# Impact calculation- Regression

I am dealing with the below data: $$\begin{array} {|c|c|}\hline \text{user} & \text{A_PDE} & \text{B_PDE} & \text{C_PDE} & \text{D_PDE} & \text{Sales} & y_\text{pred} & \text{SSE} & \hat{y} \\ \hline 1 & 0.50 & 0.25 & 0 & 1.75 & 0 & 8.660412 & 75.00272 & 0 \\ \hline 2 & 0.00 & 0.00 & 1 & 0.00 & 0 & 4.02256 & 16.18099 & 0 \\ \hline 3 & 0.50 & 1.25 & 1 & 0.75 & 44 & 13.99656 & 900.2064 & 615.8487 \\ \hline 4 & 1.25 & 1.00 & 0 & 0.00 & 0 & 11.02223 & 121.4896 & 0 \\ \hline 5 & 0.00 & 0.75 & 0 & 1.50 & 0 & 7.240974 & 52.43171 & 0 \\ \hline \end{array}$$

I am trying to calculate the impact of each of the channels A_PDE, B_PDE, C_PDE, D_PDE. However, since Sales = 0 for most of the records, and my model is predicting non zero sales for those records, my $$R^2$$ value is very low. How can I fix this? I don't think removing the zero sales record from the model would be right.

• $$R^2$$ based reduction: Exclude one variable of your data set and calculate the $$R^2$$. Then exclude another variable of your data set and calculate the $$R^2$$. Repeat this process for all the variables and remove the variable for which the $$R^2$$ decreased the least or increased the most. Repeat this process for the reduced data set. Stop when the $$R^2$$ change is similar for all the variables.
• $$p$$-value based reduction: Determine the weights and remove the weights for which the $$p$$-value is not significant. For this procedure, you have to consider the cumulation of errors and apply Bonferroni correction.