Search Results

I don't think it's necessary. Neural networks don't need your target to be a standardized normal variable. Don't hesitate to share your results though!

Assuming you're using the base R lm/glm functions: If you createthe dummy variables yourself, the step process will treat them as separate variables, so you may get some of them removed while the othe …

I don't know what sslope is but it reminds me of linear regression, where we build a model $Y=\beta_0 + \beta_1 x + \epsilon$ where: $\beta_0$ is a constant. …

, you can use linear-regression and then check for significance of the coefficient corresponding to variable $A$ (test for $\beta_A:=0$ in a model $B= \beta_0 + \beta_A A + \epsilon$ where $\epsilon$ is …

Your expression is a formula for the standard estimation error for a given parameter $\beta$ in terms of the sample mean, the observations. $\sigma$ stands for the standard deviation of the errors $ …

A good measure for how well 2D data fit into a line would be the determination coefficient. As you are familiar with R, you may want to compare compare: a=rnorm(100) b=rnorm(100) plot(a,b) cor(a,b)^2 …

The category variable coefficients indicate how much your prediction is incremented if a given individual belongs to the corresponding category. What strikes me is that you have two different coeffic …

You could in principle put types 1, 2 and 3 as a single group, but I don't know if this makes sense in your particular case. Also, please remember that statistical significance is an arbitrary tresh …

I am working on a problem where I have a "target" variable $Z$ that I know for sure is influenced by a "predictor" $Y$. I also have a second predictor $X$ that is correlated with $Y$ (about -.3), and …

Let's say you have a binary variable, like sex. You create two dummy variables to reflect that in your model. Let's say you have six individuals $(M,F,F,M,M,F)$. Your dummy variables look like: $X_1 …

It is possible. There is no theoretical limit to how many regressors you can use in your model, as long as it stays below the number of observations. Nevertheless, if the number of variables gets clo …

An $AR(1)-GARCH(1,1)$ is a GARCH(1,1) model estimated from the residuals on an $AR(1)$ A $DCC-GARCH$ model is a particular type of multivariate $GARCH$ where some restrictions are made on the conditi …

You can do it on R but you'll have to write the code yourself (there's no explicit method for the lm class that will do it for you) If your model is $Y:=\beta_0+\beta_1a+\beta_2b+\beta_3ab+\beta_4a^2+ …

If you've found small correlation between two variables, but the effect is highly significant, congratulations! This means you are working with a lot of patients and very powerful (i.e: likely to dete …

What you have in your hands looks like a classification problem rather than a regression one. … Fortunatelly, you already know a classification technique (confusingly enough: logistic regression) You may also want to tree different classifiers such as decision trees, which work great with categorical …