Linear Regression model:
This model is useful to predict the relationship between two variables. The two variables are known as independent and dependent variable.
Regression is a technique to find an equation that describes the best-fitting line for a set of data. When data is plotted on a graph, this model gives us a straight-line relationship.
Basically, the core idea is to obtain a line that best fits the data. The best fit line is the one for which total prediction error of all data points are as small as possible.
Error is the distance between the points to the regression line.
Here, I have taken an example of relationship between “working hours” and “productivity”.
Below, we can see the dataset which contains
working hours (independent variable plotted on X-axis) and
productivity (dependent variable plotted on Y-axis).
On X-axis, I have plotted the no. of hours I spent on working on a project.
On Y-axis, I have plotted the no. of lines I wrote for coding.
Data points plotted on Y-axis: 10,25,40,80,95,120,200
Data points plotted on X-axis: 1,1.5,2,2.5,3,3.5,4
The Linear Regression equation is as follows:
y = a + bx + e
a = intercept
b = slope
e = error
Thanks for reading!