linear regression.

So, that's linear regression.

Today, we'll see how multiple regression, multiple regression works where we have multiple predictors.

linear regression for classification problems.

In particular let's talk about how to use gradient descent for linear regression with multiple features.

So, here's our linear regression algorithm.

linear regression isn't a good idea .

In the previous video, we talked about the form of the hypothesis for linear regression with multiple features or with multiple variables.

So linear regression works well if the data is approximately linear, but there are many examples when linear regression performs poorly.

So that's linear regression with gradient descent.

And thus, using linear regression this way.

Linear regression? Do we compute confidence intervals?

And then, we can have multiple predictors and multiple regression coefficients.

We, just ran a multiple regression analysis.

So outliers are particularly bad for linear regression.

Our first learning algorithm will be linear regression.

At first we've covered multiple regression analysis sort of conceptually, and how to interpret regression coefficients when there are multiple predictors in our regression equation.

And what's new is doing multiple regression analysis, asking for standardized regression coefficients.

How do we do that in multiple regression?

Particularly when we do things like multiple regression.

That's the beauty of doing multiple regression analysis.

We're just gonna do a multiple regression analysis.

You now know about linear regression and gradient descent.

And let's say you've implement and regularize linear regression.

This model is called linear regression or this, for example, is actually linear regression with one variable, with the variable being x.

But if you implement the algorithm written up here then you have a working implementation of linear regression with multiple features.

You can do a multiple regression analysis by hand.

Well, let's see that in a multiple regression analysis.

Thrun In this question I quiz you about linear regression.

Right? This was our objective function for the linear regression.

linear regression for fitting a straight line to our data.

This line over here is linear regression to the data.

And today, I'd like to introduce you to multiple regression.

So, I'll just summarize the multiple regression equation as follows.

Then, we can apply that to the multiple regression example.

To see how we get those. Multiple, regression coefficients estimated.

But first lets just start with basic multiple regression analysis.

So the main topic of this segment is just again estimation of regression coefficients in multiple regression.

And today we're going to do multiple regression analysis in R.

You'll have to write a script that does multiple regression analysis.

We already have linear regression and we have logistic regression, so why do we need, you know, neural networks?

Regression co efficients, in a multiple regression, using matrix algebra and last week, you didn't even know matrix algebra.

So in the first segment, we'll run a first multiple regression analysis.

last segment demonstrating the use of multiple regression analysis to test mediation.

Then linear regression tries to fit a line that best describes the data.