Statistical Functions

This function calculates the linear depreciation of an asset for a specified period, following the French accounting system. Unlike AMORDEGRC() (which uses degressive depreciation), AMORLINC() applies a straight-line method, making it adaptable to other tax jurisdictions with minor adjustments. Syntax AMORLINC(Cost; Date; First_Period; Residual_Value; Period; Rate; Basis) Arguments

This function returns the one-tailed probability value for a Gauss test (normal distribution). For a given expected value of a random variable (μ0), the Z.TEST() function returns the probability that the sample mean would be greater than the average of observations in the data set

This function returns values of a Weibull-distributed random variable. It is typically used in reliability analysis, such as calculating the mean time to failure of a device. Syntax. WEIBULL.DIST(x; alpha; beta; cumulative) Arguments x (required): The value at which the

This function calculates the variance based on the entire population. Unlike VAR.P(), VARPA() includes numbers, text, and logical values (TRUE and FALSE) in its calculation. Syntax. VARPA(value1; value2; …) Arguments value1 (required) and value2 (optional) You can enter at least

This function estimates the variance based on a sample. Unlike VAR.S(), VARA() includes numbers, text, and logical values (TRUE/FALSE) in the calculation. Syntax. VARA(value1; value2; …) Arguments value1 (required) and value2 (optional) You can enter at least one and up

This function estimates the variance based on a sample of the population. VAR.S() measures how the data points are distributed around the mean. Syntax. VAR.S(number1; number2; …) Arguments number1 (required) and number2 (optional) You can enter at least one and

This function calculates the variance assuming that the entire data set represents the whole population. Syntax. VAR.P(number1; number2; …) Arguments number1 (required) and number2 (optional) You can enter at least one and up to 255 numeric arguments, which represent the

This function returns the average value of a data group, excluding the values from the top and bottom of the data set. TRIMMEAN() calculates the average of a subset of data points, excluding the smallest and largest values of the original

This function returns values along a linear trend. TREND() fits a straight line (using the least squares method) to the arrays of known_y’s and known_x’s. It then returns the corresponding y-values along that line for the specified array of new_x’s.

The T.TEST() function returns the probability (p-value) associated with a Student’s t-test. It evaluates whether the means of two data sets are statistically different from each other. Use this function to determine if the two samples likely come from populations

This function returns the t-value of the t-distribution based on a given probability and degrees of freedom. Syntax: T.INV.2T(probability; degrees_freedom) Arguments: probability (required) – The probability associated with the two-tailed Student’s t-distribution. degrees_freedom (required) – The number of degrees of freedom characterizing

This function returns the right-tailed Student’s t-distribution. The t-distribution is used for hypothesis testing with small sample data sets. Use this function instead of referring to a table of critical values for the t-distribution. Syntax: T.DIST.RT(x; degrees_freedom) Arguments: x(required): The numeric

This function returns the two-tailed distribution (1 – α) of a Student’s t-distributed random variable. The t-distribution is used for hypothesis testing with small sample data sets. Use this function instead of a table of critical values for the t-distribution.

The STEYX() function calculates the standard error of the predicted y-values for each corresponding x-value in a linear regression. This standard error quantifies the accuracy of the predictions — the smaller the standard error, the more reliable the regression model.

The STDEVPA() function calculates the standard deviation based on an entire population, including text and logical values in the calculation. Standard deviation measures how spread out values are from the mean. It gives insight into the consistency or variability of