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You are operating on the mtcars dataset, which has 32 observations and 11 variables (you are predicting mpg using the other 10 variables): dim(mtcars) # [1] 32 11 In your training/testing set split …

You can use Fisher's exact test, which inputs a contingency table and outputs a p-value, with a null hypothesis that the odds ratio is 1 and an alternative hypothesis that the odds ratio is not equal …

For instance, here is the R code to simulate the expected value of $X^2e^Y$ with $\rho = 0.2$ using 1 million samples: # Parameters for calculation f <- function(x) x^2 g <- function(y) exp(y) rho <- …

Instead, as I understand it your question is specifically about R, and whether runif will return 0 or 1 due to the finite numerical precision of your computer. We can read the following from ? … So we conclude that drawing from the standard uniform distribution in R using runif will never return 0 or 1. …

It seems like you want to use a distance metric based on correlation, so you could assign a "dissimilarity score" between pairs of points based on correlation; I'll use 1-cor(a, b) to compute the diss …

From ?boxplot, you can read: range this determines how far the plot whiskers extend out from the box. If range is positive, the whiskers extend to the most extreme data point which is no …

Meanwhile, from the R documentation, chol returns an upper triangular matrix $M$ such that $X = M'M$ for input matrix $X$. From this we would expect them to be transposes of each other. … However, the outputs differ even more significantly because R and Stata have flipped the variable ordering -- the R function lists intercept first and expense second, while Stata lists expense first and …

From ?predict.glm, you can read that by default the type of prediction will be the link function (log odds for logistic regression) instead of probabilities. You can get predicted probabilities with …

Another way to write this by cancellation would be: $$ P(D|N) = \frac{60\times 59\times\ldots\times 51\times(N-60)\times(N-61)\times\ldots\times(N-149)}{N\times(N-1)\times(N-99)} $$ Note that the nu …

From the name Mean Absolute Percentage Error, there needs to be an absolute value in the calculation of MAPE: rowMeans(abs((actual-predicted)/actual) * 100) This matches the formula for MAPE at, fo …

The warning messages you received are informative here: 2: Some predictor variables are on very different scales: consider rescaling So it seems like a good approach is to rescale some variabl …