Statistical functions
The 147 statistical functions in Sheets, with the usual names, arguments and results.
| Function | What it does | Syntax |
|---|---|---|
| AVEDEV | Mean absolute deviation from the mean | AVEDEV(number1, [number2, ...]) |
| AVERAGEexample | Mean of values | AVERAGE(value1, [value2, ...]) |
| AVERAGEA | Mean counting text as 0 and TRUE as 1 | AVERAGEA(value1, [value2, ...]) |
| AVERAGEIFexample | Mean where one criterion matches | AVERAGEIF(range, criterion, [average_range]) |
| AVERAGEIFSexample | Conditional mean | AVERAGEIFS(avg_range, range1, criterion1, ...) |
| BETA.DIST | Beta distribution | BETA.DIST(x, alpha, beta, cumulative, [A], [B]) |
| BETA.INV | Inverse of the cumulative beta distribution | BETA.INV(probability, alpha, beta, [A], [B]) |
| BETADIST | Cumulative beta distribution (legacy) | BETADIST(x, alpha, beta, [A], [B]) |
| BETAINV | Inverse beta distribution (legacy) | BETAINV(probability, alpha, beta, [A], [B]) |
| BINOM.DIST | Binomial distribution probability | BINOM.DIST(number_s, trials, probability_s, cumulative) |
| BINOM.DIST.RANGE | Probability of a range of successes | BINOM.DIST.RANGE(trials, probability_s, number_s, [number_s2]) |
| BINOM.INV | Smallest k with binomial cdf >= alpha | BINOM.INV(trials, probability_s, alpha) |
| BINOMDIST | Binomial distribution (legacy) | BINOMDIST(number_s, trials, probability_s, cumulative) |
| CHIDIST | Right-tailed chi-squared (legacy) | CHIDIST(x, deg_freedom) |
| CHIINV | Inverse right-tailed chi-squared (legacy) | CHIINV(probability, deg_freedom) |
| CHISQ.DIST | Chi-squared distribution | CHISQ.DIST(x, deg_freedom, cumulative) |
| CHISQ.DIST.RT | Right-tailed chi-squared distribution | CHISQ.DIST.RT(x, deg_freedom) |
| CHISQ.INV | Inverse of the left-tailed chi-squared | CHISQ.INV(probability, deg_freedom) |
| CHISQ.INV.RT | Inverse of the right-tailed chi-squared | CHISQ.INV.RT(probability, deg_freedom) |
| CHISQ.TEST | Chi-squared test of independence | CHISQ.TEST(actual_range, expected_range) |
| CHITEST | Chi-squared independence test (legacy) | CHITEST(actual_range, expected_range) |
| CONFIDENCE | Normal confidence interval (legacy) | CONFIDENCE(alpha, standard_dev, size) |
| CONFIDENCE.NORM | Confidence interval, normal distribution | CONFIDENCE.NORM(alpha, standard_dev, size) |
| CONFIDENCE.T | Confidence interval, Student's t | CONFIDENCE.T(alpha, standard_dev, size) |
| CORREL | Correlation coefficient of two data sets | CORREL(array1, array2) |
| COUNTexample | Count numbers | COUNT(value1, [value2, ...]) |
| COUNTAexample | Count non-empty | COUNTA(value1, [value2, ...]) |
| COUNTBLANK | Count empty cells | COUNTBLANK(range) |
| COUNTIFexample | Count where one criterion matches | COUNTIF(range, criterion) |
| COUNTIFSexample | Conditional count | COUNTIFS(range1, criterion1, ...) |
| COVAR | Population covariance | COVAR(array1, array2) |
| COVARIANCE.P | Population covariance | COVARIANCE.P(array1, array2) |
| COVARIANCE.S | Sample covariance | COVARIANCE.S(array1, array2) |
| CRITBINOM | Inverse binomial distribution (legacy) | CRITBINOM(trials, probability_s, alpha) |
