{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

011+Categorical+data

# 011+Categorical+data - A Zhensykbaev Categorical data...

This preview shows pages 1–3. Sign up to view the full content.

A. Zhensykbaev Categorical data analysis Categorical data analysis. The binomial probability distribution analyzes the variables in one of two responses (success or failure). Quantitative variables often allow more than two categories for a response (for example, levels of education). Quantitative data that fall in more more than two categories often result from a multino- mial experiment. Below we characterize it. Multinomial probability distribution. Properties of the multinomial experiment. 1. The experiment consists of n identical trials. 2. There are k possible outcomes to each trial. 3. The probabilities of the k outcomes, p 1 , ..., p k , remain the same from trial to trial, and p 1 + · · · + p k = 1 . 4. The trials are independent. The number of observations that fall in each of the k classes denote as n 1 , ..., n k and n 1 + · · · + n k =: n. One-way Table. The multinomial probability distribution is P ( x ) = n ! n 1 ! ...n k ! p n 1 1 ...p n k k . (this is the probability to obtain in n trials n 1 times event x 1 = 1 ,..., n k times event x k = k ). In particular, if k = 2 we have a binomial distribution. Expected values of x = ( x 1 , ..., x k ) are E ( x 1 ) = np 1 , . . . , E ( x k ) = np k . We want to make inferences about the true proportions that occur in the k categories based on the sample information in the one-way table. Test of a hypothesis about multinomial probabilities. 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
A. Zhensykbaev Categorical data analysis We assume that the sample size n is large (this is satisfied if for every i the expected value E ( x i ) will be not less than 5.
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}