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$N(0,σ^2)$ then $Var(Y|X_2)=Var(β_1+β_2X_2)+Var(u)=0+σ^2=σ^2$, since $β_1+β_2X_2$ is not a random variable. 2) In the logistic regression, it is assumed that the errors follows a binomial distribution … It is better to write it as $Var(Y_j|X_j)=m_j.E[Y_j|X_j].(1-E[Y_j|X_j])=m_j\pi(X_j).(1-\pi(X_j))$, since those probabilities depend on $X_j$, as referenced here or in Applied Logistic Regression. …

To answer "Could it be ok to test performance on a model trained on the full data set with cross-validation?" NO, I don't think this is OK. You should fit all the 3 models to the same subset of your d …

Since your outcome is binary (win/loss), you may want to fit a Logistic regression by including all or a subset of those 6 independent variables and decide which variable is significant based on reported … Some examples of fitting a logistic regression in R have been given here. …

I will give an example for logistic regression in R since I don't have the S-plus: > mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") > mylogit <- glm(admit ~ gre + gpa + rank, data …

Thanks to those who tried to answer it. However, I don't think either of these answers are that much helpful to me. In fact there is a phd thesis written on this available here. There are also some R …

I have a logistic regression model and my main goal is to predict probability of surviving using explanatory variables like age, gender etc. … When I fit the logistic regression, the model performs well in country A (as expected) but not that well over other countries. …

I have a Bernoulli response variable and I am going to fit a logistic regression. … Ideally I would like to see the logistic regression coefficients of this categorized variable to be statistically significant. …