The RSQ() function returns the square of the linear correlation coefficient. The syntax for the function is RSQ (known_y, known_x). The argument "known_y" and "known_x" can be an array of numerical values, references to a range of cells or a named range of which you want to find the square of the correlation coefficient. If the "known_y" and "known_x" contain a different number of data points and #N/A error will be returned. If there is only one data point in the the "known_y" and "known_x" array the function will return a #DIV/0 error value to the cell. The r squared function is a measure of the linear correlation coefficient which relates the proportion of variance of y and the variation x. This essentially describes how y changes when x changes. If RSQ is close to one then x anf y have a strong linear correlation and so can confindently be described as a linear using a linear equation or function. If RSQ is close to zero there is very little linear correlation between x and y meaning that it is not sensible to describe y using a linear equation or function involving x. To learn more about the correlation coefficient in mathematics see: [The Linear Correlation Mathematics Knowledgebase]. [The PEARSON() function knowledgebase]

How to use the RSQ() function: Type " =RSQ( " Enter the reference for the "known_y" values "A2:A10". Type a comma. Enter the refencence for the "known_x" values "B2:B10 ". Type")" then press the "Enter" key.

Note: Logical values are ecepted when entered directly into the function. However if the arrays contain elements that cannot be translated into numeric values the function will ignore those elements of the array.