From the course: Introduction to jamovi

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Regression: Chapter overview

Regression: Chapter overview - jamovi Tutorial

From the course: Introduction to jamovi

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Regression: Chapter overview

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- [Instructor] In our next chapter on regression in jamovi, we're going to look at associations, specifically through correlation and scatter plot, and we're also going to look at the ways we can use many variables to predict scores on one variable, how we can use predictor variables to look at an outcome or criterion variable. And we'll do this primarily through variations on regression. Jamovi gives us, actually, a great set of choices. We can do standard linear regression and one of the most powerful, flexible and useful procedures available. We can also do binomial logistic regression, where the outcome variable is not a quantitative or a continuous score but a dichotomy, this or that. And you're trying to use a collection of variables to predict which of two categories a case will go into. Then, there's multinomial logistic regression, where you have several categories in your outcome variable. This is actually a very sophisticated procedure, and it's a surprising thing that…

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