49 functions for this use case · out of 12 defined
Returns the gamma distribution. You can use this function to study variables that may have a skewed distribution. The gamma distribution is commonly used in queuing analysis.
BINOM.DIST returns the individual term binomial distribution probability.
Returns the number of combinations for a given number of items. Use COMBIN to determine the total possible number of groups for a given number of items.
Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation.
Returns the Weibull distribution. Use this distribution in reliability analysis, such as calculating a device's mean time to failure.
Returns the probability of a trial result using a binomial distribution.
Returns the inverse of the left-tailed probability of the chi-squared distribution. The chi-squared distribution is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.
Returns the inverse of the right-tailed probability of the chi-squared distribution. If probability = CHISQ.DIST.RT(x,...), then CHISQ.INV.RT(probability,...) = x. Use this function to compare observed results with expected ones in order to decide whether your original hypothesis is valid.
Calculates the probability that a member of a standard normal population will fall between the mean and z standard deviations from the mean.
Returns the inverse of the lognormal cumulative distribution function of x, where ln(x) is normally distributed with parameters Mean and Standard_dev. If p = LOGNORM.DIST(x,...) then LOGNORM.INV(p,...) = x. Use the lognormal distribution to analyze logarithmically transformed data.
Returns the Student's left-tailed t-distribution. The t-distribution is used in the hypothesis testing of small sample data sets. Use this function in place of a table of critical values for the t-distribution.
Returns the probability associated with a Student's t-Test. Use T.TEST to determine whether two samples are likely to have come from the same two underlying populations that have the same mean. Results from the test shows if the difference is statistically significant or from chance.
Returns the right-tailed probability of the chi-squared distribution. The χ2 distribution is associated with a χ2 test. Use the χ2 test to compare observed and expected values. For example, a genetic experiment might hypothesize that the next generation of plants will exhibit a certain set of colors. By comparing the observed results with the expected ones, you can decide whether your original hypothesis is valid.
Returns the negative binomial distribution, the probability that there will be Number_f failures before the Number_s-th success, with Probability_s probability of a success. This function is similar to the binomial distribution, except that the number of successes is fixed, and the number of trials is variable. Like the binomial, trials are assumed to be independent. For example, you need to find 10 people with excellent reflexes, and you know the probability that a candidate has these qualifications is 0.3. NEGBINOM.DIST calculates the probability that you will interview a certain number of unqualified candidates before finding all 10 qualified candidates.
NORM.S.DIST returns the standard normal distribution (has a mean of zero and a standard deviation of one). Use this function in place of a table of standard normal curve areas.
Returns the chi-squared distribution. The chi-squared distribution is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.
Returns the exponential distribution.
rticle describes the formula syntax and usage of the F.DIST.RT function in Microsoft Excel. Returns the (right-tailed) F probability distribution (degree of diversity) for two data sets. You can use this function to determine whether two data sets have different degrees of diversity. For example, you can examine the test scores of men and women entering high school and determine if the variability in the females is different from that found in the males.
Returns the number of permutations for a given number of objects (with repetitions) that can be selected from the total objects.
Returns the probability that values in a range are between two limits. If upper_limit is not supplied, returns the probability that values in x_range are equal to lower_limit.
Returns the left-tailed inverse of the Student's t-distribution.
Returns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value.
The CHISQ.TEXT function returns the test for independence. It returns the value from the chi-squared (χ2) distribution for the statistic and the appropriate degrees of freedom. You can use χ2 tests to determine whether hypothesized results are verified by an experiment.
Returns the F probability distribution. You can use this function to determine whether two data sets have different degrees of diversity. For example, you can examine the test scores of men and women entering high school, and determine if the variability in the females is different from that found in the males.
Returns the inverse of the Fisher transformation. Use this transformation when analyzing correlations between ranges or arrays of data. If y = FISHER(x), then FISHERINV(y) = x.
Returns the inverse of the gamma cumulative distribution. If p = GAMMA.DIST(x,...), then GAMMA.INV(p,...) = x. You can use this function to study a variable whose distribution may be skewed.
Returns the lognormal distribution of x, where ln(x) is normally distributed with parameters Mean and Standard_dev. Use this function to analyze data that has been logarithmically transformed.
Returns the normal distribution for the specified mean and standard deviation. This function has a very wide range of applications in statistics, including hypothesis testing. If mean or standard_dev is nonnumeric, NORM.DIST returns the #VALUE! error value.
Returns the two-tailed Student's t-distribution. The Student's t-distribution is used in the hypothesis testing of small sample data sets. Use this function in place of a table of critical values for the t-distribution.
Returns the right-tailed Student's t-distribution. The t-distribution is used in the hypothesis testing of small sample data sets. Use this function in place of a table of critical values for the t-distribution.
Returns the two-tailed inverse of the Student's t-distribution.
.DIST(x,alpha,beta,cumulative,[A],[B])
Returns the Fisher transformation at x. This transformation produces a function that is normally distributed rather than skewed. Use this function to perform hypothesis testing on the correlation coefficient.
Returns the probability of a given number of sample successes, given the sample size, population successes, and population size.
Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one.
.INV(probability,alpha,beta,[A],[B])
Returns the inverse of the F probability distribution. If p = F.DIST(x,...), then F.INV(p,...) = x. The F distribution can be used in an F-test that compares the degree of variability in two data sets. For example, you can analyze income distributions in the United States and Canada to determine whether the two countries have a similar degree of income diversity.
Use this function to determine whether two samples have different variances.
Return the gamma function value.
Returns the natural logarithm of the gamma function, Γ(x).
Returns the value of the density function for a standard normal distribution.
Returns the Poisson distribution. A common application of the Poisson distribution is predicting the number of events over a specific time, such as the number of cars arriving at a toll plaza in 1 minute.
Returns a normalized value from a distribution characterized by mean and standard_dev.
Returns the confidence interval for a population mean, using a Student's t distribution.
Returns the natural logarithm of the gamma function, Γ(x).
This article describes the formula syntax and usage of the Z.TEST, which returns the one-tailed P-value of a z-test.
Returns the inverse of the (right-tailed) F probability distribution. If p = F.DIST.RT(x,...), then F.INV.RT(p,...) = x. The F distribution can be used in an F-test that compares the degree of variability in two data sets. For example, you can analyze income distributions in the United States and Canada to determine whether the two countries have a similar degree of income diversity.
The confidence interval is a range of values. Your sample mean, x, is at the center of this range and the range is x ± CONFIDENCE.NORM. For example, if x is the sample mean of delivery times for products ordered through the mail, x ± CONFIDENCE.NORM is a range of population means. For any population mean, μ0, in this range, the probability of obtaining a sample mean further from μ0 than x is greater than alpha; for any population mean, μ0, not in this range, the probability of obtaining a sample mean further from μ0 than x is less than alpha. In other words, assume that we use x, standard_dev, and size to construct a two-tailed test at significance level alpha of the hypothesis that the population mean is μ0. Then we will not reject that hypothesis if μ0 is in the confidence interval and will reject that hypothesis if μ0 is not in the confidence interval. The confidence interval does not allow us to infer that there is probability 1 – alpha that our next package will take a delivery time that is in the confidence interval.
Returns the number of permutations for a given number of objects that can be selected from number objects. A permutation is any set or subset of objects or events where internal order is significant. Permutations are different from combinations, for which the internal order is not significant. Use this function for lottery-style probability calculations.