A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

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From the course by Johns Hopkins University

Statistical Reasoning for Public Health 2: Regression Methods

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A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

From the lesson

Introduction and Module 1A: Simple Regression Methods

In this module, a unified structure for simple regression models will be presented, followed by detailed treatises and examples of both simple linear and logistic models.

- John McGready, PhD, MSAssociate Scientist, Biostatistics

Bloomberg School of Public Health

So in this section, we're going to take on

Â simple logistic regression which is very similar in spirit to

Â simple linear regression and

Â the general linear model framework we set up at the beginning of Lecture One.

Â In this situation, however,

Â we're dealing with a binary outcome.

Â And we're going to end up for reasons that I'll

Â get into in the second part of this lecture

Â set that we're going to have to end up modeling it on the log odd scale.

Â So what we'll be doing is starting with something that's measured as a yes or no,

Â one or zero, and then transforming it to

Â a probability then an odds then the log of an odds.

Â We think we're going to estimate is a linear function of

Â our predictors is the long odds of the outcome.

Â And that may seem strange at first but it's not

Â a particularly convenient scale but we'll see that the result the estimates

Â we get are one step removed from scales that we're familiar with in

Â terms of comparisons and making statements about risk through the odds.

Â And we'll see, we're also not constrained to odds and odds ratios which are

Â the immediate results we get from logistic regression but with a little work,

Â we can translate our estimates into proportions or probabilities as well.

Â In general, though, the generalized framework will be very similar

Â to what we did with linear regression in terms of the comparisons

Â being made by our slopes

Â and the whole spirit of modeling or outcomes and the linear function of predictors.

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