EEB225H1 Lecture Notes - Lecture 7: Simple Linear Regression, Logistic Regression, Nonlinear Regression
28 Apr 2016
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In simple linear regression dependent variable between the two. The line of best fit minimizes residuals to o that and to capture data. If you put linear, there is massive difference between observed. ^no idea where the line of best fit will be at. polynomial example increasing orders: curvature increases as order of polynomial increase: Do not fit a polynomial with too many terms (the sample size should be at least 7 times the number of terms). Fernandez-juricic et al. (2003) - effect of human disturbance on the nesting of house sparrows. They counted breeding sparrows in 18 parks in madrid, spain, and also counted the number of people walking through each park (both measurement variables). Use specifically for (0,1) data: 0= absence/ no 1= presence/yes. More than one x (predictors) and one y. Like linear, but now you have lots of predictor. Equation of a plane (not a line as in linear regression).
