Aug 5, 2023 · A couple decides to keep having children until they have the same number of boys and girls, and then stop. Assume they never have twins, that the "trials" are independent with probability 1/2 of a boy, and that they are fertile enough to keep producing children indefinitely.

May 16, 2021 · When you use a paired T-test, you are essentially doing a one-sample test, where your one sample consists of the paired differences between outcomes in two groups. If you create a new sample of these difference values and then apply the formula for a one-sample T-test, you will see that this is equivalent to the paired test.

Sep 26, 2021 · Cell "girls x boyonly school" is empty, likewise cell "boys x girlonly school". So I recommend you to obtain the vector of predicted values and check yourself, which differences the coefficients represent.

Suppose I want to build a model to predict some kind of ratio or percentage. For example, let's say I want to predict the number of boys vs. girls who will attend a party, and features of the party...

May 23, 2019 · alternatively, you could inverse the relation and model the independent group variable as a function of the dependent variables. This is especially interesting with the multivariate type of dependent data if it has some structure, e.g. clusters. Plotting the dependent variables in a scatter plot with different colors for the groups, in order to see what kind of …

Oct 23, 2016 · The GLS estimators are MLEs and the first model is a submodel on the second, so you can use the likelihood ratio test here. The likelihoods can be extracted using the logLik function and the degrees of freedom for the test will be 4 4 since you've deleted 4 4 parameters to arrive at the submodel. What is a simple, effective way to present the comparison? I think the …

Whereas, the t test is appropriate test of difference between the means of two groups at a time (e.g., boys and girls). It is also possible to compute a series of t tests, one for each pair of means.

I proved that the percentage of variation explained by a given predictor in a multiple linear regression is the product of the slope coefficient and the correlation of the predictor with the fitted values of the dependent variable (assuming that all variables have been standardized to have mean zero and variance one; which is without loss of generality). Find it here:

Nov 8, 2023 · I ran an ANOVA finding for example an interaction between gender and grade than I want to know in what grades boys and girls differ, but in many cases I find (adjusted) p-values of 0 and 1. How / w...

Dec 24, 2022 · The formula for the correlation is equal to the covariance between x and y, divided by the standard deviations of x and y. So what happens if you keep on denominator (where the standard deviation is) larger and large?