#### Overview of this book

Learning SciPy for Numerical and Scientific Computing Second Edition
Credits
www.PacktPub.com
Preface
Free Chapter
Introduction to SciPy
Working with the NumPy Array As a First Step to SciPy
SciPy for Linear Algebra
SciPy for Numerical Analysis
SciPy for Signal Processing
SciPy for Data Mining
SciPy for Computational Geometry
Interaction with Other Languages
Index

## 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`). In all...