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Lecture 5

SOC350H5 Lecture 5: Lecture 5
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Department
Sociology
Course
SOC350H5
Professor
David Pettinicchio
Semester
Winter

Description
Lecture 5  We want to interpret slope and if model is a good model – explained variance  Because it’s a bivariate mode – only one variable explaining  Conceptually there is no outcome that can be explained by just one variable  Multitude of factors – other variables that are supposed to influence the dep variable  Control for other expected factors that would explain that outcome  To do so you have to do multivariate reg  Pay attention to slope, sig, r sq  Want to know if you make model better or worse when you add other variation  Stan beta – compare slopes  Stand coefficient – allows you to talk about magnitude of effect Multivariate reg  Additive models – full model table  Which includes all the indep variable tha you pla on including  I des with multiple reg is to see what happens to dep variable as you add  You aren’t just putting in variables for just putting variables  This allows you to design models that speak to competing explanation for why things look the way they do  Take into account, other effects  How much that helps explain the outcome  What does lit say – have proof for including variables  You want to add variables that are theoretically meaningful  Whatever you do for putting in models has to be motivated by general belief in lit review  Bivariate regression – a reg where we predict (y) – with inequality Multivariate reg  Ineq but also involve GDP  2 x variable  inequality is no longer statistically sig  When you start with hyp that says ineq influences homicide  You add other variables  Are you explaining more variation than other variable? Increase in R sq  This is a better model than the first one  Model can suffer because of things you didn’t control for variables that other people say matter  When you get the SPSS output these values have to be presented in certain way  Show the title – inequality GDP  Slope (has * because it means that it sig at .05) second model is full – includes new r2 new slope, new variable, stan error, intercept  When you look at reg – want to look at: size of sig of original, r2 and what happens to new variable – sig?  Additional tools that help contextualize findings and say whe
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