A correlation coefficient is one number, and most of the real work is choosing the right coefficient and reading the interval around it. Magic Stat gives you Pearson, Spearman and Kendall with their confidence intervals, plus partial correlation — from the spreadsheet you already have.
A correlation coefficient summarises how strongly two variables move together. Pearson's r measures the strength of a linear relationship between two continuous variables. Spearman's rho and Kendall's tau work from the ranks instead, so they describe any monotonic relationship and are not thrown off by skew, outliers or ordinal data. That is the first decision: the coefficient has to match the shape of the data, not the other way round.
The second thing a coefficient does not say is causation — and it also does not say how precise the estimate is. Magic Stat reports the confidence interval next to every coefficient, because an r computed on a handful of pairs and the same r computed on hundreds are not the same result.
When two variables look associated only because both move with a third, the useful analysis is partial correlation: the correlation between X and Y after the linear effect of the covariates is removed. Magic Stat does that in the same dialog.
prho that R's cor.test uses), not the t approximation, when there are no ties.Coefficient: Pearson · Spearman · Kendall.
Interval: Fisher z for Pearson, Bonett–Wright for Spearman, bootstrap for Kendall — matched automatically to the coefficient you chose.
Partial correlation: choose the covariates to control for; the result is the correlation of the residuals.
Pearson if both variables are continuous and the relationship is roughly linear. Spearman if the relationship is monotonic but not linear, or the data are skewed or ordinal. Kendall if there are many ties or the sample is small and you want a coefficient defined in terms of concordant pairs.
There is no universal threshold, and treating one as if there were is a common mistake. What matters is the coefficient together with its confidence interval and the sample size. Magic Stat labels the strength for convenience, but the interval is the honest part.
No, and no software can make it. If you suspect a third variable drives both, use partial correlation to hold it constant — that still does not establish a causal direction.
No. If you already work in R and it serves you, keep it — R's cor.test is the reference this dialog is built to match. The value here is the interval, the partial correlation and the report in one place.
No. The analysis runs on your computer. The only signal the app sends automatically is an anonymous installation counter — no data, no names, no e-mail.
Linear regression → Robust regression → Descriptive statistics →