ENCODEURL(text)

Returns a URL-encoded string.

FILTERXML(xml, xpath)

Returns specific data from the XML content by using the xpath.

WEBSERVICE(url)

Returns data from a web service.

ACCRINT(issue, first_interest, settlement, rate, par, frequency, basis)

Returns the accrued interest for a security settlement that pays periodic interest.

ACCRINTM(issue, settlement, rate, par, basis)

Returns the accrued interest for a security that pays interest at maturity.

AMORDEGRC(cost, date_purchased, first_period, salvage, period, rate, basis)

Returns the depreciation for each accounting period. This function is provided for the French accounting system. If an asset is purchased in the middle of the accounting period, the prorated depreciation is taken into account. The function is similar to AMORLINC, except that a depreciation coefficient is applied in the calculation, depending on the life of the assets.

AMORLINC(cost, date_purchased, first_period, salvage, period, rate, basis)

Returns the depreciation for each accounting period. This function is provided for the French accounting system. If an asset is purchased in the middle of the accounting period, the prorated depreciation is taken into account.

COUPDAYBS(settlement, maturity, frequency, basis)

Returns the number of days from the beginning of the coupon period to the settlement date.

COUPDAYS(settlement, maturity, frequency, basis)

Returns the number of days in the coupon period that contains the settlement date.

COUPDAYSNC(settlement, maturity, frequency, basis)

Returns the number of days from the settlement date to the next coupon date.

COUPNCD(settlement, maturity, frequency, basis)

Returns a number that represents the next coupon date after the settlement date. To view the number as a date, you select Date in the Number Format drop-down menu on the Home tab.

COUPNUM(settlement, maturity, frequency, basis)

Returns the number of coupons payable between the settlement date and maturity date, rounded up to the nearest whole coupon.

COUPPCD(settlement, maturity, frequency, basis)

Returns a number that represents the previous coupon date before the settlement date. To view the number as a date, choose a date from the Number Format drop-down menu on the home tab.

CUMIPMT(rate, nper, pv, start_period, end_period, type)

Returns the cumulative interest paid on a loan between start_period and end_period.

CUMPRINC(rate, nper, pv, start_period, end_period, type)

Returns the cumulative principal paid on a loan between start_period and end_period.

DB(cost, salvage, life, period, month)

Returns the depreciation of an asset for a specified period, using the fixed-declining balance method.

DDB(cost, salvage, life, period, factor)

Returns the depreciation of an asset for a specified period, using the double-declining-balance method or some other specified method.

DISC(settlement, maturity, pr, redemption, basis)

Returns the discount rate for a security.

DOLLARDE(fractional_dollar, fraction)

Converts a dollar price expressed as a fraction into a dollar price expressed as a decimal number. Use DOLLARDE to convert fractional dollar numbers, such as securities prices, to decimal numbers.

DOLLARFR(decimal_dollar, fraction)

Converts a dollar price expressed as a decimal number into a dollar price expressed as a fraction. Use DOLLARFR to convert decimal numbers to fractional dollar numbers, such as securities prices.

DURATION(settlement, maturity, coupon yld, frequency, basis)

Returns the Macaulay duration for an assumed par value of $100. The duration is defined as the weighted average of the present value of the cash flows and is used as a measure of a bond price’s response to changes in yield.

EFFECT(nominal_rate, npery)

Returns the effective annual interest rate, given the nominal annual interest rate and the number of compounding periods per year.

FV(rate, nper, pmt, pv, type)

Returns the future value of an investment, based on periodic, constant payments and a constant interest rate.

FVSCHEDULE(principal, schedule)

Returns the future value of an initial principal after applying a series of compound interest rates. Use FVSCHEDULE to calculate the future value of an investment with a variable or adjustable rate.

INTRATE(settlement, maturity, investment, redemption, basis)

Returns the interest rate for a fully invested security.

IPMT(rate, per, nper, pv, fv, type)

Returns the interest payment for a given period for an investment, based on periodic, constant payments and a constant interest rate. For a more complete description of the arguments in IPMT and more information about annuity functions, see PV.

IRR(values, guess)

Returns the internal rate of return for a series of cash flows represented by the numbers in values. These cash flows do not have to be even, as they would be for an annuity. However, the cash flows must occur at regular intervals, such as monthly or annually. The internal rate of return is the interest rate received for an investment consisting of payments (negative values) and income (positive values) that occur at regular periods.

ISPMT(rate, per, nper, pv)

Calculates the interest paid during a specific period of an investment. This function is provided for compatibility with Lotus 1-2-3.

MDURATION(settlement, maturity, coupon, yld, frequency, basis)

Returns the modified duration for a security coupon with an assumed par value of $100.

