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 $x_0=1$)