# Interval estimation, correlation measures, and statistical tests

We briefly covered interval estimation as an introductory example of SciPy: `bayes_mvs`

, in Chapter 1, *Introduction to SciPy*, with very simple syntax, as follows:

bayes_mvs(data, alpha=0.9)

It returns a tuple of three arguments in which each argument has the form `(center, (lower, upper))`

. The first argument refers to the mean; the second refers to the variance; and the third to the standard deviation. All intervals are computed according to the probability given by `alpha`

, which is `0.9`

by default.

We may use the `linregress`

routine to compute the regression line of some two-dimensional data *x*, or two sets of one-dimensional data, *x* and *y*. We may compute different correlation coefficients, with their corresponding p-values, as well. We have the **Pearson correlation coefficient** (`pearsonr`

), **Spearman's rank-order correlation** (`spearmanr`

), **point biserial correlation** (`pointbiserialr`

), and **Kendall's tau** for ordinal data (`kendalltau...`