The standard approach for evaluating any form of correlation by statistical means is to calculate the Pearson correlation coefficient using the following expression:<\/p>\r\n\r\n\r\n\r\n
r\\left( {x,y} \\right) = \\frac{1}{n} \\sum \\limits_{i = 1}^n \\left( {\\frac{{{x_i} – \\bar x}}{{{S_x}}}} \\right)\\left( {\\frac{{{y_i} – \\bar y}}{{{S_y}}}} \\right)<\/span><\/p>\r\n\r\n\r\n\r\n where:<\/p>\r\n\r\n\r\n\r\n Error correlation can sometimes be determined using this equation. However, care must be taken to ensure that we are really working out the error correlation and not just a correlation due to physical or geophysical processes. This means somehow distinguishing the error from the value, and this is only realistically possible if there is a way of independently estimating the error. Often this is not possible, meaning that we must use a Type B method of error correlation evaluation. However, in some instances there is a method by which the error can be independently estimated, as we\u2019ll see on the next page.<\/p>\r\n","protected":false},"excerpt":{"rendered":"\r\n