Pearson product-moment correlation coefficient | Wikipedia audio article
Channel: wikipedia tts
Date: 06/10/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Pearson_correlation_coefficient


00:01:34 1 Definition
00:03:10 1.1 For a population
00:07:54 1.2 For a sample
00:17:25 2 Mathematical properties
00:19:00 3 Interpretation
00:20:35 3.1 Geometric interpretation
00:21:23 3.2 Interpretation of the size of a correlation
00:22:59 4 Inference
00:24:34 4.1 Using a permutation test
00:26:10 4.2 Using a bootstrap
00:27:45 4.3 Testing using Student's it/i-distribution
00:29:21 4.4 Using the exact distribution
00:30:56 4.5 Using the Fisher transformation
00:32:31 5 In least squares regression analysis
00:34:06 6 Sensitivity to the data distribution
00:35:42 6.1 Existence
00:37:17 6.2 Sample size
00:40:28 6.3 Robustness
00:42:03 7 Variants
00:44:25 7.1 Adjusted correlation coefficient
00:46:00 7.2 Weighted correlation coefficient
00:58:43 7.3 Reflective correlation coefficient
00:59:31 7.4 Scaled correlation coefficient
01:01:07 7.5 Pearson's distance
01:02:42 7.6 Circular correlation coefficient
01:04:17 7.7 Partial correlation
01:05:53 8 Decorrelation of in/i random variables
01:09:52 9 Software Implementations
01:13:02 10 See also
01:16:12 11 References
01:18:35 12 External links
01:20:10 Circular correlation coefficient
01:20:56 {x1,...,xn} and Y
01:24:07 Partial correlation
01:25:43 Decorrelation of n random variables
01:28:06 Software Implementations
01:29:42 "pearson") in its "stats" package (also cor(x, y, method
01:31:17 See also



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SUMMARY
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In statistics, the Pearson correlation coefficient (PCC, pronounced ), also referred to as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC) or the bivariate correlation, is a measure of the linear correlation between two variables X and Y.  According to the Cauchy–Schwarz inequality it has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation. It is widely used in the sciences. It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s and for which the mathematical formula was derived and published by Auguste Bravais in 1844. The naming of the coefficient is thus an example of Stigler's Law.