Linear regression is a analytical method through which we build a model in which we assume that two numeric variables have a linear relationship. In this video I'll take you through simple and multiple regression analysis. Multiple regression is different from simple linear regression in that more than one explanatory variable is used to explain the outcome variable. The explanatory variable is sometimes called the independent variable. The video goes through understanding how these models work and being able to interpret the results. I talk about the residual values and the assumptions about the residuals. I also talk about colliniarity and confounding. And I talk about the diagnostic plots that you can use to check your assumptions. We look at the F statistic, the multiple R squared and the adjusted R squared values. These values tell you how much of the change in the outcome variable can be explained by a change in the explanatory variable. I also talk about how to build a model but adding new variables and talk about interaction. Interaction refers to how variables interact with each other in terms of how the explanatory variables engender change in the outcome variable.

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This channel posts global health and public health teaching videos and videos about how to find the right job in global health. If you haven't already, please consider subscribing to this channel and becoming part of this community. The channel also provides teaching on research methods, statistical analysis and how to write and publish a scientific paper.

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