MIRR(values, finance_rate, reinvest_rate)

Returns the modified internal rate of return for a series of periodic cash flows. MIRR considers both the cost of the investment and the interest received on reinvestment of cash.

NOMINAL(effect_rate, npery)

Returns the nominal annual interest rate, given the effective rate and the number of compounding periods per year.

NPER(rate, pmt, pv, fv, type)

Returns the number of periods for an investment, based on periodic, constant payments and a constant interest rate.

NPV(rate, value1, value2, ...)

Calculates the net present value of an investment by using a discount rate and a series of future payments (negative values) and income (positive values).

ODDFPRICE(settlement, maturity, issue, first_coupon, rate, yld, redemption, frequency, basis)

Returns the price per $100 face value of a security having an odd (short or long) first period.

ODDFYIELD(settlement, maturity, issue, first_coupon, rate, pr, redemption, frequency, basis)

Returns the yield of a security that has an odd (short or long) first period.

ODDLPRICE(settlement, maturity, last_interest, rate, yld, redemption, frequency, basis)

Returns the price per $100 face value of a security having an odd (short or long) last coupon period.

ODDLYIELD(settlement, maturity, last_interest, rate, pr, redemption, frequency, basis)

Returns the yield of a security that has an odd (short or long) last period.

PDURATION(rate, pv, fv)

Returns the number of periods required by an investment to reach a specified value.

PMT(rate, nper, pv, fv, type)

Calculates the payment for a loan based on constant payments and a constant interest rate.

PPMT(rate, per, nper, pv, fv, type)

Returns the payment on the principal for a given period for an investment based on periodic, constant payments and a constant interest rate.

PRICE(settlement, maturity, rate, yld, redemption, frequency, basis)

Returns the price per $100 face value of a security that pays periodic interest.

PRICEDISC(settlement, maturity, discount, redemption, basis)

Returns the price per $100 face value of a discounted security.

PRICEMAT(settlement, maturity, issue, rate, yld, basis)

Returns the price per $100 face value of an issue security that pays interest at maturity.

PV(rate, nper, pmt, fv, type)

Returns the present value of an investment. The present value is the total amount that a series of future payments is worth now. For example, when you borrow money, the loan amount is the present value to the lender.

RATE(nper, pmt, pv, fv, type, guess)

Returns the interest rate per period of an annuity. RATE is calculated by iteration and can have zero or more solutions. If the successive results of RATE do not converge to within 0.0000001 after 20 iterations, RATE returns a NUM! error.

RECEIVED(settlement, maturity, investment, discount, basis)

Returns the amount received at maturity for a fully invested security.

RRI(nper, pv, fv)

Returns an equivalent interest rate for the growth of an investment.

SLN(cost, salvage, life)

Returns the straight-line depreciation of an asset for one period.

SYD(cost, salvage, life, per)

Returns the sum-of-years’-digits depreciation of an asset for a specified period.

TBILLEQ(settlement, maturity, discount)

Returns the bond-equivalent yield for a Treasury bill (T-bill).

TBILLPRICE(settlement, maturity, discount)

Returns the price per $100 face value for a T-bill.

TBILLYIELD(settlement, maturity, pr)

Returns the yield for a T-bill.

VDB(cost, salvage, life, start_period, end_period, factor, no_switch)

Returns the depreciation of an asset for any specified period, including partial periods, using the double-declining-balance method or some other specified method. VDB stands for variable declining balance.

XIRR(values, dates, guess)

Returns the internal rate of return for a schedule of cash flows that is not necessarily periodic. To calculate the internal rate of return for a series of periodic cash flows, use the IRR function.

XNPV(rate, values, dates)

Returns the net present value for a schedule of cash flows that is not necessarily periodic. To calculate the net present value for a series of cash flows that is periodic, use the NPV function.

YIELD(settlement, maturity, rate, pr, redemption, frequency, basis)

Returns the yield on a security that pays periodic interest. You use YIELD to calculate bond yield.

YIELDDISC(settlement, maturity, pr, redemption, basis)

Returns the annual yield for a discounted security.

YIELDMAT(settlement, maturity, issue, rate, pr, basis)

Returns the annual yield of a security that pays interest at maturity.

AVEDEV(number1, number2, ...)

Returns the average of the absolute deviations of data points from their mean. AVEDEV is a measure of the variability in a data set.

AVERAGE(number1, number2, ...)

Returns the average (arithmetic mean) of the arguments.

AVERAGEA(value1, value2, ...)

Calculates the average (arithmetic mean) of the values in the list of arguments. In addition to numbers, text and logical values, such as TRUE and FALSE, are included in the calculation.

BETA.DIST(x, alpha, beta, cumulative, A, B)

Returns the cumulative beta probability density function. The cumulative beta probability density function is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.

