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2011
Introduction to categorical data analysis for statistics.
Alan Agresti, Maria Kateri
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Introduction to categorical data analysis for statistics.
Alan Agresti, Maria Kateri
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2014
Categorical variables may have categories which are naturally ordered called ordinal variables or those that have no natural order called nominal variables. For example, the variable “weight” with categories “small,” “medium,” and “big” is an ordinal variable, so also is the attitudinal variable with categories “agree,” “neutral,” and “disagree.” On ...
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Categorical variables may have categories which are naturally ordered called ordinal variables or those that have no natural order called nominal variables. For example, the variable “weight” with categories “small,” “medium,” and “big” is an ordinal variable, so also is the attitudinal variable with categories “agree,” “neutral,” and “disagree.” On ...
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2012
In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous.
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In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous.
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1995
In this chapter we will mainly be concerned with random variables whose outcomes are not ordinary numbers, but elements of several possible categories, classes or groups. The hair color or the eye color of a newborn baby are typical examples. A common problem is the question, whether there is a relationship between these categorical outcomes, for ...
Michael Falk +2 more
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In this chapter we will mainly be concerned with random variables whose outcomes are not ordinary numbers, but elements of several possible categories, classes or groups. The hair color or the eye color of a newborn baby are typical examples. A common problem is the question, whether there is a relationship between these categorical outcomes, for ...
Michael Falk +2 more
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Jackknifing in Categorical Data Analysis
Australian Journal of Statistics, 1982SummaryEstimation of nonlinear functions of a multinomial parameter vector is necessary in many categorical data problems. The first and second order jackknife are explored for the purpose of reduction of bias. The second order jackknife of a function g(.) of a multinomial parameter is shown to be asymptotically normal if all second order partials ∂2g ...
Parr, William C., Tolley, H. Dennis
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dynXcube – Categorizing dynamic data analysis
Information Sciences, 2018Abstract Data analysis has gained strategic importance for virtually any organization. It covers areas like business analytics, big data, business intelligence, and data mining, among others. The past decades have also witnessed increasing efforts to capture, analyze, and interpret dynamic data instead of just static snapshot data. This is due to the
Georg Peters, Richard Weber
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2017
The general linear model, which incorporates statistical analyses, such as ordinary least squares regression, t‐test, and analysis of variance, is based on a series of assumptions about the independent variables, the dependent variable, and the error terms.
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The general linear model, which incorporates statistical analyses, such as ordinary least squares regression, t‐test, and analysis of variance, is based on a series of assumptions about the independent variables, the dependent variable, and the error terms.
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