Question

a) In a linear regression, why do we need to be concerned with the range of the independent (X) variable? (Provide an example)

b) Explain the idea that correlation doesn’t imply causation (Provide and example)

Answer 1

a

b) Correlation doesn't imply caution:

Correlation is a statistical technique which tells us how strongly the pair of variables are linearly related and change together. It does not tell us why and how behind the relationship but it just says the relationship exists.Let's say, Correlation between Ice cream sales and sunglasses sold are highly positive. It means as the sales of ice creams is increasing so do the sales of sunglasses.

Causation says any change in the value of one variable will cause a change in the value of another variable, which means one variable makes other to happen. It is also referred as cause and effect. let's say when a person is exercising then the amount of calories burning goes up every minute. Former is causing latter to happen.

But two highly correlated values doesn't imply one is causing the other.

study says Ice cream sales is correlated with homicides in New York (Study)

As the sales of ice cream rise and fall, so do the number of homicides. Does the consumption of ice cream causing the death of the people. This correlation comes from a coincidence not from cause effect. On the contrary you can see the temperature is highly correlated with ice cream sell. then in this case the cause effect relationship (high temp causes high number of ice cream sell) is maintained.