BETA.INV(probability, alpha, beta, A, B)

Returns the inverse of the cumulative beta probability density function. That is, if probability is equal to BETADIST(x,...), then BETA.INV(probability,...) is equal to x. You can use the cumulative beta distribution in project planning to model probable completion times, given an expected completion time and variability.

BINOM.DIST(number_s, trials, probability_s, cumulative)

Returns the individual term binomial distribution probability. You use BINOM.DIST in problems with a fixed number of tests or trials, when the outcomes of any trial are only success or failure, when trials are independent, and when the probability of success is constant throughout the experiment. For example, BINOM.DIST can calculate the probability that two of the next three babies born will be male.

BINOM.DIST.RANGE(trials, probability_s, number_s, [number_s2])

Returns the probability of a trial result using a binomial distribution.

BINOM.INV(trials, probability_s, alpha)

Returns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value. You use this function for quality assurance applications. For example, you can use BINOM.INV to determine the greatest number of defective parts that are allowed to come off an assembly line run without having to reject the entire lot.

CHISQ.DIST(x, degrees_freedom, cumulative)

Returns the one-tailed probability of the chi-squared distribution. The chi-squared distribution is associated with a chi-squared test. You use the chi-squared test to compare observed and expected values. For example, in a genetic experiment, you 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.

CHISQ.DIST.RT(x, degrees_freedom)

Returns the right-tailed probability of the chi-squared distribution.

CHISQ.INV(probability, degrees_freedom)

Returns the inverse of the one-tailed probability of the chi-squared distribution. If the probability is equal to CHISQ.DIST(x,...), then CHISQ.INV(probability,...) is x. You use this function to compare observed results with expected ones to decide whether your original hypothesis is valid.

CHISQ.INV.RT(probability, degrees_freedom)

Returns the inverse of the right-tailed probability of the chi-squared distribution.

CHISQ.TEST(actual_range, expected_range)

Returns the test for independence. CHISQ.TEST returns the value from the chi-squared distribution for the statistic and the appropriate degrees of freedom. You can use chi-squared tests to determine whether hypothesized results are verified by an experiment.

CONFIDENCE.NORM(alpha, standard_dev, size)

Returns the confidence interval for a population mean. The confidence interval is a range on either side of a sample mean. For example, if you order a product through the mail, you can determine, with a particular level of confidence, the earliest and latest the product will arrive. Uses standard normal distribution.

CONFIDENCE.T(alpha, standard_dev, size)

Returns the confidence interval based on the student’s t-distribution.

CORREL(array1, array2)

Returns the correlation coefficient of the array1 and array2 cell ranges. You use the correlation coefficient to determine the relationship between two properties. For example, you can examine the relationship between a location’s average temperature and the use of air conditioners.

COVARIANCE.P(array1, array2)

Returns covariance, the average of the products of deviations for each data point pair. You use covariance to determine the relationship between two data sets. For example, you can examine whether greater income accompanies greater levels of education. Based on a population.

COVARIANCE.S(array1, array2)

Returns covariance, the average of the products of deviations for each data point pair. You use covariance to determine the relationship between two data sets. For example, you can examine whether greater income accompanies greater levels of education. Based on a sample.

DEVSQ(number1, number2, ...)

Returns the sum of squares of deviations of data points from their sample mean.

EXPON.DIST(x, lambda, cumulative)

Returns the exponential distribution. You use EXPON.DIST to model the time between events, such as how long a bank’s automated teller machine takes to deliver cash. For example, you can use EXPON.DIST to determine the probability that the process takes, at most, 1 minute.

F.DIST(x, degrees_freedom1, degrees_freedom2, cumulative)

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 test scores given to men and women entering high school and determine whether the variability in the females is different from that found in the males.

F.DIST.RT(x, degrees_freedom1, degrees_freedom2)

Returns the right-tailed F probability distribution.

F.INV(probability, degrees_freedom1, degrees_freedom2)

Returns the inverse of the F probability distribution. If the probability is equal to F.DIST(x,...), then F.INV(probability,...) is equal to x.

F.INV.RT(probability, degrees_freedom1, degrees_freedom2)

Returns the inverse of the right-tailed F probability distribution.

F.TEST(array1, array2)

Returns the result of an F-test. An F-test returns the one-tailed probability that the variances in array1 and array2 are not significantly different. You use this function to determine whether the two samples have different variances. For example, given test scores from public and private schools, you can test whether those schools have different levels of diversity.

FISHER(x)

Returns the Fisher transformation at x. This transformation produces a function that is approximately normally distributed rather than skewed. You use this function to perform hypothesis testing on the correlation coefficient.

FISHERINV(y)

Returns the inverse of the Fisher transformation. You use this transformation when analyzing correlations between ranges or arrays of data. If y is equal to FISHER(x), then FISHERINV(y) is equal to x.

