You are dealing with count data, so technically the MW test (equivalently Wilcoxon rank sum), which deals with continuous outcomes, would not be the theoretically "correct" method. There are ...

The collinearity is not between those two variables. They both have coefficients. Rather it is a joint collinearity between the two variables and the interaction variable. The interaction variable is ...

P-values are a measure of the strength of evidence against a particular null hypothesis. You have specified that the null hypothesis is that the trend is either zero or decreasing posed against the ...

The specific answer to the question is "yes, that is a linear model". In R the "*" operator used in a formula creates what is known as an interaction. If those two variables are ...

Reprex (courtesy of https://easystats.github.io/blog/posts/performance_check_collinearity/): library(glmmTMB); library(performance) #note: needed to also install the insight package before ...

Complete linear dependency between two variables is very simple to test: if( cor(v1,v2) != 1 ) { print("v1 is not linearly dependent on v2"} It's extremely unlikely that a factor variable and a ...

What you can do will depend strongly on the numbers of events in the 0-3, 3-6, and 6+ month groups. If you have adequate numbers, say from a Medicare study of discharged patients, you could simply ...

Confidence intervals answer the question: "What is the range of plausible findings under the triparte assumption that the population is Normally distributed, the mean is the observed value and the ...

You have not defined the question sufficiently. If in this particular circumstance the question revolves around a proportion or a rate with an arguably Poisson distribution, then you are in luck, ...

This would be logically equivalent to testing for the coefficient B being zero in a model that already had an estimate for A, and this in turn would be tested by running an ANOVA for a significant ...

The literature on radiation carcinogenesis is large. It's a reasonable assumption that there is a lag from exposure to clinical event. It's likely in the extreme that there is some knowledge about ...

The lm-function would estimate the line where trt was the x-predictor and ctl was the y-outcome: png(); plot(trt ~ ctl) abline(lmod <- lm (ctl ~ trt ), col='red') dev....

Neither normality tests nor homoscedasticity tests are really checks of independence (but I think you do know that.). And you could argue that most of the t-tests and anova methods are really ...

If the collinearity is rather simple, i.e. pairs of variables are tightly correlated then you can run cor over all the pairwise combinations. A more efficient way is to look at rcorr in the Hmisc ...

In R you would need to use orthogonal polynomials rather than just regressing against X and X^2 if you wanted to get any valid inference of the impact of adding the X^2 term. I'm not a Stata user, but ...

The usual comparisons with the F distribution are at the "right tail". Comparisons at the left tail could be done, but any identified discrepancy between the data and the model would imply the the ...