and see how often they've been searched for on Google over time. Google Trends also displays how frequently your topics have appeared in Google News stories, and which geographic regions have searched for them most often.

## H

## I

## J

## K

## L

## M

## N

## O

## P

### PQI (W3)

"PQI" steht für "Predictive Quantities Indicator".

(E?)(L?) http://www.languagemonitor.com/pqi

(E?)(L?) http://www.languagemonitor.com/about

...

The "Predictive Quantities Indicator" ("PQI")

The Global Language Monitor’s proprietary algorithm, the "Predictive Quantities Indicator" tracks the frequency of words and phrases in the global print and electronic media, on the Internet, throughout the Blogosphere, as well as accessing proprietary databases (Factiva, Lexis-Nexis, etc.).

The "PQI" was created by Paul JJ Payack, Rico Blaser (our Quant), and Peter Payack, poet laureate of Cambridge, Massachusetts.

Once a keyword base index is created (including selected keywords, phrases, ‘excluders’ and ‘penumbra’ words), ‘timestamps’ and a ‘media universe’ are determined.

The "PQI" is a weighted Index, factoring in: Long-term trends, Short-term changes, Momentum, and Velocity. As such it can create ’signals’ that can be used in a variety of applications.

Outputs include: the raw "PQI", a Directional Signal, or a Relative Ranking with 100 as the base.

There are two differing "PQI"s. When analyzing words and phrases in political contexts, GLM uses the "Political-sensitivity Quotient Index"; when analyzing words and phrases in any other context, GLM uses a slightly different "Predictive Quantities Indicator".

...

## Q

## R

## S

### statsoft

Electronic Statistics Textbook

(E?)(L?) http://www.statsoft.com/textbook/distribution-tables/

Elementary Concepts | Statistics Glossary | Basic Statistics | ANOVA / MANOVA | Association Rules | Boosting Trees | Canonical Analysis | CHAID Analysis | C & R Trees | Classification Trees | Cluster Analysis | Correspondence Analysis | Data Mining Techniques | Discriminant Analysis | Distribution Fitting | Experimental Design | Factor Analysis | General Discrim. Analysis | General Linear Models | Generalized Additive Mod. | Generalized Linear Mod. | General Regression Mod. | Graphical Techniques | Ind.Components Analysis | Linear Regression | Log-Linear Analysis | MARSplines | Machine Learning | Multidimensional Scaling | Neural Networks | Nonlinear Estimation | Nonparametric Statistics | Partial Least Squares | Power Analysis | Process Analysis | Quality Control Charts | Reliability / Item Analysis | SEPATH (Structural eq.) | Survival Analysis | Text Mining | Time Series / Forecasting | Variance Components | Statistical Advisor | Distribution Tables | References Cited

(E?)(L?) http://www.statsoft.com/textbook/statistics-glossary/

Statistics Glossary

Entries in the Statistics Glossary are taken from the Electronic Manual of STATISTICA and may contain elements that refer to specific features of the STATISTICA system.

- 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: Jacobian Matrix Jogging Weights Johnson Curves Join Joining Networks (in Neural Networks) JPEG JPG
- K: Kendall Tau Kernel Functions k-Means Algorithm (in Neural Networks) k-Nearest Algorithm Kohonen Algorithm (in Neural Networks) Kohonen Networks Kohonen Training Kolmogorov-Smirnov Test Kronecker Product Kruskal-Wallis Test Kurtosis
- 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: Quadratic Quality Quality Control Quantiles Quantile-Quantile Plots Quantile-Quantile Plots - Categorized Quartile Range Quartiles Quasi-Newton Method QUEST Quota Sampling
- 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: Unconfounding, Maximum Unequal N HSD Uniform Distribution Unimodal Distribution Unit Penalty Unit Types (in Neural Networks) Unsupervised and Supervised Learning Unsupervised Learning (in Neural Networks) Unweighted Means
- V: Variance Variance Components in Mixed Model ANOVA Variance Inflation Factor (VIF) V-fold Cross-validation Vintage Analysis Voronoi Voronoi Scatterplot Voting
- W: Wald Statistic Warehousing, Data Weibull Distribution Weigend Weight Reg. (in Neural Networks) Weighted Least Squares Wilcoxon test Wilson-Hilferty Transformation Win Frequencies (in Neural Networks) Wire
- X: X11 Output XML (Extensible Markup Language)
- Y: Yates Corrected Chi-square
- Z: Z Distribution (Standard Normal)

Erstellt: 2011-10

## T

### textarc

Statistik von Texten

(E?)(L2) http://www.textarc.org/

Diese Site stellt das Vorkommen von Worten und Personen in einem Text graphisch dar. Die Funktion wird am Beispiel von Lewis Carrolls "Alice im Wunderland" dargestellt. (Achtung: längere Ladezeiten)