FORECAST(x, known_y's, known_x's)

Calculates, or predicts, a future value by using existing values. The predicted value is a y value for a given x value. The known values are existing x values and y values, and the new value is predicted by using linear regression. You can use this function to predict future sales, inventory requirements, or consumer trends.

FORECAST.ETS(target_date, values, timeline, seasonality, data_completion)

Returns the forecasted value for a specific future target date using the exponential smoothing method. New in Excel 2019.

FORECAST.ETS.CONFINT(target_date, values, timeline, confidence_level, seasonality)

Returns a confidence interval for the forecast value at the specified target date. New in Excel 2019.

FORECAST.ETS.SEASONALITY(values, timeline, data_completion, aggregation)

Returns the length of the repetitive pattern Microsoft Excel detects for the specified time series. New in Excel 2019.

FORECAST.ETS.STAT(values, timeline, statistic_type, seasonality, data_completion)

Returns the requested statistic for the forecast. The eight available statistics are Alpha, Beta, Gamma, mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAE), root mean squared error metric (RMSE), and step size. New in Excel 2019.

FORECAST.LINEAR(x, known_y's, known_x's)

Calculates, or predicts, a future value by using existing values. The predicted value is a y value for a given x value. The known values are existing x values and y values, and the new value is predicted by using linear regression. You can use this function to predict future sales, inventory requirements, or consumer trends. This is a new name for FORECAST in Excel 2019.

FREQUENCY(data_array, bins_array)

Calculates how often values occur within a range of values and returns a vertical array of numbers. For example, you can use FREQUENCY to count the number of test scores that fall within ranges of scores. Because FREQUENCY returns an array, it must be entered as an array formula.

GAMMA(x)

Returns the gamma function value.

GAMMA.DIST(x, alpha, beta, cumulative)

Returns the gamma distribution. You can use this function to study variables that might have a skewed distribution. The gamma distribution is commonly used in queuing analysis.

GAMMA.INV(probability, alpha, beta)

Returns the inverse of the gamma cumulative distribution. If the probability is equal to GAMMA.DIST(x,...), then GAMMA.INV (probability,...) is equal to x.

GAMMALN(x)

Returns the natural logarithm of the gamma function.

GAUSS(x)

Returns 0.5 less than the standard normal curve distribution.

GEOMEAN(number1, number2, ...)

Returns the geometric mean of an array or a range of positive data. For example, you can use GEOMEAN to calculate average growth rate given compound interest with variable rates.

GROWTH(known_y's, known_x's, new_x's, const)

Calculates predicted exponential growth by using existing data. GROWTH returns the y values for a series of new x values that you specify by using existing x values and y values. You can also use the GROWTH worksheet function to fit an exponential curve to existing x values and y values.

HARMEAN(number1, number2, ...)

Returns the harmonic mean of a data set. The harmonic mean is the reciprocal of the arithmetic mean of reciprocals.

HYPGEOM.DIST(sample_s, number_sample, population_s, number_population)

Returns the hypergeometric distribution. HYPGEOM.DIST returns the probability of a given number of sample successes, given the sample size, population successes, and population size. You use HYPGEOM.DIST for problems with a finite population, in which each observation is either a success or a failure, and each subset of a given size is chosen with equal likelihood.

INTERCEPT(known_y's, known_x's)

Calculates the point at which a line will intersect the y-axis by using existing x values and y values. The intercept point is based on a best-fit regression line plotted through the known x values and known y values. You use the intercept when you want to determine the value of the dependent variable when the independent variable is 0. For example, you can use the INTERCEPT function to predict a metal’s electrical resistance at 0 degrees Celsius when your data points were taken at room temperature or higher.

KURT(number1, number2, ...)

Returns the kurtosis of a data set. Kurtosis characterizes the relative peakedness or flatness of a distribution compared with the normal distribution. Positive kurtosis indicates a relatively peaked distribution. Negative kurtosis indicates a relatively flat distribution.

LARGE(array, k)

Returns the kth largest value in a data set. You can use this function to select a value based on its relative standing. For example, you can use LARGE to return a highest, runner-up, or third-place score.

LINEST(known_y's, known_x's, const, stats)

Calculates the statistics for a line by using the least-squares method to calculate a straight line that best fits the data and returns an array that describes the line. Because this function returns an array of values, it must be entered as an array formula.

LOGEST(known_y's, known_x's, const, stats)

In regression analysis, calculates an exponential curve that fits the data and returns an array of values that describes the curve. Because this function returns an array of values, it must be entered as an array formula.

