Final answer:
To interpret the impact of each coefficient in a glm model with a log link, you need to consider the change in the log of the outcome variable for a one-unit increase in the predictor variable. Exponentiating the coefficients and calculating standard errors and confidence intervals are important for precise interpretation. To summarize this information, create a table with predictor variables, coefficient estimates, exponentiated estimates, standard errors, and confidence intervals.
Step-by-step explanation:
When interpreting the impact of each coefficient in a glm model with a log link, there are a few key considerations. The coefficients represent the change in the log of the outcome variable (y) for a one-unit increase in the corresponding predictor variable (x), while holding all other variables constant. It is important to exponentiate the coefficients to interpret them in terms of the original scale of the outcome variable. Additionally, it is essential to calculate standard errors and confidence intervals to assess the precision and significance of the coefficient estimates.
If you want to build a table summarizing this information, you can list the predictor variables in the first column. Then, in the subsequent columns, you can include the coefficient estimates, exponentiated estimates, standard errors, and confidence intervals. This table will provide a concise summary of the impact of each coefficient, allowing you to interpret and compare the effects of different predictor variables.