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The multinomial (aka polytomous) logistic regression is a simple extension of binomial logistic regression model. They are used when the dependent variable is greater than two (disordered) has rated categories.
Dummy coding of independent variables is quite common. In multinomial logistic regression, the dependent variable is dummy-coded variables 1 / 0 is a variable for all categories except one, so if there are categories M will be M-1 dummy variables. All except their own category dummy variable. Each category is dummy variable has a value of 1 in its category and a 0 for all others. A category, the category of reference, is not its own dummy variable, as is clearly indicated by all other variables equal to 0.
Logistic regression mulitnomial then estimated a binary logistic regression model separately for each of these dummy variables. The result is M-1 binary> Logistic regression models. Each tells the effect of predictors on the probability of success in this category compared to the reference category. Each model has its own intercept and regression coefficients - the predictors may be of interest to each class may vary.
Why not just run a series of binary regression models? They could, and people were once, in multinomial regression models in the software away. You will probably get similar results. But it workstogether means that they simultaneously estimate the parameter estimates are more efficient means - there are fewer errors, unexplained.
Ordinal Logistic Regression: Proportional Odds Model
If the response categories are ordered, you could have a multinomial regression model. The disadvantage is that you throw away the information about the order. An ordinal logistic regression model retains this information, it is moreare involved.
In the proportional odds model, where the event is not modeled with a score in a single category, as in the binary and multinomial models. Rather, the event is modeled with a score in a particular category or all of the previous category.
For example, for a response variable with three ordered categories, the possible events, defined as:
* In Group 1
* In Group 2, or 1
* In Group 3, 2 or 1
In proportionalQuote model has its own intercept any results, but the same regression coefficients. This means:
1. the overall rate of all cases, be different, but according to the effect of predictors on the probability of an event in any other category, for each category. This is a hypothesis of the model, you need to check. It is often violated.
The model is a bit 'different than usual, written in SPSS, with a minus sign in all the intercepts and regressionCoefficients. This is a convention to ensure that lead to positive coefficients, the increase in X to an increased likelihood of a greater number of response values categories. In SAS, the character is a plus, and elevations of a predictor for increased risk of lead lower numbered response categories. Make sure you understand how the model in your statistics package before interpreting the results.
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