# How do I interpret generalized linear regression with continues independent variable with Gaussian family and log link

I am running generalized linear regression Gaussian family and log link.

Independent variable is Time (continues variable).

Dependent variables:

1. years of practice (continues variable). Interpretation something like: each unit increase of years of practice gives increase of what kind of unit? of time?

2. impact (binary variable, with categories "type one" or "type two"). Interpretation something like: "impact type 2" in comparison to "impact type 2" gives increase of what kind of unit? of time?

How do I interpret results?

Let $$Y$$ be Time (your independent variable), $$P$$ be years of practice, and $$I$$ be impact (0 for type one, 1 for type 2).

Then, a glm with Gaussian family and log link fits

$$\log(\mathbb{E}(Y))=\beta_0 + \beta_pP + \beta_II$$

Exponentiating, we get

$$\mathbb{E}(Y) = e^{\beta_0 + \beta_1P + \beta_2I}$$

With a Gaussian family, our model is $$Y \sim N(e^{\beta_0 + \beta_1P + \beta_2I}, \sigma^2)$$

Note that we can break up the exponential term:

$$e^{\beta_0 + \beta_1P + \beta_2I}=e^{\beta_0}e^{\beta_1P}e^{\beta_2I}$$

The relationship between Y and the predictors is no longer additive as it would be with identity link. Instead, it is multiplicative. For example, a unit increase in $$P$$ mulitplies the value of the response by $$\beta_1$$, rather than increasing the response by $$\beta_1$$. Likewise, a person with impact of type 2 ($$I=1$$) will have $$\beta_2$$ times as many years $$Y$$ as a person with impact of type 1 who has the same number of years in practice $$P$$.

When performing inference on the coefficients, our estimates for the $$\beta$$'s do not have an exact Normal distribution, as they do with Gaussian family and identity link. However, there are several methods for testing coefficients that can be run on any glm. @gung's answer gives an excellent overview of the three types.