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Unformatted text preview: Categorical Data Analysis  Lei Sun 1 CHL 5210  Statistical Analysis of Qualitative Data Topic: Introduction Outline • Logistic and administrative work. • Some notes on Categorical data. • An example: the low birth weight study. • Structure of the course. • Some key distributions for categorical variables (Binomial, Multinomial and Poisson). • A brief summary of classical statistical inference (estimation, hypothesis testing and confidence interval). Categorical Data Analysis  Lei Sun 2 Logistic and administrative work. • Course web site. – http://www.utstat.toronto.edu/sun – news and announcements. • Time and location. – Mondays: 101pm. – HSB 790. • Instructors. – Lei Sun ([email protected], 4169787519). – Laurent Briollais ([email protected]) • Office hour: the last hour or half hour of the class. • The course has no teaching assistant. • Registration. – All students must register. (A rule of the graduate student office). – September 26: final date to enroll; October 31: final date to withdraw. • Prerequisites. – Statistics at the graduate level or consent of instructor. – Working knowledge of SAS, R or other equivalent software packages is necessary. Categorical Data Analysis  Lei Sun 3 • Format of instruction: lecutures (focus on some mathematical statistics details, not just run software packages). • Evaluation: roughly based on homework problem sets (30%), midterm (30%), term project (30%) and overall participation (10%). • Text book: Alan Agresti (2002). Categorical Data Analysis. Second edition. Wiley. • Others. – Please email the instructor at [email protected] with your: name, department, program (i.e.. MSc, PHD) and research interest (optional). – Suggestions/Comments: topics, formats, future courses, etc. Categorical Data Analysis  Lei Sun 4 A Few Notes on Categorical Data. • NominalOrdinal scale distinction. – Nominal: variables having categories without a natural ordering, e.g. types of food (Chinese, French, Italian). – Ordinal: having ordered categories but distances between categories are unknown, e.g. social class (lower, middle, upper). – Interval variable: having numerical distances between any two val ues, e.g. annual income. • A variable may be classified differently, depending on its measure, e.g. education: – Nominal: public or private school. – Ordinal: high school, bachelor’s, master’s or doctorate degree. – Interval: the number of years of education. • Scale determines the appropriate statistical methods. – Increasing measurement level: nominal, ordinal, interval. – Methods used at one level can be used at higher level but not at lower level....
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This note was uploaded on 02/22/2012 for the course CHL 5210H taught by Professor Leisun during the Fall '11 term at University of Toronto Toronto.
 Fall '11
 LeiSun

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