# How to Tackle Linear Regression Problems

The term **linear regression** is commonly used in the theory of the statistics. It can be defined as an approach towards the modeling of the relationship between any scalar variable say y which is dependent and 1 or more variables which are explanatory represented by X. When we have only 1 variable which is explanatory then it is known as the simple regression. And when we have greater than 1 variable which is explanatory then it is known as the multiple regression.

However the multiple type of the regression should not be confused with the multivariate type of the linear regression in which correlated variables which are dependent are being predicted unlike just a single variable which is scalar. (know more about Linear regression , here)

Under the linear regression, the data is modeled with the help of the linear type predictor functions and the unknown type parameters of the model are judged by the data. These types of the models are known as the linear models. Generally linear type of the regression is being referred as a model where the conditional type mean of the y is called as an affine function of the X if the value of the X is given.

And in rare case this type of the regression is referred as a model where the median or any quantile of the conditional type of the distribution of the y is represented in the form of any linear function of the X if the value of the X is given. It should be known that it is the 1^{st} kind of the regression that is being studied so rigorously and is being utilized in many practical uses.

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