Estimating impact of Predictor


I have a simple regression with 24-36 months of data, let's say of Sales which is the dependant variable and % spend on promotions in that month as independent variable (amongst other variables). I am running a normal linear regression on this

The independent variable , let's say can vary from 0-100% and the Dependant variable Sales varies from 7-10K $.

After running the regression, i am getting unusually high beta for the Predictor, 50-60K as the value.

What i want to understand is, is this the right construct of running a linear regression and if any transformation is required? I dont think so, as when i plotted the two on a scatter plot and fit a linear trend like,it fit perfectly and while displaying the R-squared, it showed a value of 60%.

if i want to understand, what is the impact on Sales on a 1% increase in the INdependent variable - how do i do this?

Currently, what i am doing is :

1. Computing Y (Baseline) as (beta * Average X + C)

in my case Beta is 60K, Average X is, let's say 15% and hence i obtain Y (Baseline) as 8.5k

2. For a 1% increase in X, i want to compute Y... so, i keep beta as same, C as same, i add 1% to average X and hence compute Y

i subtract this Y from Y(Baseline) computed in Step #1.

Is there anything i am doing wrong...?



Omega Contributor
If this isn't time series the model should be appropriate. The one thing you would need to look out for is, the distribution of IV, and whether a majority of values are at the lower or upper bounds (e.g., 0% or 100%), if that is possible. Meaning can you have 11% or negative percentage.