Fonctions Statistiques
[ ] indicates optional parameters
BETA.INVERSE(probabilité; alpha; bêta; [A]; [B])BETA.INVERSE(probabilité; alpha; bêta; [A]; [B])
Returns the value associated with the specified cumulative beta distribution probability.
probabilité | The cumulative beta distribution probability for which you want the value. |
alpha | The alpha value. |
bêta | The beta value. |
A | The lower limit. If this parameter is omitted it defaults to 0. |
B | The upper limit. If this parameter is omitted it defaults to 1. |
Returns the kth percentile of a set of values.
plage | An array or reference to cells containing the values. |
k | The percentile value. |
CENTREE.REDUITE(x; espérance; écart_type)
Returns the standardized value of x for the specified mean and standard deviation.
x | The value that you want to standardize. |
espérance | The mean of the values. |
écart_type | The standard deviation of the values. |
COEFFICIENT.ASYMETRIE(nombre1; [nombre2; ...])
Returns the skewness of a set of numbers.
nombre1; ... | The numbers of which you want the skewness. |
COEFFICIENT.CORRELATION(plage1; plage2)
Returns the correlation coefficient of two ranges.
plage1 | The first range to be compared. |
plage2 | The second range to be compared. |
COEFFICIENT.DETERMINATION(y_range; x_range)
Returns the square of the Pearson correlation coefficient.
y_range | The first range to be compared. |
x_range | The second range to be compared. |
Returns the covariance of two ranges.
plage1 | The first range to be compared. |
plage2 | The second range to be compared. |
CRITERE.LOI.BINOMIALE(tirages; probabilité_succès; alpha)
Returns the value at which the cumulative binomial distribution is greater than or equal to alpha.
tirages | The total number of trials. |
probabilité_succès | The probability of a single trial being successful. |
alpha | The value at which you want to evaluate the function. |
CROISSANCE(y_connus; [x_connus]; [x_nouveaux]; [constante])
Returns the expected values of y for given x values for an exponential curve passing through a specified set of points.
y_connus | The y values that are already known. | ||||
x_connus | The x values that are already known. If this parameter is omitted it defaults to an array of values from 1 to the number of known ys. | ||||
x_nouveaux | The new x values for which y values are required. If this parameter is omitted it defaults to the known xs. | ||||
constante | Specifies whether the line must pass through the origin. The possible values are: | ||||
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If this parameter is omitted it defaults to VRAI. |
DROITEREG(y_connus; [x_connus]; [constante]; [statistiques])
Returns the coefficients for a straight line using multiple linear regression.
y_connus | The y values that are already known. | ||||
x_connus | One or more sets of x values corresponding to the known y values. If this parameter is omitted it defaults to an array of values from 1 to the number of known ys. | ||||
constante | Specifies whether the line must pass through the origin. The possible values are: | ||||
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If this parameter is omitted it defaults to VRAI. | |||||
statistiques | Specifies whether the additional statistics are returned in the rows below the coefficients. These are: the standard error values for the coefficients, the R2 coefficient, the standard error for the Y estimate, the F statistic, the degrees of freedom, the regression sum of squares and the residual sum of squares. The possible values are: | ||||
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If this parameter is omitted it defaults to FAUX. |
ECART.MOYEN(nombre1; [nombre2; ...])
Returns the average of the differences of a set of numbers from their mean.
nombre1; ... | The numbers of which you want the average deviation. |
ECARTYPE(nombre1; [nombre2; ...])
Returns the standard deviation (based on a population sample) of a set of numbers.
nombre1; ... | The numbers of which you want the standard deviation. |
ECARTYPEP(nombre1; [nombre2; ...])
Returns the standard deviation (based on the entire population) of a set of numbers.
nombre1; ... | The numbers of which you want the standard deviation. |
ERREUR.TYPE.XY(y_connus; x_connus)
Returns the standard error of the y values of a line passing through a specified set of points.
y_connus | The y values that are already known. |
x_connus | The x values that are already known. |
Returns the Fisher transformation.
x | The value at which to evaluate the function. |
Returns the inverse Fisher transformation.
y | The value at which to evaluate the function. |
FREQUENCE(tableau_données; matrice_intervalles)
Returns the counts of items in specified numeric categories.
tableau_données | An array or reference to a range of cells containing values to be counted. |
matrice_intervalles | An array or reference to a range of cells containing the upper limits for each category. |
Returns the kth largest number in a set of numbers.
plage | An array or reference to cells containing numbers of which you want the kth largest. |
k | The rank of the number that you want. |
INTERVALLE.CONFIANCE(alpha; écart_type; taille)
Returns the confidence interval for a population mean.
