Variables (in design of experiments): Many statistical methods rest on a statistical model which states a relationship Y = f(X1,..,XN) between a dependent variable (Y) and independent variable(s) X1,...,XN. In designed experiments, the dependent variable is often named "response", independent variables manipulated by the experimenter "factors", independent variables not manipulated...
View Full DescriptionZ score: An observation's z-score tells you the number of standard deviations it lies away from the population mean (and in which direction). The calculation is as follows: z = x - m s , where x is the observation itself, m is the mean of the distribution, s...
View Full DescriptionAverage Deviation: The average deviation or the average absolute deviation is a measure of dispersion. It is the average of absolute deviations of the individual values from the median or from the mean. Browse Other Glossary Entries
View Full DescriptionBox Plot: A box plot is a graph that characterizes the pattern of variation of the data. The plot simultaneously displays several measures of central tendency and dispersion of the data at hand. The box plot provides the following information: (1) the position of the median; (2) the 25th and...
View Full DescriptionStatistical Glossary Chernoff Faces: Chernoff faces are a category of icon plots . Each unit is represented as a schematic face. Variables of interest are represented by particular parameters of the face, e.g. the nose size, eye-to-eye distance, etc. Browse Other Glossary Entries
View Full DescriptionCircular Icon Plots: Circular icon plots are a category of icon plots . Each variable is represented by a ray or direction; all rays start in the center. The value of each variable is reflected as the distance from the center. The most common categories of circular icon plots are...
View Full DescriptionCoefficient of variation: The coefficient of variation is the standard deviation of a data set, divided by the mean of the same data set. Browse Other Glossary Entries
View Full DescriptionStatistical Glossary Column icon plots: See sequential icon plots . Browse Other Glossary Entries
View Full DescriptionCorrelation Coefficient: The correlation coefficient indicates the degree of linear relationship between two variables. The correlation coefficient always lies between -1 and +1. -1 indicates perfect linear negative relationship between two variables, +1 indicates perfect positive linear relationship and 0 indicates lack of any linear relationship. The correlation coefficient (also...
View Full DescriptionCorrelation Matrix: A Correlation matrix describes correlation among M variables. It is a square symmetrical MxM matrix with the (ij)th element equal to the correlation coefficient r_ij between the (i)th and the (j)th variable. The diagonal elements (correlations of variables with themselves) are always equal to 1.00. Many methods of...
View Full DescriptionCorrespondence Plot: A correspondence plot represents the results of correspondence analysis (CA). For each category (possible value of a variable), its scores derived by CA for the first two dimensions are depicted as a point on the x-y plane. An interesting feature of the correspondence plot is that two variables,...
View Full DescriptionCovariance: The covariance between two random variables X and Y is the expected value of the product of the variables´ deviations from their means. If there is a high probability that large values of X go with large values of Y and small values of X go with small values...
View Full DescriptionDescriptive Statistics: Descriptive statistics refers to statistical techniques used to summarize and describe a data set, and also to the statistics (measures) used in such summaries. Measures of central tendency (e.g. mean, median) and variation (e.g. range, standard deviation) are the main descriptive statistics. Displays of data such as histograms...
View Full DescriptionGeometric mean: The geometric mean of n values is determined by multiplying all n values together, then taking the nth root of the product. It is useful in taking averages of ratios. The geometric mean is often used for data which take only on positive values and the values can...
View Full DescriptionGeometric Mean and Mean (comparison): The quantitative distinction between the geometric mean and the mean can be illustrated by the following table: Data set Mean Geometric Mean 1, 1, 1 1 1 1, 2, 3 2 1.6 1, 2, 1000 334 6.7 The analytical relation between the mean (M) and...
View Full DescriptionGini coefficient: The Gini coefficient is used in economics to measure income inequality. Generally speaking, it is used to measure the extent of departure from a perfectly even distribution of income. A "0" indicates no departure, i.e. everyone has the same income. A "1" indicates complete departure - all income...
View Full DescriptionGini´s Mean Difference: Gini´s mean difference is a descriptive statistic , a measure of variation. For a sample of N values the Gini´s mean difference is the average of all pairwise absolute differences: GMD = 1 N(N-1) ? ij |xi-xj|; i,j = 1,...,N; i ? j . Browse Other...
View Full DescriptionHotelling Trace Coefficient: The Hotelling Trace coefficient (also called Lawley-Hotelling or Hotelling-Lawley Trace) is a statistic for a multivariate test of mean differences between two groups. The null hypothesis is that centroid s don´t differ between two groups. The coefficient is equal to Hotelling´s T-Square divided by (N-m), where N...
View Full DescriptionHotelling-Lawley Trace: See Hotelling Trace coefficient . Browse Other Glossary Entries
View Full DescriptionStatistical Glossary Icon Plots: Icon plots are graphical tools for multivariate analysis. They provide graphical representation of observed units described by many variables. Each unit or observation is represented by a small image which depends on the values of the variables of interest. Icon plots rely on the ability of...
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