R Linear Regression

Regression analysis is a statistical tool to determine relationships between different types of variables. Variables that remain unaffected by changes made in other variables are known as independent variables, also known as a predictor or explanatory variables while those that are affected are known as dependent variables also known as the response variable. Linear regression is a statistical procedure which is used to predict the value of a response variable, on the basis of one or more predictor variables. Some common examples of linear regression are calculating GDP, CAPM, oil and gas prices, medical diagnosis, capital asset pricing etc. R Simple linear regression enables us to find a relationship between a continuous dependent variable Y and a continuous independent variable X. It is assumed that values of X are controlled and not subject to measurement error and corresponding values of Y are observed.