alpha | The significance level. |
écart_type | The population standard deviation. |
taille | The sample size. |
INVERSE.LOI.F(probabilité; degrés_liberté1; degrés_liberté2)
Returns the value associated with the specified F distribution probability.
probabilité | The probability for which you want the value. |
degrés_liberté1 | The degrees of freedom of the first set. |
degrés_liberté2 | The degrees of freedom of the second set. |
KHIDEUX.INVERSE(probabilité; degrés_liberté)
Returns the value associated with the specified chi-squared distribution probability.
probabilité | The probability for which you want the value. |
degrés_liberté | The number of degrees of freedom. |
KURTOSIS(nombre1; [nombre2; ...])
Returns the kurtosis of a set of numbers.
nombre1; ... | The numbers of which you want the kurtosis. |
Returns the natural logarithm of the gamma function evaluated at x.
x | The value at which you want to evaluate the function. |
LOGREG(y_connus; [x_connus]; [constante]; [statistiques])
Returns the coefficients for an exponential curve using multiple linear regression.
y_connus | The y values that are already known. | ||||
x_connus | One or more sets of x values corresponding to the known y values. If this parameter is omitted it defaults to an array of values from 1 to the number of known ys. | ||||
constante | Specifies whether the line must pass through the origin. The possible values are: | ||||
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If this parameter is omitted it defaults to VRAI. | |||||
statistiques | Specifies whether the additional statistics are returned in the rows below the coefficients. These are: the standard error values for the coefficients, the R2 coefficient, the standard error for the Y estimate, the F statistic, the degrees of freedom, the regression sum of squares and the residual sum of squares. The possible values are: | ||||
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If this parameter is omitted it defaults to FAUX. |
LOI.BETA(x; alpha; bêta; [A]; [B])
Returns the cumulative beta distribution probability.
x | The value at which you want to evaluate the function. |
alpha | The alpha value. |
bêta | The beta value. |
A | The lower limit. If this parameter is omitted it defaults to 0. |
B | The upper limit. If this parameter is omitted it defaults to 1. |
LOI.BINOMIALE(nombre_succès; tirages; probabilité_succès; cumulative)
Returns the binomial distribution probability.
nombre_succès | The number of trials that are successful. | ||||
tirages | The total number of trials. | ||||
probabilité_succès | The probability of a single trial being successful. | ||||
cumulative | Specifies whether to return the cumulative probability or not. The possible values are: | ||||
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LOI.BINOMIALE.NEG(nombre_f; nombre_s; probabilité_s)
Returns the negative binomial distribution probability.
nombre_f | The number of trials that fail. |
nombre_s | The threshold number of trials that are successful. |
probabilité_s | The probability of a single trial being successful. |
LOI.EXPONENTIELLE(x; lambda; cumulative)
Returns the exponential distribution probability.
x | The value at which you want to evaluate the function. | ||||
lambda | The lambda value. | ||||
cumulative | Specifies whether to return the cumulative probability or not. The possible values are: | ||||
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LOI.F(x; degrés_liberté1; degrés_liberté2)
Returns the F distribution probability.
x | The value at which you want to evaluate the function. |
degrés_liberté1 | The degrees of freedom of the first set. |
degrés_liberté2 | The degrees of freedom of the second set. |
LOI.GAMMA(x; alpha; bêta; cumulative)
Returns the gamma distribution probability.
x | The value at which you want to evaluate the function. | ||||
alpha | The alpha value. | ||||
bêta | The beta value. | ||||
cumulative | Specifies whether to return the cumulative probability or not. The possible values are: | ||||
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LOI.GAMMA.INVERSE(probabilité; alpha; bêta)
Returns the value associated with the specified gamma distribution probability.
probabilité | The probability for which you want the value. |
alpha | The alpha value. |
bêta | The beta value. |
LOI.HYPERGEOMETRIQUE(succès_échantillon; nombre_échantillon; succès_population; nombre_population)
Returns the hypergeometric distribution probability.
succès_échantillon | The number of sample trials that are successful. |
nombre_échantillon | The total number of trials in the sample. |
succès_population | The number of population trials that are successful. |
nombre_population | The total number of trials in the population. |
LOI.KHIDEUX(x; degrés_liberté)
Returns the chi-squared distribution probability.
x | The value at which you want to evaluate the function. |
degrés_liberté | The number of degrees of freedom. |
LOI.LOGNORMALE(x; espérance; écart_type)
Returns the cumulative lognormal distribution probability.
