Course in Categorical Data Analysis
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€58.99
A01=Thomas Leonard
Alcohol Abuse Study
Author_Thomas Leonard
categorical data analysis
categorical data statistical inference
Category=GPH
Category=PBT
Cell Frequencies
Conditional Independence Model
contingency table analysis
Cross-product Ratio
Cumulative Distribution Function
Data Sets
Discrimination Function
eq_isMigrated=1
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
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
Product Multinomial Model
Residual Deviance
Row Totals
Significance Probability
Simpson's Paradox
Simpson’s Paradox
teaching categorical analysis
University Of Wisconsin
Product details
- ISBN 9781584881803
- Weight: 380g
- Dimensions: 156 x 234mm
- Publication Date: 22 Nov 1999
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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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.
