# Multivariate Linear Regression

In univariate linear regression, we had only 1 input parameter x.

Here, we have n input parameters $$x_1, ..., x_n$$

So, the hypothesis function changes to:

$$h_θ(x)=θ_0+θ_1x_1+θ_2x_2+......+θ_nx_n$$

If $$θ$$ denotes the (n+1) dimensional $$θ$$ vector and X denotes the input vector,

$$h_θ(x)=θ_TX$$ (if $$x_0=1$$)
