Statistiskās funkcijas 2. daļa
F.DIST
Calculates the values of the left tail of the F distribution.
Sintakse
F.DIST(Number; DegreesFreedom1; DegreesFreedom2; Cumulative)
Number is the value for which the F distribution is to be calculated.
degreesFreedom1 is the degrees of freedom in the numerator in the F distribution.
degreesFreedom2 is the degrees of freedom in the denominator in the F distribution.
Cumulative = 0 or False calculates the density function Cumulative = 1 or True calculates the distribution.
Piemērs
=F.DIST(0.8;8;12;0) yields 0.7095282499.
=F.DIST(0.8;8;12;1) yields 0.3856603563.
F.DIST.RT
Calculates the values of the right tail of the F distribution.
Sintakse
F.DIST.RT(Number; DegreesFreedom1; DegreesFreedom2)
Number is the value for which the F distribution is to be calculated.
degreesFreedom1 is the degrees of freedom in the numerator in the F distribution.
degreesFreedom2 is the degrees of freedom in the denominator in the F distribution.
Piemērs
=F.DIST.RT(0.8;8;12) yields 0.6143396437.
F.INV
Returns the inverse of the cumulative F distribution. The F distribution is used for F tests in order to set the relation between two differing data sets.
Sintakse
F.INV(Number; DegreesFreedom1; DegreesFreedom2)
Number is probability value for which the inverse F distribution is to be calculated.
DegreesFreedom1 is the number of degrees of freedom in the numerator of the F distribution.
DegreesFreedom2 is the number of degrees of freedom in the denominator of the F distribution.
Piemērs
=F.INV(0.5;5;10) yields 0.9319331609.
F.INV.RT
Returns the inverse right tail of the F distribution.
Sintakse
F.INV.RT(Number; DegreesFreedom1; DegreesFreedom2)
Number is probability value for which the inverse F distribution is to be calculated.
DegreesFreedom1 is the number of degrees of freedom in the numerator of the F distribution.
DegreesFreedom2 is the number of degrees of freedom in the denominator of the F distribution.
Piemērs
=F.INV.RT(0.5;5;10) yields 0.9319331609.
F.TEST
Returns the result of an F test.
Sintakse
F.TEST(Data1; Data2)
Dati1 ir pirmais ierakstu masīvs.
Dati2 ir otrais ierakstu masīvs.
Piemērs
=F.TEST(A1:A30;B1:B12) calculates whether the two data sets are different in their variance and returns the probability that both sets could have come from the same total population.
FDIST
Calculates the values of an F distribution.
Sintakse
FDIST(Number; DegreesFreedom1; DegreesFreedom2)
Number is the value for which the F distribution is to be calculated.
degreesFreedom1 is the degrees of freedom in the numerator in the F distribution.
degreesFreedom2 is the degrees of freedom in the denominator in the F distribution.
Piemērs
=FDIST(0.8;8;12) yields 0.61.
FINV
Returns the inverse of the F probability distribution. The F distribution is used for F tests in order to set the relation between two differing data sets.
Sintakse
FINV(Number; DegreesFreedom1; DegreesFreedom2)
Number is probability value for which the inverse F distribution is to be calculated.
DegreesFreedom1 is the number of degrees of freedom in the numerator of the F distribution.
DegreesFreedom2 is the number of degrees of freedom in the denominator of the F distribution.
Piemērs
=FINV(0.5;5;10) yields 0.93.
FISHER
Returns the Fisher transformation for x and creates a function close to a normal distribution.
Sintakse
FISHER(Number)
Number is the value to be transformed.
Piemērs
=FISHER(0.5) yields 0.55.
FISHERINV
Returns the inverse of the Fisher transformation for x and creates a function close to a normal distribution.
Sintakse
FISHERINV(Number)
Number is the value that is to undergo reverse-transformation.
Piemērs
=FISHERINV(0.5) yields 0.46.
FTEST
Returns the result of an F test.
Sintakse
FTEST(Data1; Data2)
Dati1 ir pirmais ierakstu masīvs.
Dati2 ir otrais ierakstu masīvs.
Piemērs
=FTEST(A1:A30;B1:B12) calculates whether the two data sets are different in their variance and returns the probability that both sets could have come from the same total population.
GAMMA
Returns the Gamma function value. Note that GAMMAINV is not the inverse of GAMMA, but of GAMMADIST.
Sintakse
Number is the number for which the Gamma function value is to be calculated.
GAMMA.DIST
Returns the values of a Gamma distribution.
The inverse function is GAMMAINV or GAMMA.INV.
This function is identical to GAMMADIST and was introduced for interoperability with other office suites.
Sintakse
GAMMA.DIST(Number; Alpha; Beta; C)
Number is the value for which the Gamma distribution is to be calculated.
Alpha is the parameter Alpha of the Gamma distribution.
Beta is the parameter Beta of the Gamma distribution.
C (optional) = 0 or False calculates the density function C = 1 or True calculates the distribution.
Piemērs
=GAMMA.DIST(2;1;1;1) yields 0.86.
GAMMA.INV
Returns the inverse of the Gamma cumulative distribution GAMMADIST. This function allows you to search for variables with different distribution.
This function is identical to GAMMAINV and was introduced for interoperability with other office suites.
Sintakse
GAMMA.INV(Number; Alpha; Beta)
Number is the probability value for which the inverse Gamma distribution is to be calculated.
Alpha is the parameter Alpha of the Gamma distribution.
