Of course not all outcomesdependent variables can be reasonably modelled using linear regression. In previous posts Ive looked at R squared in linear regression and argued that I think it is more appropriate to think of it is a measure of explained variation rather than goodness of fit.
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Perhaps the second most common type of regression model is logistic regression.
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. Its easy to confuse yourself at that final stage by trying to read off the vertex as h k whatever number is inside the squared part whatever the other number is without noticing the fact that the h-part is subtracted and the k-part is addedIf you take care to ensure that you have your quadratic completely converted to vertex form by being careful of the signs then you.
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