Multiple Linear Regression is a regression approach that models the linear relationship between a single dependent continuous variable and multiple independent variables.
Multiple linear regression (MLR), often known as multiple regression, is a statistical technique that predicts the result of a response variable using several explanatory variables. Multiple regression is a variant of linear regression that employs only one explanatory variable.
Several regression is a type of regression that includes both linear and nonlinear regressions with multiple explanatory variables. Numerous regression, as opposed to linear regression, contains multiple independent factors that influence the slope of the relationship.
When you want to know how strong the association is between two or more independent variables and one dependent variable, you can use multiple linear regression (e.g. how rainfall, temperature, and amount of fertilizer added affect crop growth).
The most common use case of multilinear regression is to predict the future performance of an individual or a group on a given task based on their past performance
Learner's Ratings
4.7
Overall Rating
71%
29%
0%
0%
0%
Reviews
M
Muhammad Fahad Bashir
5
Explained the concept easily
P
Pradeep Kumar Kaushik
5
Please give me iris,csv file.
A
Ankit Malik
4
where is the finaldata.csv
V
Vimal Bhatt
5
great learning plateform kushal sir is really too good
Share a personalized message with your friends.