Calculate the square of the Pearson product moment correlation coefficient.

`RSQ(known_y's, known_x's)`

- array1 - required, a number or name
- array2 - required, a number or name

`=RSQ(A3:A9, B3:B9)`

The RSQ function can be used to calculate the square of the Pearson product moment correlation coefficient between two sets of data points. For example, if you have data points in cells A3:A9 and B3:B9, you can use the formula above to calculate the coefficient.

`=RSQ(A3:A9, B3:B9)`

The RSQ function is used to measure the strength of the linear relationship between two sets of data points. For example, if you have data points in cells A3:A9 and B3:B9, you can use the RSQ function to measure the strength of the linear relationship between them. You can do this by entering the formula above into a cell.

`=RSQ(A3:A9, B3:B9)`

The RSQ function can be used to determine whether two sets of data points are related. If the returned result is close to 1, then the two sets of data points are strongly related. On the other hand, if the result is close to 0, then the two sets of data points are weakly related. For example, if you want to determine the relationship between data points in A3:A9 and B3:B9, you can use the RSQ function by entering the example formula into a cell.

The RSQ function calculates the square of the Pearson product moment correlation coefficient, which is the proportion of the variance in y that is attributable to the variance in x. It requires two arguments that must be numbers, arrays, or references.

- The RSQ function calculates the square of the Pearson product moment correlation coefficient, measuring the proportion of variance in y that is attributable to the variance in x.
- The RSQ function accepts numerical, array, and reference arguments, but ignores logical values, text, and empty cells.
- If known_y's and known_x's are empty or have a different number of data points, the RSQ function throws errors.

The RSQ function calculates the square of the Pearson product moment correlation coefficient. This is a measure of how well a linear equation can describe the relationship between two sets of data.

The r-squared value tells you the proportion of the variance in the dependent variable that is explained by the independent variable. A higher r-squared value indicates a better fit.

The RSQ function takes two arguments. These can be numbers, names, arrays, or references containing numbers.

Yes, the RSQ function ignores logical values, text, and empty cells in arguments.

- The RSQ function throws errors if known_y's and known_x's are empty or have different numbers of data points.
- The RSQ function throws an error if known_y's and known_x's are only 1 data point.