"uncorrelated", "strongly correlated" ... I hear these terms as basic concepts. Good to get some basic feel
1) One of the first "sound bites" is the covariance vs correlation definitions. I like http://en.wikipedia.org/wiki/Covariance_and_correlation. Between 2 series of data (X and Y), cov can be a very large num (like 580,189,272billion), which can't possibly reveal the strength of correlation between X and Y. In contrast, the correlation number (say, 0.8) is dimentionless, and
has a value between -1 and 1. This is intuitive.
* highly correlated means close to +1 or -1
* uncorrelated means 0.
2) Below I feel these are 2 similar definitions of the corr. Formally, this number is the Linear correlation between two variables X and
Y.
A) For the entire population,
B) For a sample taken from the population,
B2) The formula above is identical to the r definition on P612 [[Prem Mann]]
, where SS stands for sum-of-sqaures
B3) An equivalent (equally useful) formula is
3) r-squared is a standard measure of the goodness of a linear regression model
1) One of the first "sound bites" is the covariance vs correlation definitions. I like http://en.wikipedia.org/wiki/Covariance_and_correlation. Between 2 series of data (X and Y), cov can be a very large num (like 580,189,272billion), which can't possibly reveal the strength of correlation between X and Y. In contrast, the correlation number (say, 0.8) is dimentionless, and
has a value between -1 and 1. This is intuitive.
* highly correlated means close to +1 or -1
* uncorrelated means 0.
2) Below I feel these are 2 similar definitions of the corr. Formally, this number is the Linear correlation between two variables X and
Y.
A) For the entire population,
B) For a sample taken from the population,
B2) The formula above is identical to the r definition on P612 [[Prem Mann]]
, where SS stands for sum-of-sqauresB3) An equivalent (equally useful) formula is
SSxy = 
3) r-squared is a standard measure of the goodness of a linear regression model
