A correlation analysis determines the strength and direction of a linear relationship between two variables, whereas a simple linear regression analysis calculates parameters in a linear equation that may be used to forecast the values of one variable based on the values of the other. Correlation.
Only one independent variable is present in simple linear regression, and the model must identify a linear relationship between it and the dependent variable. Multiple Linear Regression, on the other hand, uses more than one independent variable to find a relationship.
To model the relationship between two continuous variables, simple linear regression is utilised. The goal is frequently to anticipate the value of an output variable (or responder) based on the value of an input variable (or predictor).
In a nutshell, linear regression is a useful supervised machine learning approach for modelling linear connections between two variables. Simple linear regression is a nice place to start when looking at our data and considering how to develop more complex models.
Learner's Ratings
4.5
Overall Rating
73%
16%
6%
4%
1%
Reviews
S
Sayali Jadhav
4
Very useful all data
S
Syed Mohammad Qaiser Rizvi
5
Way of teaching is very good. I want to get PPT which he is using to teach us ?
A
Anuja Bagadi
5
It is a very interactive and useful course..
K
Kashinath Myakale
5
All courses is very useful for which are looking free courses and gaining knowledge
A
ANKiT KUMAR BAMNiYA
4
SC
D
DOGALA UDAYKUMAR
5
OK
A
Anuj Jehta
5
nice
N
Nishant Saxena
5
great content
A
Aditya Purohit
5
Very good course & amazing cocepts & detailed explaination of each and every thing .
Thanku soo much Learn Vern ...
Share a personalized message with your friends.