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Internally, in regression (including logistic regression), nominal variables are treated as numbers, with possible values being 0 and 1. …

Depending on the 'link function' they can be used for linear regression, Poisson regression, logistic regression (which would give you probabilities and allow for classification), and many more. … For example, if you assume that the noise is additive and Gaussian, this leads to ordinary linear regression. …

You can most likely interpret your regression as a linear regression over non-linearly transformed variable. What matters is your loss function. … Mean absolute error is likely to be correlated, but a less suitable measure than $R^2$ -- unless, of course, your regression is minimising the sum of absolute errors. …

I was recently asked why I haven't analysed interactions in my (predictive) regression model (I understood "interactions" here to mean products of predictor variables). …

Two points: Your data are log-log scaled. So why don't you take the logs of them? Since you expect a sigmoid function behind the data, why not trying fitting it to the data? Below, I model your lo …

I have curves, describing survival of cells in a toxic agent: One curve per cell line, the concentration of the agent on the x-axis and the fraction of survived cells on the y-axis: I'd like to dra …

Proportional odds logistic regression predicts probabilities for each level $l$, conditioned on the predictor $x$: $$ P(y = l ~|~ x) \text{ for every } l \in L $$ But in practice we mostly simply want …

The formula your "most books" give cannot be right because, in general, $N \neq D$. Consequently, you cannot multiply a row vector of length $D$ with a matrix containing $N$ rows. Thats simple linear …

In case I'm not missing anything from your explanation (some data and details of the model would be useful, though), I guess the reason lies in the different error functions the two models use. In the …

It depends on the probability distributions of the errors, unexplained differences between your model and the observed data. MSE is appropriate when you expect the errors to be normally distributed. T …

Your "education" variable is not simply categorical, it is ordinal. High school is more than no school, undergraduate school is more than nothing or high school etc. Ordinal variables can be thought o …

For an experiment I'm designing, I want to model the outcomes by a binomial regression, something like bmodel <- glm(cbind(succ, (N - succ)) ~ x + x_ind, data = tb1, family = "binomial") where x_ind … The answer to the above linked question explains how to compute the power for binomial regression when the predictor is continuous: $$ \gamma = 1 - {\bf{\Phi}}(1.96-\vert\beta\vert\sigma_x \sqrt{(np(1- …

I'd like to have something equivalent to the F-test as in linear regression. … I have checked regression with constraints, How can I add minimum and maximum constraints to a coefficient in a regression in R? …

I have read Simple linear regression model with random x, Regression with random X, Find P(Y=y | X=x) when X is a continuous random variable, and What are the Differences in Linear Regression of Y vs X …

I tried to replicate @dimitriy's results in Python and got slightly different results: Logit Regression Results =================================== … To my knowledge, people use 0-2 / 3-6, or even trichotomise (0-2, 3-5, 6), in which case ordinal regression needs to be used. …