LOGNORM.DIST(x, mean, standard_dev, cumulative)

Returns the cumulative lognormal distribution of x, in which LN(x) is normally distributed with the parameters mean and standard_dev. You use this function to analyze data that has been logarithmically transformed.

LOGNORM.INV(probability, mean, standard_dev)

Returns the inverse of the lognormal cumulative distribution function of x, where LN(x) is normally distributed with the parameters mean and standard_dev. If the probability is equal to LOGNORM.DIST(x,...), LOGNORM.INV(probability,...) is equal to x.

MAX(number1, number2, ...)

Returns the largest value in a set of values.

MAXA(value1, value2, ...)

Returns the largest value in a list of arguments. Text and logical values such as TRUE and FALSE are compared, as are numbers.

MEDIAN(number1, number2, ...)

Returns the median of the given numbers. The median is the number in the middle of a set of numbers; that is, half the numbers have values that are greater than the median and half have values that are less.

MIN(number1, number2, ...)

Returns the smallest number in a set of values.

MINA(value1, value2, ...)

Returns the smallest value in a list of arguments. Text and logical values such as TRUE and FALSE are compared, as are numbers.

MODE.MULT(number1, number2, ...)

Returns a vertical array of the most frequently occurring, or repetitive, values in an array or a range of data. MODE.MULT was new in Excel 2010 and handles the specific case when there are two or more values that are tied for the most frequently occurring value. Whereas MODE.SNGL returns only the first mode value, MODE.MULT returns all the mode values.

MODE.SNGL(number1, number2, ...)

Returns the most frequently occurring, or repetitive, value in an array or a range of data. Like MEDIAN, MODE.SNGL is a location measure. If there are two values that are tied for the most frequently occurring value, only the first one will be returned by MODE.SNGL. If you need to return all of the tied values, use the new MODE.MULT.

NEGBINOM.DIST(number_f, number_s, probability_s, cumulative)

Returns the negative binomial distribution. NEGBINOM.DIST returns the probability that there will be number_f failures before the number_sth success when the constant probability of a success is probability_s. This function is similar to the binomial distribution function, except that the number of successes is fixed and the number of trials is variable. As with the binomial distribution function, trials are assumed to be independent.

NORM.DIST(x, mean, standard_dev, cumulative)

Returns the normal cumulative distribution for the specified mean and standard deviation. This function has a wide range of applications in statistics, including hypothesis testing.

NORM.INV(probability, mean, standard_dev)

Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation.

NORM.S.DIST(z)

Returns the standard normal cumulative distribution function. The distribution has a mean of zero and a standard deviation of one. You use this function in place of a table of standard normal curve areas.

NORM.S.INV(probability)

Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one.

PEARSON(array1, array2)

Returns the Pearson product–moment correlation coefficient, r, a dimensionless index that ranges from –1.0 to 1.0, inclusive, and reflects the extent of a linear relationship between two data sets.

PERCENTILE.EXC(array, k)

Returns the kth percentile of values in a range. You can use this function to establish a threshold of acceptance. For example, you can decide to examine candidates who score above the 90th percentile. PERCENTILE.EXC assumes the percentile is between 0 and 1, exclusive.

PERCENTILE.INC(array, k)

Returns the kth percentile of values in a range. PERCENTILE.INC assumes the percentile is between 0 and 1, inclusive.

PERCENTRANK.EXC(array, x, significance)

Returns the rank of a value in a data set as a percentage of the data set. You can use this function to evaluate the relative standing of a value within a data set. PERCENTRANK.EXC is renamed from PERCENTRANK. It assumes the percentile is between 0 and 1, exclusive.

PERCENTRANK.INC(array, x, significance)

Returns the rank of a value in a data set as a percentage of the data set. You can use this function to evaluate the relative standing of a value within a data set. For example, you can use PERCENTRANK.INC to evaluate the standing of an aptitude test score among all scores for the test. PERCENTRANK.INC assumes percentiles from 0 to 1, inclusive.

PERMUT(number, number_chosen)

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 in which internal order is significant. Permutations are different from combinations, for which the internal order is not significant. You use this function for lottery-style probability calculations.

PERMUTATIONA(number, number_chosen)

Returns the number of permutations for a given number of objects (with repetitions) that can be selected from the total objects.

PHI(x)

Returns the value of the density function for a standard normal distribution.

POISSON.DIST(x, mean, cumulative)

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.

PROB(x_range, prob_range, lower_limit, upper_limit)

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.

QUARTILE.EXC(array, quart)

Returns the quartile of a data set. Quartiles are often used in sales and survey data to divide populations into groups. For example, you can use QUARTILE.EXC to find the top 25% of incomes in a population. This function assumes percentiles run from 0 to 1, exclusive.

QUARTILE.INC(array, quart)

Returns the quartile of a data set. Quartiles are often used in sales and survey data to divide populations into groups. This function assumes percentiles run from 0 to 1, inclusive.