x | The value at which you want to evaluate the function. |
espérance | The mean of the natural logarithms of the values. |
écart_type | The standard deviation of the natural logarithms of the values. |
LOI.LOGNORMALE.INVERSE(probabilité; espérance; écart_type)
Returns the value associated with the specified cumulative lognormal distribution probability.
probabilité | The probability for which you want the value. |
espérance | The mean of the natural logarithms of the values. |
écart_type | The standard deviation of the natural logarithms of the values. |
LOI.NORMALE(x; espérance; écart_type; cumulative)
Returns the normal distribution probability.
x | The value at which you want to evaluate the function. | ||||
espérance | The mean of the values. | ||||
écart_type | The standard deviation of the values. | ||||
cumulative | Specifies whether to return the cumulative probability or not. The possible values are: | ||||
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LOI.NORMALE.INVERSE(probabilité; espérance; écart_type)
Returns the value associated with the specified cumulative normal distribution probability.
probabilité | The probability for which you want the value. |
espérance | The mean of the values. |
écart_type | The standard deviation of the values. |
Returns the cumulative standard normal distribution probability.
z | The value at which you want to evaluate the function. |
LOI.NORMALE.STANDARD.INVERSE(probabilité)
Returns the value associated with the specified cumulative standard normal distribution probability.
probabilité | The probability for which you want the value. |
LOI.POISSON(x; espérance; cumulative)
Returns the Poisson distribution probability.
x | The value at which you want to evaluate the function. | ||||
espérance | The mean of the values. | ||||
cumulative | Specifies whether to return the cumulative probability or not. The possible values are: | ||||
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LOI.STUDENT(x; degrés_liberté; uni/bilatéral)
Returns the Student's T distribution probability.
x | The value at which you want to evaluate the function. |
degrés_liberté | The degrees of freedom. |
uni/bilatéral | Specifies the tails to include in the distribution. Should be 1 or 2. |
LOI.STUDENT.INVERSE(probabilité; degrés_liberté)
Returns the value associated with the specified Student's T distribution probability.
probabilité | The probability for which you want the value. |
degrés_liberté | The degrees of freedom. |
LOI.WEIBULL(x; alpha; bêta; cumulative)
Returns the Weibull distribution probability.
x | The value at which you want to evaluate the function. | ||||
alpha | The alpha value. | ||||
bêta | The beta value. | ||||
cumulative | Specifies whether to return the cumulative probability or not. The possible values are: | ||||
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Returns the maximum of a set of numbers.
nombre1; ... | The numbers of which you want the maximum. |
Returns the maximum of a set of values.
valeur1; ... | The values of which you want the maximum. |
MEDIANE(nombre1; [nombre2; ...])
Returns the median of a set of numbers.
nombre1; ... | The numbers of which you want the median. |
Returns the minimum of a set of numbers.
nombre1; ... | The numbers of which you want the minimum. |
Returns the minimum of a set of values.
valeur1; ... | The values of which you want the minimum. |
Returns the mode of a set of numbers.
nombre1; ... | The numbers of which you want the mode. |
MOYENNE(nombre1; [nombre2; ...])
Returns the average of a set of numbers.
nombre1; ... | The numbers of which you want the average. |
MOYENNEA(valeur1; [valeur2; ...])
Returns the average of a set of values.
valeur1; ... | The values of which you want the average. |
MOYENNE.GEOMETRIQUE(nombre1; [nombre2; ...])
Returns the geometric mean of a set of numbers.
nombre1; ... | The numbers of which you want the geometric mean. |
MOYENNE.HARMONIQUE(nombre1; [nombre2; ...])
Returns the harmonic mean of a set of numbers.
nombre1; ... | The numbers of which you want the harmonic mean. |
MOYENNE.REDUITE(plage; pourcentage)
Returns the mean of a set of numbers with the extreme values removed.
plage | An array or reference to cells containing the numbers. |
pourcentage | The percentage of the numbers to exclude from the calculation. |
Returns the count of numbers in a list.
valeur1; ... | The items whose numbers are to be counted. |
NBVAL(valeur1; [valeur2; ...])
