I know some statistics but I still have a hard time grasping what “controlling for a variable means”. For me, it means that you want to isolate the variance explained by a particular variable by controlling for variables that contribute with confounding variance.
E.g., I want to predict ice-cream sales. As a predictor, I choose outside temperature. Let’s say that this explains 25% of the variance in ice cream sales. Now, let’s say that I want to control for what time of the day it is. People might buy more ice cream around lunch than in the morning. This is confounding since I only want to know how much variance outside temperature contributes with. So, I control for time of day. Now, when I do this, the variance explained by temperature should decrease – right?
Or, does “controlling for” simply means including time of day as a predictor, just like outside temperature?
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Controlling for means this variable is eliminated from the data by some other method before evaluating based on the criteria you want. For example, in medical studies they isolate for the placebo effect by randomizing the pool of candidates and make it a blind or double blind study.
Check out the wiki: https://en.m.wikipedia.org/wiki/Controlling_for_a_variable#:~:text=In%20controlled%20experiments%20of%20medical,such%20as%20the%20placebo%20effect.
In your specific example of ice cream sales, it would be hard to isolate temperature from time of day since temperature fluctuates through the day. You may have to either work with a daily average sale and daily avg temp or isolate down to the same selling period so that the “time of day” doesn’t influence sales. For example, using 11am-1pm to capture your data for analysis and then comparing to to other days at the same time period.
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