RANK.AVG(number, ref, order)

Returns the rank of a number in a list of numbers. The rank of a number is its size relative to other values in a list. (If you were to sort the list, the rank of the number would be its position.) When two or more items are tied, RANK.AVG averages their ranks.

RANK.EQ(number, ref, order)

Returns the rank of a number in a list of numbers. When two or more items are tied, RANK.EQ assigns the lower rank to all items in the tie. Renamed from RANK in Excel 2010.

RSQ(known_y's, known_x's)

Returns the square of the Pearson product–moment correlation coefficient through data points in known_y's and known_x's. The r-squared value can be interpreted as the proportion of the variance in y attributable to the variance in x.

SKEW(number1, number2, ...)

Returns the skewness of a distribution. Skewness characterizes the degree of asymmetry of a distribution around its mean. Positive skewness indicates a distribution with an asymmetric tail extending toward more positive values. Negative skewness indicates a distribution with an asymmetric tail extending toward more negative values.

SKEW.P(number1, number2, ...)

Returns the skewness of a distribution based on a population. Skewness characterizes the degree of asymmetry of a distribution around its mean. Positive skewness indicates a distribution with an asymmetric tail extending toward more positive values. Negative skewness indicates a distribution with an asymmetric tail extending toward more negative values.

SLOPE(known_y's, known_x's)

Returns the slope of the linear regression line through data points in known_y's and known_x's. The slope is the vertical distance divided by the horizontal distance between any two points on the line, which is the rate of change along the regression line.

SMALL(array, k)

Returns the kth smallest value in a data set. You use this function to return values with a particular relative standing in a data set.

STANDARDIZE(x, mean, standard_dev)

Returns a normalized value from a distribution characterized by mean and standard_dev.

STDEV.P(number1, number2, ...)

Calculates standard deviation based on the entire population given as arguments. The standard deviation is a measure of how widely values are dispersed from the average value (that is, the mean).

STDEV.S(number1, number2, ...)

Estimates standard deviation based on a sample. The standard deviation is a measure of how widely values are dispersed from the average value (that is, the mean).

STDEVA(value1, value2, ...)

Estimates standard deviation based on a sample. The standard deviation is a measure of how widely values are dispersed from the average value (that is, the mean). Text and logical values such as TRUE and FALSE are included in the calculation.

STDEVPA(value1, value2, ...)

Calculates standard deviation based on the entire population given as arguments, including text and logical values. The standard deviation is a measure of how widely values are dispersed from the average value (that is, the mean).

STEYX(known_y's, known_x's)

Returns the standard error of the predicted y value for each x in the regression. The standard error is a measure of the amount of error in the prediction of y for an individual x.

SUMSQ(number1, number2, ...)

Returns the sum of the squares of the arguments.

SUMX2MY2(array_x, array_y)

Returns the sum of the difference of squares of corresponding values in two arrays.

SUMX2PY2(array_x, array_y)

Returns the sum of the sum of squares of corresponding values in two arrays. The sum of the sum of squares is a common term in many statistical calculations.

SUMXMY2(array_x, array_y)

Returns the sum of squares of differences of corresponding values in two arrays.

T.DIST(x, degrees_freedom, tails, cumulative)

Returns the percentage points (that is, probability) for the Student t-distribution, where a numeric value (x) is a calculated value of t for which percentage points are to be computed. The t-distribution is used in the hypothesis testing of small sample data sets. You use this function in place of a table of critical values for the t-distribution.

T.DIST.2T(x, degrees_freedom)

Returns the two-tailed probability for the Student t-distribution.

T.DIST.RT(x, degrees_freedom)

Returns the right-tailed probability for the Student t-distribution.

T.INV(probability, degrees_freedom)

Returns the t-value of the Student’s t-distribution as a function of the probability and the degrees of freedom.

T.INV.2T(probability, degrees_freedom)

Returns the right-tailed t-value of the Student’s t-distribution as a function of the probability and the degrees of freedom.

T.TEST(array1, array2, tails, type)

Returns the probability associated with a Student’s t-test. You use T.TEST to determine whether two samples are likely to have come from the same two underlying populations that have the same mean.

TREND(known_y's, known_x's, new_x's ,const)

Returns values along a linear trend. Fits a straight line (using the method of least squares) to the arrays known_y's and known_x's. Returns the y values along that line for the array of new_x's that you specify.

TRIMMEAN(array, percent)

Returns the mean of the interior of a data set. TRIMMEAN calculates the mean taken by excluding a percentage of data points from the top and bottom tails of a data set. You can use this function when you want to exclude outlying data from your analysis.

VAR.P(number1, number2, ...)

Calculates variance based on the entire population.

