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- Forward, backward, stepwise, and subsets. What are these methods, and how do we use them to build basic regression models? Grab a drink, get comfortable, and let's find out. Hello, and namaste. My name is Brandon, and welcome to the next video in my series on basic statistics. Whether you are a new viewer or a returning viewer, I am very grateful you have chosen to spend some of your valuable time with me. We'll make the most of it. As you're watching, if you like the video, please give it a thumbs up, share it with classmates, colleagues, or friends, or anyone else you think might benefit from watching. Also in the description below you'll find a link to all of my playlists. It's basically a table of contents, like you would find in the front of a book, for example. And please, if you haven't already, please hit the subscribe button and that bell notification. So now that we are introduced, let's go ahead and start learning. So this video is the next in our series on model building. So when we start to build more complex regression models, it's good to have a foundation of, in this case, four basic techniques that many people learn, and those are called forward regression, backward regression, stepwise regression, and best subsets regression. Now this video is going to be an overview of all four of these. There is no math involved, it's all conceptual. We're gonna be using a lot of visuals, a lot of graphics, and animations and things like that to get the fundamental ideas across. And then in future videos, we will take a deeper dive into each one and actually conduct each technique, see how things change as variables are added or deleted, and things like that. So this is a broad overview, so we can set the stage for more detailed learning as we go. So let's go ahead and get started. So first we will start with this famous quote. If you're around statistics or machine learning or data science long enough, you will encounter this quote, if you haven't already. So this quote is by George Box, who was a statistician at the University of Wisconsin here in the U.S., who recently passed away a few years ago, actually. And his famous quote is, "All models are wrong, but some are useful." And I think this is a very good way to start this video. So of course, we're gonna start the basic building blocks of regression model building. Now, every model we build is obviously gonna be an approximation of reality. And as you add more variables, they get more complicated. So things tend to happen, variables interact with each other. We're trying to mimic a real phenomenon that exists in the world, and all models are gonna have problems. They're gonna have deficiencies. They might overfit or underfit. We might not have the best mix of variables at any one...