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Use this tag only for regression models in which the response is a nonlinear function of the parameters. Do not use this tag for nonlinear data transformation; use [data-transformation] for that instead.

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Bell-curve shape regression [duplicate]

I am trying to fit some data that looks like a bell-curve: we reach a maximum at some value close to the mean, then the graph falls towards zero as we get further away from it. I am not the "owner" of …
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How do I test a hypothesis that proposes existence of a positive relationship between two va...

Even though the relation is non-linear (or maybe it is but with a lot of noise!), you can use linear-regression and then check for significance of the coefficient corresponding to variable $A$ (test f …
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When to use non-parametric regression such as kernel, generalized additive model, spline, an...

There are no standard criteria for taking your choice. As a general tip, you should use the simplest useful model (this sounds great and makes me feel very good about myself, but translating it into p …
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Residual Analysis assumptions for non-linear regression

There is also the hypothesis of homoskedasticity in the case of linear regression (ie: all residuals must have the same variance) When jumping into the non-linear regression world, different methods …
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Prediction in logistic regression with prediction criteria ranges

If Rank and Income are independent, you can do as follows: Top 15% observations have a 1 in 3 chance of being in the top 20% but not in the top 10% and a 2 in 3 chance of actually being in the top 10 …
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