Linear Regression Model:
It is a technique of analyzing data sets in data science. It is an algorithm of machine learning that is most easy and famous. It uses statistical procedures for analysis prediction. Age, sales, salary, and average can be predicted by linear regression. Linear regression algorithms depict the association between two or more variables. The linear regression model uses a line to show the change. The linear regression uses a straight line, while all non-linear models use curved lines. One variable is dependent (a), and there can be only one or more than one independent variable. The linear regression algorithm will predict how the change in the independent variable’s value affects the value of a dependent variable. Linear regression algorithm is of the following types:
- Simple Linear Regression Model
- Multiple Linear Regression Model
In this article, you will get knowledge about both of the above-mentioned algorithms. So let’s get started. Earn yourself a promising career in data science by enrolling in the Data Science Course fee in Hyderabad offered by 360DigiTMG.
Simple Linear Regression Model:
Suppose there is only one independent variable that is used for predicting the change into the dependent variable. In that case, this type of regression algorithm is known as a simple linear regression model. Linear regression uses a formula to calculate or predict the change.
When to use simple linear regression?
- When you want to know how strong is the relationship between two variables (one independent and one dependent), i.e., the relationship between income and happiness.
- You can also use a simple regression model when you want to know the dependent variable’s value at an absolute value of an independent variable.
Formula:
Y= β0 + β1 + ℇ
y is the value of a dependent variable that is predicted
β0 is the divert when x is 0. It is the value of the prediction
β1 is a coefficient of regression that shows that as x will increase, how y is changed
Here x is independent, and y is dependent
ℇ is the error ratio expected.
Simple linear regression models work based on assumptions. If your data does not fulfill these assumptions, then this model is not for you. Use some other regression models. Looking forward to becoming a Data Scientist? Check out the Data Science Online Course and get certified today.
Example:
A simple linear regression model can show the relationship between income and happiness. Her salary is independent, whereas joy is a dependent variable. The simple linear regression model will depict how the change in the wage affects happiness.
Multiple Linear Regression Model:
Alternatively, suppose two or more independent variables are predicting the change in the dependent variable’s value. In that case, this type of model is known as the multiple linear regression model. The multiple linear regression model’s objectives are to show the relationship between two or more independent and dependent variables. Also, check this Data Science in Chennai to start a career in Data Science.
Formula:
Yi= β0 + β1xi1 + β2xi2+…+ βpxip+ ℇ
Yi = dependent variable
xi = independent variables
β0= y-intercept at time 0
ℇ = error ratio
The multiple linear regression also works based on assumptions. If you are using this model, then your data must fulfill the assumptions. Become a Data Scientist with 360DigiTMG Data Science Course in Pune with a Placement guarantee. Get trained by the alumni from IIT, IIM, and ISB.
Example:
One can survey which factors are involved in heart disease. For example, you can survey how smoking and hard work affect the heart. Here smoking and hard work are the independent variables, and heart disease is a dependent variable. So here, you can apply the multiple linear regression model.
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