VAR.S(number1, number2, ...)

Estimates variance based on a sample.

VARA(value1, value2, ...)

Estimates variance based on a sample. In addition to numbers, text and logical values such as TRUE and FALSE are included in the calculation.

VARPA(value1, value2, ...)

Calculates variance based on the entire population. In addition to numbers, text and logical values such as TRUE and FALSE are included in the calculation.

WEIBULL.DIST(x, alpha, beta, cumulative)

Returns the Weibull distribution. You use this distribution in reliability analysis, such as to calculate a device’s mean time to failure.

Z.TEST(array, x, sigma)

Returns the two-tailed p value of a z-test. The z-test generates a standard score for x with respect to the data set, array, and returns the two-tailed probability for the normal distribution. You can use this function to assess the likelihood that a particular observation is drawn from a particular population.

ACOS(number)

Returns the arccosine of a number. The arccosine is the angle whose cosine is number. The returned angle is given in radians in the range 0 to π.

ACOSH(number)

Returns the inverse hyperbolic cosine of a number, which must be n greater than or equal to 1. The inverse hyperbolic cosine is the value whose hyperbolic cosine is number, so ACOSH(COSH(number)) equals number.

ACOT(number)

Returns the arccotangent of a number in radians, in the range of 0 to π.

ACOTH(number)

Returns the inverse hyperbolic cotangent of a number.

ASIN(number)

Returns the arcsine of a number. The arcsine is the angle whose sine is number. The returned angle is given in radians in the range –π / 2 to π / 2.

ASINH(number)

Returns the inverse hyperbolic sine of a number. The inverse hyperbolic sine is the value whose hyperbolic sine is number, so ASINH(SINH(number)) equals number.

ATAN(number)

Returns the arctangent of a number. The arctangent is the angle whose tangent is number. The returned angle is given in radians in the range –π / 2 to π / 2.

ATAN2(x_num, y_num)

Returns the arctangent of the specified x- and y-coordinates. The arctangent is the angle from the x-axis to a line containing the origin (0, 0) and a point with coordinates (x_num, y_num). The angle is given in radians between –π and π, excluding –π.

ATANH(number)

Returns the inverse hyperbolic tangent of a number. number must be between –1 and 1 (excluding –1 and 1). The inverse hyperbolic tangent is the value whose hyperbolic tangent is number, so ATANH(TANH(number)) equals number.

COS(number)

Returns the cosine of the given angle.

COSH(number)

Returns the hyperbolic cosine of a number.

COT(number)

Returns the cotangent of an angle.

COTH(number)

Returns the hyperbolic cotangent of a number.

CSC(number)

Returns the cosecant of a number.

CSCH(number)

Returns the hyperbolic cosecant of a number.

DEGREES(angle)

Converts radians into degrees.

LN(number)

Returns the natural logarithm of number. Natural logarithms are based on the constant e (2.71828182845904).

LOG(number, base)

Returns the logarithm of number to the specified base.

LOG10(number)

Returns the base-10 logarithm of number.

RADIANS(angle)

Converts degrees to radians.

SEC(number)

Returns the secant of an angle.

SECH(number)

Returns the hyperbolic secant of an angle.

SIN(number)

Returns the sine of the given angle.

SINH(number)

Returns the hyperbolic sine of number.

TAN(number)

Returns the tangent of the given angle.

TANH(number)

Returns the hyperbolic tangent of number.

MDETERM(array)

Returns the matrix determinant of an array.

MINVERSE(array)

Returns the inverse matrix for the matrix stored in an array.

MMULT(array1, array2)

Returns the matrix product of two arrays. The result is an array with the same number of rows as array1 and the same number of columns as array2.

MUNIT(dimension)

Returns the unit matrix for the specified dimension.

SERIESSUM(x, n, m, coefficients)

Returns the sum of a power series based on the formula SERIES(x,n,m,a) ≈ a₁xn + a₂x^(n+m) + a₃x^(n+2m) + ... + a_ix^(n+(i-1)m)

SUMPRODUCT(array1, array2, array3,...)

Multiplies corresponding components in the given arrays and returns the sum of those products.

BASE(number, radix, min_length)

Converts a number into a text representation with the given radix (base).

BESSELI(x, n)

Returns the modified Bessel function, which is equivalent to the BESSELJ function evaluated for purely imaginary arguments.

BESSELJ(x, n)

Returns the Bessel function of the first kind.

BESSELK(x, n)

Returns the modified Bessel function of the second kind, which is equivalent to the BESSELY functions evaluated for purely imaginary arguments.

BESSELY(x, n)

Returns the Bessel function of the second kind. This is the most commonly used form of the Bessel functions. This function provides solutions of the Bessel differential equation and are infinite at x=0. This function is sometimes called the Neumann function.

