Course Contents
Introduction, Describing two way contingency tables, Inference for two way contingency tables, models for binary response variables, Log linear model, fitting Log linear and Logit models, building and applying Log linear models, Log linear, Probit and Logit models, Multinomial response models for matched pairs, analyzing repeated categorical response data, Logistic regression models and their analysis.
Course Synopsis
To analyze in descriptive and inferential perspective, the contingency tables. To familiarize with generalized linear models and their application.
Course Learning Outcomes
• To develop students' abilities in understanding and solving practical statistical problems involving categorical data
• To present theory and techniques for the analysis of categorical data
• To enable students to learn how to choose appropriate techniques, to analyze categorical data, and present results
• To analyze in descriptive and inferential perspective, the contingency tables. To familiarize generalized linear models and their application.
Introduction to Ordinal Logistic Regression & Proportional Odds Assumption by Gopal Prasad Malakar
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Ordinal logistic regression using SPSS (July, 2019) by Mike Crowson
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Introduction to MultiNomial Logistic Regression (Outcome more than two class) & Solution Approach by Gopal Prasad Malakar
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Multinomial logistic regression using SPSS (July, 2019) by Mike Crowson
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Interpreting Odds Ratio for Multinomial Logistic Regression using SPSS - Nominal and Scale Variables by Dr. Todd Grande
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LECTURE 1-Ch(1)
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LECTURE 2-Ch(1)
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LECTURE 3-Ch(2)
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LECTURE 4-Ch(3)
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LECTURE 5-Ch(4)
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LECTURE 6-Ch(4)
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LECTURE 7-Ch(5)
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LECTURE 8-Ch(6)
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LECTURE 9-Ch(8)
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LECTURE 10- Ch(9)
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LECTURE 11-Ch(9)
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LECTURE 12-Ch(11)
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LECTURE 13-Ch(11)
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Book Title : Categorical Data Analysis
Author : Agresti, Alen
Edition : 2nd
Publisher : John Wiley and Sons.
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Book Title : Categorical Data Analysis
Author : Agresti, Alen
Edition : 3rd
Publisher : Wiley Interscience
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