| DEVSQ | Sum of squared deviations from the mean | DEVSQ(number1, [number2, ...]) |
| EXPON.DIST | Exponential distribution | EXPON.DIST(x, lambda, cumulative) |
| EXPONDIST | Exponential distribution (legacy) | EXPONDIST(x, lambda, cumulative) |
| F.DIST | F probability distribution | F.DIST(x, deg_freedom1, deg_freedom2, cumulative) |
| F.DIST.RT | Right-tailed F distribution | F.DIST.RT(x, deg_freedom1, deg_freedom2) |
| F.INV | Inverse of the F distribution | F.INV(probability, deg_freedom1, deg_freedom2) |
| F.INV.RT | Inverse of the right-tailed F distribution | F.INV.RT(probability, deg_freedom1, deg_freedom2) |
| F.TEST | F-test of two variances | F.TEST(array1, array2) |
| FDIST | Right-tailed F distribution (legacy) | FDIST(x, deg_freedom1, deg_freedom2) |
| FINV | Inverse right-tailed F (legacy) | FINV(probability, deg_freedom1, deg_freedom2) |
| FISHER | Fisher transformation | FISHER(x) |
| FISHERINV | Inverse of the Fisher transformation | FISHERINV(y) |
| FORECAST | Value at x on the linear trend | FORECAST(x, known_y's, known_x's) |
| FORECAST.ETS | Forecast by exponential smoothing | FORECAST.ETS(target_date, values, timeline, [seasonality], [data_completion], [aggregation]) |
| FORECAST.ETS.CONFINT | Confidence interval of an ETS forecast | FORECAST.ETS.CONFINT(target_date, values, timeline, [confidence_level], [seasonality], [data_completion], [aggregation]) |
| FORECAST.ETS.SEASONALITY | Season length found in a series | FORECAST.ETS.SEASONALITY(values, timeline, [data_completion], [aggregation]) |
| FORECAST.ETS.STAT | Statistic of an ETS forecast model | FORECAST.ETS.STAT(values, timeline, statistic_type, [seasonality], [data_completion], [aggregation]) |
| FORECAST.LINEAR | Value at x on the linear trend | FORECAST.LINEAR(x, known_y's, known_x's) |
| FREQUENCY | Count of values per bin, as a column | FREQUENCY(data_array, bins_array) |
| FTEST | F-test of two variances (legacy) | FTEST(array1, array2) |
| GAMMA | Gamma function | GAMMA(number) |
| GAMMA.DIST | Gamma distribution | GAMMA.DIST(x, alpha, beta, cumulative) |
| GAMMA.INV | Inverse of the cumulative gamma distribution | GAMMA.INV(probability, alpha, beta) |
| GAMMADIST | Gamma distribution (legacy) | GAMMADIST(x, alpha, beta, cumulative) |
| GAMMAINV | Inverse gamma distribution (legacy) | GAMMAINV(probability, alpha, beta) |
| GAMMALN | Natural log of the gamma function | GAMMALN(x) |
| GAMMALN.PRECISE | Natural log of the gamma function | GAMMALN.PRECISE(x) |
| GAUSS | Standard normal cdf minus 0.5 | GAUSS(z) |
| GEOMEAN | Geometric mean | GEOMEAN(number1, [number2, ...]) |
| GROWTH | Values along an exponential trend | GROWTH(known_y's, [known_x's], [new_x's], [const]) |
| HARMEAN | Harmonic mean | HARMEAN(number1, [number2, ...]) |
| HYPGEOM.DIST | Hypergeometric distribution | HYPGEOM.DIST(sample_s, number_sample, population_s, number_pop, cumulative) |
| HYPGEOMDIST | Hypergeometric probability (legacy) | HYPGEOMDIST(sample_s, number_sample, population_s, number_pop) |
| INTERCEPT | Y-intercept of the linear regression line | INTERCEPT(known_y's, known_x's) |
| KURT | Kurtosis of a sample | KURT(number1, [number2, ...]) |
| LARGE | The k-th largest value | LARGE(array, k) |
