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Is it necessary to check the correlation before going for regression?
1. As explained in the above responses, finding a significant correlation is not a pre-requisite for running regression. There are many cases where two variables might not show a strong bivariate correlation but may show a strong association in regression once other variables are controlled for.
Is correlation affected by variables?
A correlation value close to 0 indicates no association between the variables. Since the formula for calculating the correlation coefficient standardizes the variables, changes in scale or units of measurement will not affect its value.
When do you use correlation in regression analysis?
In this section we will first discuss correlation analysis, which is used to quantify the association between two continuous variables (e.g., between an independent and a dependent variable or between two independent variables).
What is the relationship between a regression and an outcome variable?
Regression analysis is a related technique to assess the relationship between an outcome variable and one or more risk factors or confounding variables. The outcome variable is also called the response or dependent variable and the risk factors and confounders are called the predictors, or explanatory or independent variables.
How does a correlation help in feature selection?
For example, two variables which are linearly dependent (say, x and y which depend on each other as x = 2y) will have a higher correlation than two variables which are non-linearly dependent (say, u and v which depend on each other as u = v2) How does correlation help in feature selection?
Why exclude highly correlated features when building regression model ?? If you are someone who has worked with data for quite some time, you must be knowing that the general practice is to exclude highly correlated features while running linear regression.