Transcription of Non-Linear & Logistic Regression
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Non-Linear & Logistic Regression
Non-Linear & Logistic Regression If the statistics are boring, then you've got the wrong numbers. Edward R. Tufte (Statistics Professor, Yale University) Regression Analyses When do we use these? PART 1: find a relationship between response variable (Y) and a predictor variable (X) ( Y~X) PART 2: use relationship to predict Y from X Simple linear Regression : y = b + m*x y = 0 + 1 * x1 Multiple linear Regression : y = 0 + 1*x1 + 2*x2 .. + n*xn Non linear Regression : when a line just doesn t fit our data Logistic Regression : when our data is binary (data is represented as 0 or 1) Non-Linear Regression Curvilinear relationship between response and predictor variables The right type of Non-Linear model are usually conceptually determined based on biological considerations For a starting point we can plot the relationship between the 2 variables and visually check which model might be a good option There are obviously MANY curves you can generate to try and fit your data Exponential Curve Non-Linear Regression option #1 Rapid increasing/decreasing change in Y or X for a change in the other Ex: bacteria growth/decay, human population growth, infection rates (humans, trees, etc.)
parameters – we are using maximum likelihood estimation • We can however calculate a pseudo R2 - Lots of options on how to do this, but the best for logistic regression appears to be McFadden's calculation Logistic Regression (a.k.a logit …
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