How do you interpret coefficient results?
A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease.
What is the right way to interpret a beta coefficient?
If the beta coefficient is significant, examine the sign of the beta. If the beta coefficient is positive, the interpretation is that for every 1-unit increase in the predictor variable, the outcome variable will increase by the beta coefficient value.
How is the interaction coefficient related to the outcome?
The interaction coefficient does not represent the difference in outcome between groups in the post period. Rather, as we go from pre- to post- treatment, each group experiences some change (possibly 0, but typically not) in the outcome. The interaction coefficient represents the difference between those changes.
How to interpret difference in differences regression results?
In interpreting results like this, it is important to remember what each coefficient means. I’ll assume that your treatment variable is coded 1 = active treatment/0 = control, and that your time variable is also a dichotomy with 0 = era prior to intervention and 1 = era following intervention.
How to find the mean difference between treatment and control?
To get the estimated mean difference in Y between the treatment and control groups after the intervention, you need to look at β1 + β3. It is possible that you will find that β1 + β3 is significantly different from zero, even though neither β1, nor β3 by itself is.
When is a coefficient not significant in regression?
There are several considerations here. First, when the p-value is not significant, the coefficient is indistinguishable from zero statistically. In other words, your sample provides insufficient evidence to conclude that the sample effect exists in the population. In that light, you don’t consider the sign.