| function name |
function description |
grammatical form |
| AVEDEV |
return a set of data with the average absolute deviation of the average, that is, dispersion. |
AVEDEV (number1, number2, ...) |
| AVERAGE |
arithmetic mean return parameter. |
AVERAGE (number1, number2, ...) |
| AVERAGEA |
list of numerical parameters of the average (arithmetic average). Not only the figures, and text and logical values (such as TRUE and FALSE) will also be taken into account. |
AVERAGEA (value1, value2 ,...) |
| BETADIST |
return the cumulative distribution function Beta function. Beta cumulative distribution function is usually used to study samples of the collection of certain things and the changes occurred. |
BETADIST (x, alpha, beta, A, B) |
| BETAINV |
return beta cumulative distribution function of the inverse function. That is, if the probability = BETADIST (x ,...), then BETAINV (probability ,...) = x. beta cumulative distribution function can be used for project design, in a given time and look forward to the completion of changes in parameters, the simulation of possible completion time. |
BETAINV (probability, alpha, beta, A, B) |
| BINOMDIST |
return a dollar value of the binomial probability distribution. |
BINOMDIST (number_s, trials, probability_s, cumulative) |
| CHIDIST |
return ¦Ã2 one-tailed probability distribution. ¦Ã2 and ¦Ã2 test related to the distribution. ¦£2 can compare the use of test observations and expectations. |
CHIDIST (x, degrees_freedom) |
| CHIINV |
return ¦Ã2 one-tailed probability distribution of the inverse function. |
CHIINV (probability, degrees_freedom) |
| CHITEST |
return value of the independence test. CHITEST function return value ¦Ã2 statistical distribution and the corresponding degree of freedom. |
CHITEST (actual_range, expected_range) |
| CONFIDENCE |
overall average return of the confidence interval. Confidence interval is the average of either side of the sample area. |
CONFIDENCE (alpha, standard_dev, size) |
| CORREL |
return array1 and array2 range of the correlation coefficient between. The use of correlation coefficient to determine the relationship between two attributes. |
CORREL (array1, array2) |
| COUNT |
return the number of parameters. COUNT function can be calculated using the array or range in the number of digital items. |
COUNT (value1, value2, ...) |
| COUNTA |
parameters group to return to Africa the number of null value. COUNTA function can be calculated using the array or range in the number of data items. |
COUNTA (value1, value2, ...) |
| COVAR |
return covariance, that is, each pair of data points the average deviation of the product, the use of covariance can decide between the two data sets relationship. |
COVAR (array1, array2) |
| CRITBINOM |
return so that the cumulative binomial distribution of greater than or equal to the minimum threshold. This function can be used for quality inspection. |
CRITBINOM (trials, probability_s, alpha) |
| DEVSQ |
data points to return to their respective sample mean and the sum of squares deviation. |
DEVSQ (number1, number2 ,...) |
| EXPONDIST |
return to exponential distribution. EXPONDIST function can be used to establish the time interval between the events model. |
EXPONDIST (x, lambda, cumulative) |
| FDIST |
return F probability distribution. Use this function to determine the existence of the two data series of different changes in extent. |
FDIST (x, degrees_freedom1, degrees_freedom2) |
| FINV |
back to F of the inverse probability distribution function. |
FINV (probability, degrees_freedom1, degrees_freedom2) |
| FISHER |
return point x of the Fisher transform. Generate a transformation similar to the normal distribution rather than a function of deflection. |
FISHER (x) |
| FISHERINV |
return to the inverse Fisher transform function. Transformation can be analyzed using this data or the correlation between array. |
FISHERINV (y) |
| FORECAST |
in accordance with the given data or to predict the future value. |
FORECAST (x, known_y's, known_x's) |
| FREQUENCY |
to return to a vertical array of data in a regional frequency distribution. |
FREQUENCY (data_array, bins_array) |
| FTEST |
return the results of F tests. F test to return to the array when the array 1 and the variance 2, when no significant difference between the one-tailed probability. You can use this function to determine whether the variance of the two different samples. |
FTEST (array1, array2) |
