Linear Regression. Interpret weights as an effect on a prediction? 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.
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?
  Blockquote


Somewhat yes. Precisely it means if A changes by one unit the outcome will change 100 times, on average (while B is frozen because it is a multiple linear regression -> you have more than one independent variable).

There are tons of examples and blogs explaining it, see for example here .
Also you can analyze your regression model more by doing some statistical tests like identifying how variables are correlated using Variance Inflation Factors and estimate the statistical significance (p-value, F-test), here is a very nice intro. 


  An example: From a weather data set, I used two parameters, humidity
  and pressure, to make a prediction on temperature.


I do not see any problem with your analysis. As you said you see an inverse correlation between humidity vs. temperature in your plot as well as via your -.32386 weight. While the weight of -.02219 for pressure is nearly zero and negligible and obviously you won't see such relationship between pressure-temperature.