Calculates the covariance of a dataset.
data_y- The range representing the array or matrix of dependent data.
data_x- The range representing the array or matrix of independent data.
Any text encountered in the
valuearguments will be ignored.
Positive covariance indicates that the independent data and dependent data tend to change together in the same direction; negative indicates that they tend to change together in the opposite direction (i.e. increase in one leads to decrease in the other). The magnitude of covariance is difficult to interpret - use
PEARSON, the normalized version of
COVAR, to gauge strength of linear correlation.
STEYX: Calculates the standard error of the predicted y-value for each x in the regression of a dataset.
SLOPE: Calculates the slope of the line resulting from linear regression of a dataset.
RSQ: Calculates the square of r, the Pearson product-moment correlation coefficient of a dataset.
INTERCEPT: Calculates the y-value at which the line resulting from linear regression of a dataset will intersect the y-axis (x=0).
FORECAST: Calculates the expected y-value for a specified x based on a linear regression of a dataset.
COVAR: Calculates the covariance of a dataset.
CORREL: Calculates r, the Pearson product-moment correlation coefficient of a dataset.