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What is the difference between correlation and linear regression?

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What is the difference between correlation and linear regression?

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Correlation and linear regression are not the same. Correlation quantifies the degree to which two variables are related. With correlation, you are not drawing a best-fit line (that is regression). You simply are computing a correlation coefficient (r) that tells you how much one variable tends to change when the other one does. When r is 0.0, there is no relationship. When r is positive, there is a trend that one variable goes up as the other one goes up. When r is negative, there is a trned that one variable goes up as the other one goes down. With correlation, you don’t have to have any thought about cause and effect. It doesn’t matter which of the two variables you call “X” and which you call “Y”. You’ll get the same correlation coefficient if you swap the two. Correlation is almost always used when you measure both variables. It rarely is appropriate when one variable is something you experimentally manipulate. Linear regression finds the best line that predicts Y from X. The X va

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Correlation and linear regression are not the same. Consider these differences: • Correlation quantifies the degree to which two variables are related. Correlation does not find a best-fit line (that is regression). You simply are computing a correlation coefficient (r) that tells you how much one variable tends to change when the other one does. • With correlation you don’t have to think about cause and effect. You simply quantify how well two variables relate to each other. With regression, you do have to think about cause and effect as the regression line is determined as the best way to predict Y from X. • With correlation, it doesn’t matter which of the two variables you call “X” and which you call “Y”. You’ll get the same correlation coefficient if you swap the two. With linear regression, the decision of which variable you call “X” and which you call “Y” matters a lot, as you’ll get a different best-fit line if you swap the two. The line that best predicts Y from X is not the sa

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