We can see that all the variable have 0 or close to 0 values which means that the dummy coefficients that we just interpreted are highly significant. In the above example another way of understanding the importance and relevance of how good the data is that is being run by regression can be seen by the P-value. Note: Each dummy variable must be interpreted with its benchmark category mentioned before in the model. So the female coefficient of -3.07 means that “The average hourly salary of a female is less than $3.07 compared to the average salary of a male worker (the benchmark category)Ī non-white worked with a coefficient of -1.56 means that the average hourly wage of a nonwhite worker is as low as $1.57 compared to the average hourly salary of a white worker (the benchmark category. Remember that we explained that whenever we are interpreting the dummy variable, we have to refer to the benchmark or comparison category. How would we interpret the female dummy coefficient? Dummy variable interacts with both quantitative and qualitative variables but remember that an introduction of each dummy variable is at the cost of consuming each degree of freedom in the model.
This means that the intercept itself acts as a regressors in the model whose value is always one. If in case we don’t have an intercept in the model, than the dummy variables must equal the amount of the qualitative variables available in the equation.If in an equation we have an intercept, the amount of dummy variable must be one less than the amount of each qualitative variable.There are some important point you need to remember before you use regression analysis with dummy variables. When we run a regression of this equation in eviews, we will come up with a solution somewhat like this: For example: Using a wage function example: Let me explain how can we use dummy variable in a function and how do we interpret the terms written in that function.
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