Beta is the parameter Beta of the Gamma distribution.
Piemērs
=GAMMA.INV(0.8;1;1) yields 1.61.
GAMMADIST
Returns the values of a Gamma distribution.
The inverse function is GAMMAINV.
Sintakse
GAMMADIST(Number; Alpha; Beta; C)
Number is the value for which the Gamma distribution is to be calculated.
Alpha is the parameter Alpha of the Gamma distribution.
Beta is the parameter Beta of the Gamma distribution.
C (optional) = 0 or False calculates the density function C = 1 or True calculates the distribution.
Piemērs
=GAMMADIST(2;1;1;1) yields 0.86.
GAMMAINV
Returns the inverse of the Gamma cumulative distribution GAMMADIST. This function allows you to search for variables with different distribution.
Sintakse
GAMMAINV(Number; Alpha; Beta)
Number is the probability value for which the inverse Gamma distribution is to be calculated.
Alpha is the parameter Alpha of the Gamma distribution.
Beta is the parameter Beta of the Gamma distribution.
Piemērs
=GAMMAINV(0.8;1;1) yields 1.61.
GAMMALN
Returns the natural logarithm of the Gamma function: G(x).
Sintakse
GAMMALN(Number)
Number is the value for which the natural logarithm of the Gamma function is to be calculated.
Piemērs
=GAMMALN(2) yields 0.
GAMMALN.PRECISE
Returns the natural logarithm of the Gamma function: G(x).
Sintakse
GAMMALN.PRECISE(Number)
Number is the value for which the natural logarithm of the Gamma function is to be calculated.
Piemērs
=GAMMALN.PRECISE(2) yields 0.
GAUSS
Returns the standard normal cumulative distribution.
It is GAUSS(x)=NORMSDIST(x)-0.5
Sintakse
GAUSS(Skaitlis)
Number is the value for which the value of the standard normal distribution is to be calculated.
Piemērs
=GAUSS(0.19) = 0.08
=GAUSS(0.0375) = 0.01
GEOMEAN
Returns the geometric mean of a sample.
Sintakse
GEOMEAN(Number1; Number2; ...; Number30)
Number1, Number2, ..., Number30 are numeric arguments or ranges that represent a random sample.
Piemērs
=GEOMEAN(23;46;69) = 41.79. The geometric mean value of this random sample is therefore 41.79.
HARMEAN
Returns the harmonic mean of a data set.
Sintakse
HARMEAN(Number1; Number2; ...; Number30)
Number1, Number2, ..., Number30 are up to 30 values or ranges, that can be used to calculate the harmonic mean.
Piemērs
=HARMEAN(23;46;69) = 37.64. The harmonic mean of this random sample is thus 37.64
HYPGEOM.DIST
Returns the hypergeometric distribution.
Sintakse
HYPGEOM.DIST(X; NSample; Successes; NPopulation; Cumulative)
X is the number of results achieved in the random sample.
NSample is the size of the random sample.
Successes is the number of possible results in the total population.
NPopulation is the size of the total population.
Cumulative : 0 or False calculates the probability density function. Other values or True calculates the cumulative distribution function.
Piemēri
=HYPGEOM.DIST(2;2;90;100;0) yields 0.8090909091. If 90 out of 100 pieces of buttered toast fall from the table and hit the floor with the buttered side first, then if 2 pieces of buttered toast are dropped from the table, the probability is 81%, that both will strike buttered side first.
=HYPGEOM.DIST(2;2;90;100;1) yields 1.
HYPGEOMDIST
Returns the hypergeometric distribution.
Sintakse
HYPGEOMDIST(X; NSample; Successes; NPopulation)
X is the number of results achieved in the random sample.
NSample is the size of the random sample.
Successes is the number of possible results in the total population.
NPopulation is the size of the total population.
Piemērs
=HYPGEOMDIST(2;2;90;100) yields 0.81. If 90 out of 100 pieces of buttered toast fall from the table and hit the floor with the buttered side first, then if 2 pieces of buttered toast are dropped from the table, the probability is 81%, that both will strike buttered side first.
TRIMMEAN
Returns the mean of a data set without the Alpha percent of data at the margins.
Sintakse
TRIMMEAN(Datums; Alfa)
Data is the array of data in the sample.
Alpha is the percentage of the marginal data that will not be taken into consideration.
Piemērs
=TRIMMEAN(A1:A50; 0.1) calculates the mean value of numbers in A1:A50, without taking into consideration the 5 percent of the values representing the highest values and the 5 percent of the values representing the lowest ones. The percentage numbers refer to the amount of the untrimmed mean value, not to the number of summands.
Z.TEST
Calculates the probability of observing a z-statistic greater than the one computed based on a sample.
Sintakse
Z.TEST(Data; mu; Sigma)
Data is the given sample, drawn from a normally distributed population.
mu is the known mean of the population.
Sigma (optional) is the known standard deviation of the population. If omitted, the standard deviation of the given sample is used.
Piemērs
=Z.TEST(A2:A20; 9; 2) returns the result of a z-test on a sample A2:A20 drawn from a population with known mean 9 and known standard deviation 2.
ZTEST
Calculates the probability of observing a z-statistic greater than the one computed based on a sample.
Sintakse
ZTEST(Data; mu; Sigma)
Data is the given sample, drawn from a normally distributed population.
mu is the known mean of the population.
Sigma (optional) is the known standard deviation of the population. If omitted, the standard deviation of the given sample is used.
See also the Wiki page.