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This, at least, is standard in R. For predicting, there is probably a predict.elman function, to which you will need to feed the values of the explanatory variable x. …

You can either subtract your probability from 1, or use lower.tail=FALSE. > pp <- 0.2 > qnorm(pp) [1] -0.8416212 > > pp <- 0.8 > qnorm(1-pp) [1] -0.8416212 > qnorm(pp,lower.tail=FALSE) [1] -0.841621 …

R vectorizes operations (here: addition) automatically. > c(1,2,3,4,5)+5 [1] 6 7 8 9 10 (Such questions are better at StackOverflow in the R tag.) …

For instance, using cat with the file parameter: > n <- letters[1:10] > n [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" > cat(n,file="foo.txt",sep="\n") Look at ?cat.

Then R will take care of all dummy encoding automatically. …

Take a large amount of testing data, enough to cover a meaningful amount of your IV space. Apply your classifier to the testing data. Evaluate it, using your favorite error measure. Use a regression …

If you can perfectly reconstruct the dummies from a candidate predictor, then the dummies and the predictor carry the same information. If the dummies encode intervals of the predictor (the most commo …

Yes, you can do that. If you simply feed the data into xreg, then Arima() will fit a regression on your predictor with ARIMA errors. (This is not an ARIMAX model. See here.) I would recommend that yo …

Your "general understanding" is precisely correct. Each one of your infinitely many experiments will give rise to a separate confidence interval for the parameter of interest (here: the difference in …

In R, use ecdf(): > xx <- rnorm(100) > ecdf(xx)(2) [1] 0.99 To get a p-value, you will need to do some judicious subtraction from one, taking absolute values and/or division by or multiplication by …

Both are available in the forecast package for R. If you do not expect multiple seasonalities, you could try standard forecasting algorithms, like exponential smoothing (forecast::ets()). … I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman, which uses R throughout. …

I don't have the APA Publication Manual at hand, but I don't think there is anything specific for a negbin regression in there. I would report it in a similar way to a correlation coefficient. For ins …

The internal optimization in HoltWinters() ran into a numerical problem. That happens. I suggest you try ets() from the forecast package, which will also fit exponential smoothing models, albeit in a …

You are looking at two completely unrelated things. Yes, looking at the normal density evaluated at 1, ..., 100 (and matched for mean and variance) will look normal, because it is. But to assess wheth …

Note that the middle boxplot does show upper and lower whiskers. Note that whiskers extend from the box to specific values, namely, for the upper whisker, "the largest value no further than 1.5 * IQR …