Returns the count of values in a list.
valeur1; ... | The items whose values are to be counted. |
ORDONNEE.ORIGINE(y_connus; x_connus)
Returns the expected value of y when x is zero for a line passing though a specified set of points.
y_connus | The y values that are already known. |
x_connus | The x values that are already known. |
Returns the Pearson correlation coefficient.
plage1 | The first range to be compared. |
plage2 | The second range to be compared. |
Returns the slope of a line passing through a specified set of points.
y_connus | The y values that are already known. |
x_connus | The x values that are already known. |
PERMUTATION(nombre; nombre_choisi)
Returns the number of permutations in which a number of items can be chosen from a total number.
nombre | The total number of items. |
nombre_choisi | The number of items chosen. |
Returns the kth smallest number in a set of numbers.
plage | An array or reference to cells containing numbers of which you want the kth smallest. |
k | The rank of the number that you want. |
PREVISION(x; y_connus; x_connus)
Returns the expected value of y for a given x value for a line passing through a specified set of points.
x | The x value at which to evaluate the function. |
y_connus | The y values that are already known. |
x_connus | The x values that are already known. |
PROBABILITE(plage_x; plage_probabilité; limite_inf; [limite_sup])
Returns the probability that numbers in a set are between the specified limits.
plage_x | An array or reference to cells containing the numbers. |
plage_probabilité | An array or reference to cells containing the probabilities associated with each number. These values must add up to 1. |
limite_inf | The lower limit of the test. |
limite_sup | The upper limit of the test. If this value is omitted it defaults to the value specified for the lower limit. |
Returns the specified quartile of a set of numbers.
plage | An array or reference to cells containing the numbers. | ||||||||||
quart | Specifies which quartile to return. The possible values are: | ||||||||||
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RANG(nombre; référence; [ordre])
Returns the rank of a number in a set of numbers.
nombre | The number of which you want the rank. | ||||
référence | An array or reference to cells containing the values. | ||||
ordre | Specifies whether the list is treated as being in ascending or descending order of value. The possible values are: | ||||
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If this parameter is omitted it defaults to 0. |
RANG.POURCENTAGE(matrice; x; [précision])
Returns the percentile of a value in a set of values.
matrice | An array or reference to cells containing the values. |
x | The value of which you want the percentile. |
précision | The number of decimal places required in the result. If this parameter is omitted it defaults to 3. |
SOMME.CARRES.ECARTS(nombre1; [nombre2; ...])
Returns the sum of the squares of the differences of a set of numbers from their mean.
nombre1; ... | The numbers of which you want the squared deviations. |
STDEVA(valeur1; [valeur2; ...])
Returns the standard deviation (based on a population sample) of a set of values.
valeur1; ... | The values of which you want the standard deviation. |
STDEVPA(valeur1; [valeur2; ...])
Returns the standard deviation (based on the entire population) of a set of values.
valeur1; ... | The values of which you want the standard deviation. |
TENDANCE(y_connus; [x_connus]; [x_nouveaux]; [constante])
Returns the expected values of y for given x values for a line passing through a specified set of points.
y_connus | The y values that are already known. | ||||
x_connus | The x values that are already known. If this parameter is omitted it defaults to an array of values from 1 to the number of known ys. | ||||
x_nouveaux | The new x values for which y values are required. If this parameter is omitted it defaults to the known xs. | ||||
constante | Specifies whether the line must pass through the origin. The possible values are: | ||||
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If this parameter is omitted it defaults to VRAI. |
Returns the probability result of the F test.
plage1 | The first range to be compared. |
plage2 | The second range to be compared. |
TEST.KHIDEUX(plage_réelle; plage_attendue)
Returns the probability result of the chi-squared test.
plage_réelle | An array or reference to cells containing the empirical results. |
plage_attendue | An array or reference to cells containing the theoretical results. |
TEST.STUDENT(matrice1; matrice2; uni/bilatéral; type)
Returns the probability result of the Student's T test.
matrice1 | The first range to be compared. | ||||||
matrice2 | The second range to be compared. | ||||||
uni/bilatéral | Specifies the tails to include in the distribution. Should be 1 or 2. | ||||||
type | Specifies which type of test is required. The possible values are: | ||||||
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Returns the probability result of the z test.
plage | An array or reference to cells containing the data against which x is to be tested. |
x | The value to be tested. |
sigma | The population standard deviation. If this parameter is omitted it defaults to the sample standard deviation of the data. |
Returns the variance (based on a population sample) of a set of numbers.
nombre1; ... | The numbers of which you want the variance. |
VAR.P(nombre1; [nombre2; ...])
Returns the variance (based on the entire population) of a set of numbers.
nombre1; ... | The numbers of which you want the variance. |
Returns the variance (based on a population sample) of a set of values.
valeur1; ... | The values of which you want the variance. |
VARPA(valeur1; [valeur2; ...])
Returns the variance (based on the entire population) of a set of values.
valeur1; ... | The values of which you want the variance. |