Therefore, the sign of the covariance shows the trend in the linear relationship between the variables. The magnitude of the covariance is not easy to interpret because it is not normalized and, therefore, depends on the magnitudes of the variables. The normalized version of the covariance, known as correlation coefficientDespite this, shows by its magnitude the strength of the linear linkage.
Therefore, a distinction must be made between:
- the covariance of two random variables, which is a population parameter that can be viewed as a property of the joint probability distribution; Y
- the sample covariance, which at the same time serves as a descriptor of the sample, it also serves as an estimated value of the population parameter.
This function returns the last covariance (sample), the average of the products of the deviations for each pair of data points in two data sets. Used to establish the link between two data sets. As an example, can examine whether higher earnings accompany higher levels of education.
The CORVARIANCE. S The function uses the following syntax to operate:
CORVARIANCE. S (matrix1, matriz2)
The CORVARIANCE. S The function has the following arguments:
- array1: this is required and represents the first whole number cell range
- array2: this is also necessary. This is the second whole number cell range.
It should be noted at the same time that:
- this function has replaced a previous function (COVAR) and should provide improved precision and a name that better reflects its use
- arguments must be numbers or names, arrays or references containing numbers
- if an array or reference argument contains text, logical values or empty cells, those values are ignored; despite this, cells with zero value are included
- And array1 Y array2 have a different number of data points, COVARIANCIA. S return the #N / A error value
- if any array1 O array2 it is empty, COVARIANCIA. S return the # DIV / 0! error value.
Please, see my example below: