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Alan Gilbert Agresti (born February 6, 1947) is an American statistician and Distinguished Professor Emeritus at the University of Florida. [1] He has written several textbooks on categorical data analysis that are considered seminal in the field.
Categorical distribution, general model; Chi-squared test; Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder–Richardson Formula 20; Linear ...
The Cochran–Armitage test for trend, [1] [2] named for William Cochran and Peter Armitage, is used in categorical data analysis when the aim is to assess for the presence of an association between a variable with two categories and an ordinal variable with k categories.
A teapot, a creamer and teacup full of tea with milk—can a taster tell if the milk went in first? The test is useful for categorical data that result from classifying objects in two different ways; it is used to examine the significance of the association (contingency) between the two kinds of classification. So in Fisher's original example ...
Outline of regression analysis; Index of statistics articles; List of scientific method topics; List of analyses of categorical data; List of fields of application of statistics; List of graphical methods; List of statistical software. Comparison of statistical packages; List of graphing software; Comparison of Gaussian process software
function draw_categorical(n) // where n is the number of samples to draw from the categorical distribution r = 1 s = 0 for i from 1 to k // where k is the number of categories v = draw from a binomial(n, p[i] / r) distribution // where p[i] is the probability of category i for j from 1 to v z[s++] = i // where z is an array in which the results ...
Simpson's paradox for quantitative data: a positive trend ( , ) appears for two separate groups, whereas a negative trend ( ) appears when the groups are combined. Visualization of Simpson's paradox on data resembling real-world variability indicates that risk of misjudgment of true causal relationship can be hard to spot.
Download as PDF; Printable version; In other projects Wikimedia Commons; ... Summary statistics for categorical data (1 C, 6 P) V. Categorical variable interactions ...