A01=Thomas Leonard
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Age Group_Uncategorized
Alcohol Abuse Study
Author_Thomas Leonard
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categorical data analysis
categorical data statistical inference
Category1=Non-Fiction
Category=PBT
Cell Frequencies
Conditional Independence Model
contingency table analysis
COP=United Kingdom
Cross-product Ratio
Cumulative Distribution Function
Data Sets
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Discrimination Function
eq_isMigrated=2
eq_nobargain
Exposure Time
Fisher exact test
Fisher's Exact Test
Fisher’s Exact Test
Full Rank Model
Ij AB
Joint Probability Mass Function
Language_English
Likelihood Ratio Statistic
Linear Logistic Model
linear regression
log-linear modeling
logistic regression methods
Main Effects Model
Maximum Likelihood
medical statistics applications
Multinomial Distribution
multinomial models
Multiple Sclerosis Data
Nonhomogeneous Poisson Process
Normal Test Statistics
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Price_€100 and above
Product Multinomial Model
PS=Active
Residual Deviance
Row Totals
Significance Probability
Simpson's Paradox
Simpson’s Paradox
softlaunch
teaching categorical analysis
University Of Wisconsin
Product details
- ISBN 9781138469617
- Weight: 540g
- Dimensions: 156 x 234mm
- Publication Date: 30 Sep 2020
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
- Language: English
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Categorical data-comprising counts of individuals, objects, or entities in different categories-emerge frequently from many areas of study, including medicine, sociology, geology, and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore, the ability to manipulate, understand, and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines.
Although t-tests, linear regression, and analysis of variance are useful, valid methods for analysis of measurement data, categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students, A Course in Categorical Data Analysis presents the easiest, most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet, readers do not need full knowledge of a statistical software package.
In this unique text, the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data, but on using different models that may lead to meaningful conclusions. The book offers some simple, innovative techniques not highighted in other texts that help make the book accessible to a broad, interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific, medical, or real-life conclusions from categorical data sets.
Leonard, Thomas
