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As the independent variable is adjusted, the level of the dependent variable will vary. The dependent variable is the
variable under study, and it is the variable that the regression model tries to predict. In the linear regression task, each
observation is made up of the value of the dependent variable and the value of the independent variable.
Regression is basically used when the dependent variable is of a continuous data type. The independent variables, on the
other hand, can be of any data type—continuous, nominal/categorical etc.
There are several types of regression analysis, which are as follows:

Random Forest Support Vector Decision Tree
Regression Regression Regression

Polynomial
Linear Regression Types of Regression
Regression

Ridge Regression Lasso Regression Logistic Regression

Linear Regression-Finding the Line

When we make a distribution in which there is an involvement of more than one variable, then such an analysis is called
Regression analysis. Regression generally focuses on predicting the value of the variable that is dependent on the other
variable. Linear regression helps to predict values based on existing data. Let us consider two variables x and y.
y – Regression or Dependent Variable
x – Independent Variable or Predictor line: y=mx+b + e

Therefore, if we use a simple linear regression model where
y depends on x, then the regression line of y on x is: y (dependent) variable

y = mx + b + e (Predicted) ŷ
where, y 2
1
u x is the independent variable. y 2
(Observed)
u y is the dependent variable.
u m is the slope of the line. y-intercept
u b is the y-intercept. x 1 x (independent) variable
u e is the residual error and represents y(observed) - ŷ(predicted) or (y - ŷ )
2
2
Properties of Linear Regression

The following are the properties of linear regression:
u The regression line minimises the sum of squared differences between the observed and predicted values.
u The line passes through the mean values of both the X and Y variables.
u The constant b represents the y-intercept of the regression line.
u The coefficient m represents the slope of the regression line and indicates the average change in the dependent
variable (y) for a unit change in the independent variable (x).

Regression coefficient

The regression coefficient (denoted as m) provides valuable insights into the relationship between the independent
variable (x) and the dependent variable (y). Several key things that can be determined from the regression coefficient:

Data Modelling and Simple Linear Regression 245