Simple Linear Regression in R Programming
Simple Linear Regression:
Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous variables. One variable denoted x is regarded as the predictor, explanatory, or independent variable. The other variable denoted y is regarded as the response, outcome or dependent variable.
Simple linear regression is the most well-known and popular algorithm in machine learning and statistics. This model will assume a linear relationship between the input and the output variable. It is represented in the form of the linear equation which has a set of inputs and a predictive output. Then it will estimate the values of the coefficient used in the representation.
In machine learning, we have a set of input variables(x) that are used to determine the output variable(y). A relationship exists between the input variables and the output variable. The goal of machine learning is to quantify this relationship.
In Linear Regression, the relationship between the input variables(x) and output variable(y) is expressed as an equation of the form y = a + bx. Thus, the goal of linear regression is to find out the values of coefficients a and b. Here, a is the intercept, and b is the slope of the line.