In a basic linear regression, can I use the weights of each explanatory variable to describe their relative effects on the predicted value? If parameter A has a weight of 100, and parameter B has a weight of 10, can I say that parameter A has 10 times more effect on the outcome than parameter B?
An example: From a weather data set, I used two parameters, humidity and pressure, to make a prediction on temperature.
When I plot humidity vs. temperature, there's a very clear inverse relationship between the two. That is, the scatter plot tends down and to the right.
When I plot pressure vs. temperature, there's no relationship at all. The pressure in this report has very little variance, and the scatter plot is nearly vertical.
Based on those two plots, I intuit that changes in humidity have an effect on the high temperature, while changes in pressure have almost no effect.
I built a linear regression model (gradient descent, by hand, in Octave). Humidity had a weight of -.32386. Pressure had a weight of -.02219. Humidity's weight was 14.6 times larger than pressure's weight.
Based on that, can I state that parameter Humidity has nearly 15 times more effect on Temperature than parameter Pressure?
Forgive what must seem like a very amateur question. If you can give a relatively simple answer and point me toward resources where I can learn more, I'd really appreciate it. Thanks in advance.