| LINEST | Linear regression coefficients and statistics | LINEST(known_y's, [known_x's], [const], [stats]) |
| LOGEST | Exponential curve fit and statistics | LOGEST(known_y's, [known_x's], [const], [stats]) |
| LOGINV | Inverse lognormal distribution (legacy) | LOGINV(probability, mean, standard_dev) |
| LOGNORM.DIST | Lognormal distribution | LOGNORM.DIST(x, mean, standard_dev, cumulative) |
| LOGNORM.INV | Inverse of the lognormal distribution | LOGNORM.INV(probability, mean, standard_dev) |
| LOGNORMDIST | Cumulative lognormal distribution (legacy) | LOGNORMDIST(x, mean, standard_dev) |
| MAXexample | Maximum value | MAX(value1, [value2, ...]) |
| MAXA | Largest value, counting text and logicals | MAXA(value1, [value2, ...]) |
| MAXIFSexample | Largest value where all criteria match | MAXIFS(max_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...) |
| MEDIANexample | Middle value | MEDIAN(value1, [value2, ...]) |
| MINexample | Minimum value | MIN(value1, [value2, ...]) |
| MINA | Smallest value, counting text and logicals | MINA(value1, [value2, ...]) |
| MINIFSexample | Smallest value where all criteria match | MINIFS(min_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...) |
| MODE | Most frequent value | MODE(number1, [number2, ...]) |
| MODE.MULT | All most frequent values, as a column | MODE.MULT(number1, [number2, ...]) |
| MODE.SNGL | Most frequent value | MODE.SNGL(number1, [number2, ...]) |
| NEGBINOM.DIST | Negative binomial distribution | NEGBINOM.DIST(number_f, number_s, probability_s, cumulative) |
| NEGBINOMDIST | Negative binomial probability (legacy) | NEGBINOMDIST(number_f, number_s, probability_s) |
| NORM.DIST | Normal distribution | NORM.DIST(x, mean, standard_dev, cumulative) |
| NORM.INV | Inverse of the normal distribution | NORM.INV(probability, mean, standard_dev) |
| NORM.S.DIST | Standard normal distribution | NORM.S.DIST(z, cumulative) |
| NORM.S.INV | Inverse of the standard normal distribution | NORM.S.INV(probability) |
| NORMDIST | Normal distribution (legacy) | NORMDIST(x, mean, standard_dev, cumulative) |
| NORMINV | Inverse normal distribution (legacy) | NORMINV(probability, mean, standard_dev) |
| NORMSDIST | Cumulative standard normal (legacy) | NORMSDIST(z) |
| NORMSINV | Inverse standard normal (legacy) | NORMSINV(probability) |
| PEARSON | Pearson correlation coefficient | PEARSON(array1, array2) |
| PERCENTILE | The k-th percentile, k from 0 to 1 | PERCENTILE(array, k) |
| PERCENTILE.EXC | The k-th percentile, k between 0 and 1 | PERCENTILE.EXC(array, k) |
| PERCENTILE.INCexample | The k-th percentile, k from 0 to 1 | PERCENTILE.INC(array, k) |
| PERCENTRANK | Rank of x as a fraction from 0 to 1 | PERCENTRANK(array, x, [significance]) |
| PERCENTRANK.EXC | Rank of x as a fraction between 0 and 1 | PERCENTRANK.EXC(array, x, [significance]) |
| PERCENTRANK.INC | Rank of x as a fraction from 0 to 1 | PERCENTRANK.INC(array, x, [significance]) |
| PHI | Standard normal density | PHI(x) |
| POISSON | Poisson distribution (legacy) | POISSON(x, mean, cumulative) |
| POISSON.DIST | Poisson distribution | POISSON.DIST(x, mean, cumulative) |
