Linear Regression | What is a Linear Regression?
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Answers ( 4 )
Linear regression can be defined as a relationship among an output variable on a certain set of in features, defined primarily with a set of assumptions on relation been restricted to be linear in parameters.
Simple linear regression, you have target variable and predictor variable.
You predict the target variable using the predictor variable assuming that
the relationship between them is linear.
The simplest form of linear regression is y = mx +c, where
y-> target variable
m-> regression coefficient
x-> predictor variable
c-> constant term
In Multiple Linear Regression, you have multiple predictor variables.
It takes the form
y = b0 + b1x + b2x + b3x + b4x + ………….. + bnx
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). ‘y’ can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.
In a simple regression problem (a single x and a single y), the form of the model would be:
y = B0 + B1*x
and y = B0 + B1x + B2x + B3x + B4x + ….. + Bnx in multiple linear regression.
Simple linear regression is a statistical technique used to model the relationship between two variables.
Mathematically, let suppose we have two variables x, y.
y -> dependent variable / target variable / output variable
x -> independent variable / predictor variable/ input variable
Linear model – y = ax + b
a = slope of the line, rate of change ‘y’ w.r.t ‘x’.
b = constant