BIN2DEC(number)

Converts a binary number to decimal.

BIN2HEX(number, places)

Converts a binary number to hexadecimal.

BIN2OCT(number, places)

Converts a binary number to octal.

BITAND(number1, number2)

Returns a bitwise AND of two numbers.

BITLSHIFT(number, shift_amount)

Returns a number shifted left by shift_amount bits.

BITOR(number1, number2)

Returns a bitwise OR of two numbers.

BITRSHIFT(number, shift_amount)

Returns a number shifted right by shift_amount bits.

BITXOR(number1, number2)

Returns a bitwise Exclusive OR of two numbers.

COMPLEX(real_num, i_num, suffix)

Converts real and imaginary coefficients into a complex number in the form x + yi or x + yj. Use suffix to control whether “i” or “j” is used.

CONVERT(number, from_unit, to_unit)

Converts a number from one measurement system to another. For example, CONVERT can translate a table of distances in miles to a table of distances in kilometers.

DEC2BIN(number, places)

Converts a decimal number to binary.

DEC2HEX(number, places)

Converts a decimal number to hexadecimal.

DEC2OCT(number, places)

Converts a decimal number to octal.

DECIMAL(number, radix)

Converts a text representation of a number with a given base into a decimal number.

DELTA(number1, number2)

Tests whether two values are equal. Returns 1 if number1 = number2; returns 0 otherwise. You use this function to filter a set of values. For example, by summing several DELTA functions, you can calculate the count of equal pairs. This function is also known as the Kronecker Delta function.

ERF(lower_limit, upper_limit)

Returns the ERROR function integrated between lower_limit and upper_limit.

ERFC(x)

Returns the complementary ERF function integrated between x and infinity.

GESTEP(number, step)

Returns 1 if number is greater than or equal to step; otherwise, returns 0. You use this function to filter a set of values. For example, by summing several GESTEP functions, you can calculate the count of values that exceed a threshold.

HEX2BIN(number, places)

Converts a hexadecimal number to binary.

HEX2DEC(number)

Converts a hexadecimal number to decimal.

HEX2OCT(number, places)

Converts a hexadecimal number to octal.

IMABS(inumber)

Returns the absolute value (modulus) of a complex number in x + yi or x + yj text format.

IMAGINARY(inumber)

Returns the imaginary coefficient of a complex number in x + yi or x + yj text format.

IMARGUMENT(inumber)

Returns the argument 0 (theta), an angle expressed in radians.

IMCONJUGATE(inumber)

Returns the complex conjugate of a complex number in x + yi or x + yj text format.

IMCOS(inumber)

Returns the cosine of a complex number in x + yi or x + yj text format.

IMCOSH(inumber)

Returns the hyperbolic cosine of a complex number.

IMCOT(inumber)

Returns the cotangent of a complex number.

IMCSC(inumber)

Returns the cosecant of a complex number.

IMCSCH(inumber)

Returns the hyperbolic cosecant of a complex number.

IMDIV(inumber1, inumber2)

Returns the quotient of two complex numbers in x + yi or x + yj text format.

IMEXP(inumber)

Returns the exponential of a complex number in x + yi or x + yj text format.

IMLN(inumber)

Returns the natural logarithm of a complex number in x + yi or x + yj text format.

IMLOG10(inumber)

Returns the common logarithm (base-10) of a complex number in x + yi or x + yj text format.

IMLOG2(inumber)

Returns the base-2 logarithm of a complex number in x + yi or x + yj text format.

IMPOWER(inumber, number)

Returns a complex number in x + yi or x + yj text format raised to a power.

IMPRODUCT(inumber1, inumber2, ...)

Returns the product of 2 to 255 complex numbers in x + yi or x + yj text format.

IMREAL(inumber)

Returns the real coefficient of a complex number in x + yi or x + yj text format.

IMSEC(inumber)

Returns the secant of a complex number.

IMSECH(inumber)

Returns the hyperbolic secant of a complex number.

IMSIN(inumber)

Returns the sine of a complex number in x + yi or x + yj text format.

IMSINH(inumber)

Returns the hyperbolic sin of a complex number.

IMSQRT(inumber)

Returns the square root of a complex number in x + yi or x + yj text format.

IMSUB(inumber1, inumber2)

Returns the difference of two complex numbers in x + yi or x + yj text format.

IMSUM(inumber1, inumber2,...)

Returns the sum of two or more complex numbers in x + yi or x + yj text format.

IMTAN(inumber)

Returns the tangent of a complex number.

OCT2BIN(number, places)

Converts an octal number to binary.

OCT2DEC(number)

Converts an octal number to decimal.

OCT2HEX(number, places)

Converts an octal number to hexadecimal.