| PROB | Probability that values lie between limits | PROB(x_range, prob_range, [lower_limit], [upper_limit]) |
| QUARTILE | Quartile 0 to 4 of a data set | QUARTILE(array, quart) |
| QUARTILE.EXC | Quartile 1 to 3, ends excluded | QUARTILE.EXC(array, quart) |
| QUARTILE.INC | Quartile 0 to 4 of a data set | QUARTILE.INC(array, quart) |
| RANK | Rank of a number in a list | RANK(number, ref, [order]) |
| RANK.AVG | Rank in a list; ties get the average rank | RANK.AVG(number, ref, [order]) |
| RANK.EQ | Rank in a list; ties share the top rank | RANK.EQ(number, ref, [order]) |
| RSQ | Square of the Pearson correlation | RSQ(known_y's, known_x's) |
| SKEW | Skewness of a sample | SKEW(number1, [number2, ...]) |
| SKEW.P | Skewness of a population | SKEW.P(number1, [number2, ...]) |
| SLOPE | Slope of the linear regression line | SLOPE(known_y's, known_x's) |
| SMALL | The k-th smallest value | SMALL(array, k) |
| STANDARDIZE | Normalized value (z-score) | STANDARDIZE(x, mean, standard_dev) |
| STDEV | Sample standard deviation | STDEV(number1, [number2, ...]) |
| STDEV.P | Population standard deviation | STDEV.P(number1, [number2, ...]) |
| STDEV.S | Sample standard deviation | STDEV.S(number1, [number2, ...]) |
| STDEVA | Sample standard deviation, text as 0 | STDEVA(value1, [value2, ...]) |
| STDEVP | Population standard deviation | STDEVP(number1, [number2, ...]) |
| STDEVPA | Population standard deviation, text as 0 | STDEVPA(value1, [value2, ...]) |
| STEYX | Standard error of the predicted y | STEYX(known_y's, known_x's) |
| T.DIST | Left-tailed Student's t distribution | T.DIST(x, deg_freedom, cumulative) |
| T.DIST.2T | Two-tailed Student's t distribution | T.DIST.2T(x, deg_freedom) |
| T.DIST.RT | Right-tailed Student's t distribution | T.DIST.RT(x, deg_freedom) |
| T.INV | Left-tailed inverse of Student's t | T.INV(probability, deg_freedom) |
| T.INV.2T | Two-tailed inverse of Student's t | T.INV.2T(probability, deg_freedom) |
| T.TEST | Student's t-test probability | T.TEST(array1, array2, tails, type) |
| TDIST | Student's t distribution (legacy) | TDIST(x, deg_freedom, tails) |
| TINV | Two-tailed inverse of Student's t (legacy) | TINV(probability, deg_freedom) |
| TREND | Values along a linear trend | TREND(known_y's, [known_x's], [new_x's], [const]) |
| TRIMMEAN | Mean without the top and bottom values | TRIMMEAN(array, percent) |
| TTEST | Student's t-test probability (legacy) | TTEST(array1, array2, tails, type) |
| VAR | Sample variance | VAR(number1, [number2, ...]) |
| VAR.P | Population variance | VAR.P(number1, [number2, ...]) |
| VAR.S | Sample variance | VAR.S(number1, [number2, ...]) |
| VARA | Sample variance, text as 0 | VARA(value1, [value2, ...]) |
| VARP | Population variance | VARP(number1, [number2, ...]) |
| VARPA | Population variance, text as 0 | VARPA(value1, [value2, ...]) |
| WEIBULL | Weibull distribution (legacy) | WEIBULL(x, alpha, beta, cumulative) |
| WEIBULL.DIST | Weibull distribution | WEIBULL.DIST(x, alpha, beta, cumulative) |
| Z.TEST | One-tailed z-test probability | Z.TEST(array, x, [sigma]) |
| ZTEST | One-tailed z-test probability (legacy) | ZTEST(array, x, [sigma]) |