Ordinal Logistic Regressionoverview data how to example see also
Ordinal Logistic RegressionStat > Regression > Ordinal Logistic Regression
Use ordinal logistic regression to perform logistic regression on an ordinal response variable. Ordinal variables are categorical variables that have three or more possible levels with a natural ordering, such as strongly disagree, disagree, neutral, agree, and strongly agree. A model with one or more predictors is fit using an iterative-reweighted least squares algorithm to obtain maximum likelihood estimates of the parameters [29].
Parallel regression lines are assumed, and therefore, a single slope is calculated for each covariate. In situations where this assumption is not valid, nominal logistic regression, which generates separate logit functions, is more appropriate.
Response: Choose if the response data has been entered as raw data or as two columns - one containing the response values and one column containing the frequencies. Then enter the column containing the number response values in the text box.
Frequency (optional): If the data has been entered as two columns - one containing the response values and one column containing the frequencies - enter the column containing the frequencies in the text box.
Model: Specify the terms to be included in the model.
Factors (optional): Specify which of the predictors are factors. Minitab assumes all variables in the model are covariates unless specified to be factors here. Continuous predictors must be modeled as covariates; categorical predictors must be modeled as factors.