Category: Linear Regression

What is stepAIC in R?

In R, stepAIC is one of the most commonly used search method for feature selection. We try to keep on minimizing the stepAIC value to come up with the final set of features. “stepAIC” does not necessarily means to improve the model performance, however it is used to simplify the model without impacting much on the performance. So AIC quantifies the amount of information loss due to this simplification. AIC stands for Akaike Information Criteria.

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What is Linear Regression? Part:1

Linear Regression is a field of study which emphasizes on the statistical relationship between two continuous variables known as Predictor and Response variables. (Note: when there are more than one predictor variables then it becomes multiple linear regression.)

  • Predictor variable is most often denoted as x and also known as Independent variable.
  • Response variable is most often denoted as y and also known as Dependent variable.
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Covariance and Correlation

Covariance and Correlation are very helpful in understanding the relationship between two continuous variables. Covariance tells whether both variables vary in same direction (positive covariance) or in opposite direction (negative covariance). There is no significance of covariance numerical value only sign is useful. Whereas Correlation explains about the change in one variable leads how much proportion change in second variable. Correlation varies between -1 to +1. If correlation value is 0 then it means there is no Linear Relationship between variables however other functional relationship may exist.

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What is Linear Regression? Part:2

In any business there are some easy to measure variables like : Age, Gender, Income, Education Level etc. and there are some difficult to measure variables like amount of loan to give, no of days a patient will stay in the hospital, price of the house after 10 years etc. So Regression is the technique which enables you to determine difficult to measure variables with the help of easy to measure variables.

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