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Next, we run the model omitting the variable (s) we wish to test, in this case, omitting i.cred . xi: logit hiqual (some output omitted) Logistic regression Number of obs = 1200 LR chi2 (0) =. May 26, 2020 · That is, if two variables of interest interact, then the relationship between them and the dependent variable depends on the value of the other interacting term. Interpreting Logistic Regression. Consider first the simple linear regression where Y is continuous and X is binary. When X = 0, E(Y|X=0) = β₀ and when X = 1, E(Y|X=1) = β₀ + β₁.. However, for multinomial regression, we need to run ordinal logistic regression. 2. You must convert your categorical independent variables to dummy variables. 3. There should be no multicollinearity. 4. There should be a linear relationship between the dependent variable and continuous independent variables. Using scores of 0 and 1, however, leads to particularly simple interpretations of the results of regression analysis. Step 2 Interpretation Of Coefficients If the categorical variable has K categories (e.g., region which might have K = 4 categories-North, South, Midwest, and West) one uses K - 1 dummy variables as seen later. For example, the following coefficients table is shown in the output for a regression equation: Regression Equation Heat Flux = 325.4 + 2.55 East + 3.80 South - 22.95 North + 0.0675 Insolation + 2.42 Time of Day. This equation predicts the heat flux in a home based on the position of its focal points, the insolation, and the time of day.