## U

## V

## W

### wolframalpha

Socioeconomic Data

(E?)(L1) http://www.wolframalpha.com/examples/SocioeconomicData.html

Demographics
- get demographic information about a country Russia demographics
- do computations with demographic statistics Japan female population / male population
- do computations with demographic statistics France pop 2000 - France pop 1980
- do computations with demographic statistics population of India in 2030
- do computations with city properties population Brussels / Paris
- get socioeconomic data for a ZIP code ZIP 88201 Native American population

Economic Data
- get economic data for the US US M1 money supply
- get salary data for a given occupation intercity bus driver salary
- get unemployment data for a US state unemployment rate North Dakota
- get energy price data price of electricity in Illinois
- get home value data median home value Madison, WI

Social Statistics
- get statistics on a specified type of crime homicide Phoenix, Austin, NYC
- determine facts about military forces Japan military strength
- get information about a religion Buddhism
- compare health indicators of countries USA vs Cuba medical personnel

Erstellt: 2011-10

### wolframalpha

Statistics & Data Analysis

(E?)(L1) http://www.wolframalpha.com/examples/Statistics.html

Descriptive Statistics
- calculate basic descriptive statistics for a data set {25, 35, 10, 17, 29, 14, 21, 31}
- compute a statistical quantity mean {21.3, 38.4, 12.7, 41.6}
- compute a statistical quantity kurtosis {21.3, 38.4, 12.7, 41.6}

Regression Analysis
- fit a line to two-dimensional data linear fit {1.3, 2.2},{2.1, 5.8},{3.7, 10.2},{4.2, 11.8}
- fit a polynomial to given data cubic fit 20.9,23.2,26.2,26.4,16.3,-12.2,-60.6,-128.9
- fit an exponential model to given data exponential fit 0.783,0.552,0.383,0.245,0.165,0.097

Statistical Distributions
- compute properties of a probability distribution beta distribution
- compute properties of a probability distribution Poisson distribution
- specify parameters for a distribution normal distribution, mean=0, sd=2

Erstellt: 2011-10

### wordfrequency.info

Wordfrequency.info

(E?)(L?) http://www.wordfrequency.info/

This site contains what we believe is the most accurate frequency data of English, and it comes in a number of different formats (see samples: 100,000 and 60,000 word lists, and a comparison of the two lists).

For the 5,000-60,000 word lists, you can download a simple word list, frequency by genre, or as an eBook or a printed frequency dictionary. For the 100,000 word list, you can see detailed frequency information for many genres in several different corpora. In addition to word frequency data, you can also download up to 155 million n-grams, and 4.3 million collocates.

Any frequency list is only as good as the corpus (collection of texts) that it is based on. The 5,000-60,000 word lists are based on the only large, genre-balanced, up-to-date corpus of American English -- the 450 million word Corpus of Contemporary American English (COCA). The 100,000 word list supplements this COCA data with detailed frequency data from the 400 million word Corpus of Historical American English, the British National Corpus, and the Corpus of American Soap Operas (for very informal language).

Erstellt: 2014-09

## X

## Y

## Z

# Bücher zur Kategorie:

Etymologie, Etimología, Étymologie, Etimologia, Etymology

US Vereinigte Staaten von Amerika, Estados Unidos de América, États-Unis d'Amérique, Stati Uniti d'America, United States of America

Statistik, Estadística, Statistique, Statistica, Statistics

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### Schur, Norman W.

1000 Most Important Words

(E?)(L1) http://www.amazon.ca/exec/obidos/ASIN/0345298632/etymologporta-20

(E?)(L1) http://www.amazon.de/exec/obidos/ASIN/0345298632/etymologety0f-21

(E?)(L1) http://www.amazon.fr/exec/obidos/ASIN/0345298632/etymologetymo-21

(E?)(L1) http://www.amazon.co.uk/exec/obidos/ASIN/0345298632/etymologety0d-21

(E?)(L1) http://www.amazon.com/exec/obidos/ASIN/0345298632/etymologpor09-20

Mass Market Paperback: 256 pages

Publisher: Ballantine Books (July 12, 1982)

Book Description

Based on the contention that we do not utillize speech to its fullest extent, this guide is an essential aide to unlocking our "passive" vocabularies and developing a keener appreciation of the richness of language.

### Schur, Norman W.

2000 Most Challenging and Obscure Words

(E?)(L1) http://www.amazon.ca/exec/obidos/ASIN/0883658488/etymologporta-20

(E?)(L1) http://www.amazon.de/exec/obidos/ASIN/0883658488/etymologety0f-21

(E?)(L1) http://www.amazon.fr/exec/obidos/ASIN/0883658488/etymologetymo-21

(E?)(L1) http://www.amazon.co.uk/exec/obidos/ASIN/0883658488/etymologety0d-21

(E?)(L1) http://www.amazon.com/exec/obidos/ASIN/0883658488/etymologpor09-20

Hardcover: 576 pages

Publisher: Galahad Books (April 1994)

Language: English

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The book is split up into two sections, the first section is the 1000 most challenging words, the second part is 1000 most obscure words.

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Lots of etymology, and very few words that aren't genuinely obscure.

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