Introductory video

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Из курса от партнера Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models

27 оценки

Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models

27 оценки

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

Из урока

Introduction and expected values

In this module, we cover the basics of the course as well as the prerequisites. We then cover the basics of expected values for multivariate vectors. We conclude with the moment properties of the ordinary least squares estimates.

Introductory video2:17

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Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

Hi, my name is Brian and welcome to the class Advanced Linear Models for

Data Science 2.

In this class we're going to build on the material that we learned in

Advanced Linear Models for Data Science 1.

You really need to know that material quite well before moving

onto this material.

So if you feel a little bit fuzzy on it or you haven't taken it for

a while, go back and review that material,

it's all also on YouTube if you don't want to re-enroll in a Coursera course.

In addition, in this class we're going to really focus on

sort of the theoretical underpinnings of linear models and

this will help you be a better applier of linear models.

But we're still not going to spend that much time going over the application.

So in order to augment this,

it would be useful to also look at my regression models course on Coursera.

I've included a lot of the links in the readings before the lectures

start where you can go and watch the relevant regression model and lectures.

What we'll go over the important applied topics that coincide

with the theoretical topics that we're going to be discussing in this class.

So make sure you watch those lectures if you aren't already

familiar with that content.

In addition, I'm going to presume that you have a basic working understanding of

mathematical statistics, and when some of those concepts are needed,

and you may or may not have had them, I'll include some

links to my course Mathematical Biostatistics Boot Camp 1 and 2.

Which covers some of the univariate basics that we'll need to cover the multi varied

versions for this class.

So in this class, mostly we're going to be focusing on are the distribution of

results associated with linear models where we assume normality

with spherical or IID normal airs with maybe a common variance.

So we're going to show how we arrived at f test and high score testing,

t test that are associated with this model.

Again, it'll be a fairly theoretical treatment.

But when we can, if we could tie it to

how we think about applications we're going to do that as well.

So welcome to the class.

I look forward to working with you and look forward also to seeing you in

the next version of this class, Advanced Linear Models for Data Science 3.

Which will hopefully be coming out soon.

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