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Univariate Descriptive Statistics  Ungrouped Data 
 
Plot & describe dataseries  generates a simple plot of the dataseries and allows one to enter a description (useful for future reference when the computation is submitted to the archive)

 
Central Tendency  arithmetic mean, geometric mean, harmonic mean, median, midrange, midmean, robustness of central tendency (winsorized and trimmed mean), etc...

 
Variability  range, variance, standard deviation, variation, MSE, absolute deviation, interquartile difference, coefficient of quartile variation, Gini's mean difference, Leik's D, dispersion, diversity, qualitative variation, mean square deviation, etc...

 
Concentration (old)  entropy, exponential index, Herfindahl, variation coefficient, Gini coefficient, Lorenz curve, etc...

 
Concentration (new)  entropy, exponential index, Herfindahl, variation coefficient, Gini coefficient, Lorenz curve, etc...

 
Moments (old)  general, non centered & centered moments, trimmed moments

 
Moments (new)  general, non centered & centered moments, trimmed moments

 
Skewness/Kurtosis  Fisher 3rd centered moment, Fisher beta 1 & gamma 1, Pearson, Yule's skewness (according to 8 different quartile definitions), Beta, Gamma, small sample skewness, Fisher 4th centered moment, Fisher beta 2 & gamma 2, small sample kurtosis, etc...

 
Quartiles  weighted average, empirical distribution, closest observation, TrueBasic(TM), Excel(TM), etc...

 
Percentiles  normal probability plot, weighted average, empirical distribution, closest observation, TrueBasic(TM), Excel(TM), etc...

 
Histogram  computes the histogram (and frequency table) for a univariate data series

 
Kernel Density Estimation  computes and plots the density trace of a data series for different Kernels: Gaussian, Epanechnikov, Rectangular, Triangular, Biweight, Cosine, and Optcosine

 
HarrellDavis Quantiles  computes the HarrellDavis Quantiles and associated standard errors.

 
Stemandleaf  Computes the Stemandleaf plot of a dataseries

 
Univariate EDA  computes a series of graphical tools for the purpose of Explorative Data Analysis

 
Bootstrap Plot  Central Tendency  computes the Bootstrap Plot for three measures of Central Tendency

 
Blocked Bootstrap Plot  Central Tendency  computes the Blocked Bootstrap Plot for three measures of Central Tendency (for stationary time series)

 
Mean Plot  computes the Mean Plot for a (time) series

 
Standard Deviation Plot  computes the Standard Deviation Plot for a (time) series

 
(Partial) Autocorrelation Function  computes the autocorrelation and partial autocorrelation function for any univariate time series

 
Variance Reduction Matrix  computes the Variance Reduction Matrix that can be used to determine which combination of seasonal and nonseasonal differencing.

 
Standard DeviationMean Plot  computes the Standard DeviationMean Plot and the Range Mean Plot

 
Spectral Analysis  computes the raw periodogram and the cumulative periodogram of a univariate time series (with the 95% KolmogorovSmirnov confidence intervals)

 
Mean versus Median  computes the arithmetic mean and the median of a univariate dataset. It displays the Kernel Density Plot and the HarrellDavis quantiles with an indication of the arithmetic mean and the median.

 
Univariate Summary Statistics  computes descriptive, summarizing statistics for any univariate data series.

 

Bivariate Descriptive Statistics  Ungrouped Data 
 
Plot & describe dataseries  plots a bivariate data series

 
Correlation  Pearson correlation, covariance, determination coefficient, scatter plot, etc...

 
Spearman Rank Correlation (old version)  Spearman Rank Order Correlation (corrected and noncorrected).

 
Spearman Rank Correlation (new version)  computes the Spearman Rank Correlation between two data series with the R language

 
Simple Regression  general linear model, mean and variances, covariance, correlation, least squares estimation, parameters, response, significance, determination coefficient, ANOVA, residuals, autocorrelation, model selection, model performance, etc...

 
Bivariate Density  computes Bivariate Kernel Density Estimates

 
Kendall Rank Correlation  computes the Kendall tau Rank Correlation between two data series

 
BoxCox Linearity Plot  computes the BoxCox Linearity Plot

 
Linear Regression Graphical Model Validation  computes the Simple Linear Regression model (Y = a + b X) and various diagnostic tools from the perspective of Explorative Data Analysis

 
Back to Back Histogram  computes the Back to Back Histogram (sometimes called Bihistogram) for a bivariate dataset

 
QQ Plots  computes various QQ Plots and Histograms for a bivariate dataset

 
Cross Correlation Function  computes the Cross Correlation Function for any univariate time series

 
Somers Dxy Rank Correlation  computes the Somers Dxy Rank Correlation

 
Bagplot  computes the Bagplot for a bivariate data set

 
Bivariate EDA  computes a series of graphical tools for the purpose of Explorative Data Analysis

 

Trivariate Descriptive Statistics  Ungrouped Data 
 
Partial Correlation  Pearson Product Moment Partial Correlation.

 
Trivariate Scatterplots  computes 3dimensional scatterplots, combinations of 2by2 scatterplots, histograms, bivariate density plots

 

Multivariate Descriptive Statistics  Ungrouped Data 
 
Kendall tau Correlation Matrix  computes the (multivariate) correlation plot based on Kendall tau rank correlations

 
Notched Boxplots  computes notched boxplots for every variable of the multivariate dataset

 
Star Plot  computes the Star Plot for a multivariate dataset

 
Agglomerative Nesting (Hierarchical Clustering)  computes the agglomerative hierarchical clustering of a multivariate dataset (Kaufman and Rousseeuw)

 
Hierarchical Clustering  computes the hierarchical clustering of a multivariate dataset based on dissimilarities.

 
Survey Scores  computes various numerical scores for a matrix containing results of a survey with questions that are measured on a Likert scale.

 
Cronbach Alpha  computes Cronbach Alpha and related statistics for items that belong to a construct.

 