| GAMMADIST |
return to gamma distribution. You can use this function to skew the distribution of study variables. Gamma distribution is usually used for queuing analysis. |
GAMMADIST (x, alpha, beta, cumulative) |
| GAMMAINV |
return the cumulative gamma distribution function of the inverse function. |
GAMMAINV (probability, alpha, beta) |
| GAMMALN |
gamma return the natural logarithm function, ¦£ (x). |
GAMMALN (x) |
| GEOMEAN |
return to positive data array or the geometric mean. |
GEOMEAN (number1, number2, ...) |
| GROWTH |
data in accordance with a given value of exponential growth projections. |
GROWTH (known_y's, known_x's, new_x's, const) |
| HARMEAN |
data set to return to the harmonic average. Countdown to reconcile with the arithmetic average of the average of each countdown. |
HARMEAN (number1, number2, ...) |
| HYPGEOMDIST |
return hypergeometric distribution. |
HYPGEOMDIST (sample_s, number_sample,
population_s, number_population) |
| INTERCEPT |
known x value and y value of a straight line with the y-axis intercept. |
INTERCEPT (known_y's, known_x's) |
| KURT |
data set to return to the peak. |
KURT (number1, number2, ...) |
| LARGE |
data set to return in the first k-max. Can use this function to select the relative standard values. |
LARGE (array, k) |
| LINEST |
calculated using the least square method the best known data fitting a straight line and return to describe the linear array. |
LINEST (known_y's, known_x's, const, stats) |
| LOGEST |
in regression analysis, the calculation of the best observational data group index regression curve fitting, and return to describe the curve of the array. |
LOGEST (known_y's, known_x's, const, stats) |
| LOGINV |
return x to the cumulative log-normal distribution function of the inverse function. |
LOGINV (probability, mean, standard_dev) |
| LOGNORMDIST |
return x of the cumulative lognormal distribution function. |
LOGNORMDIST (x, mean, standard_dev) |
| MAX |
return the greatest concentration of numerical data. |
MAX (number1, number2 ,...) |
| MAXA |
parameters to return to the list of the greatest value. |
MAXA (value1, value2 ,...) |
| MEDIAN |
return to a given set of median values. The median in a set of data in the middle of a few. |
MEDIAN (number1, number2, ...) |
| MIN |
set the parameters back to the table minimum. |
MIN (number1, number2, ...) |
| MINA |
parameter list to return to the minimum value. |
MINA (value1, value2 ,...) |
| MODE |
return array or data in a frequency region up to values. |
MODE (number1, number2, ...) |
| NEGBINOMDIST |
return a negative binomial distribution. |
NEGBINOMDIST (number_f, number_s, probability_s) |
| NORMDIST |
will be returned to the mean and standard deviation of the cumulative normal distribution function. |
NORMDIST (x, mean, standard_dev, cumulative) |
| NORMINV |
will be returned to the mean and standard deviation of the cumulative distribution function of the inverse function. |
NORMINV (probability, mean, standard_dev) |
| NORMSDIST |
return the cumulative standard normal distribution function, the distribution of average 0, standard deviation of 1. |
NORMSDIST (z) |
| NORMSINV |
cumulative standard normal distribution function to return the inverse function. The distribution of the average 0, standard deviation of 1. |
NORMSINV (probability) |
| PEARSON |
back to Pearson (Napier Health) the product moment correlation coefficient, r, this is a range of between -1.0 to 1.0 (including -1.0 and 1.0 included) of the non-dimensional index, the two data sets reflects the linear correlation between the degree of. |
PEARSON (array1, array2) |
| PERCENTILE |
return value of the K region numerical point percentage. You can use this function to create acceptable threshold. For example, to determine the top scoring 90 percentage points in the detection of candidates. |
PERCENTILE (array, k) |
| PERCENTRANK |
returned to a specific numerical data in a percentage of qualifying. This function can be used to view specific data in the data location. For example, you can use the calculation function PERCENTRANK the ability of a particular test of the ability to score in all of the location of the test scores. |
PERCENTRANK (array, x, significance) |
| PERMUT |
return from a given number of objects in the collection of a number of objects selected with a few. Order can be the subject of an internal sequence of events or for any collection or a subset. And combination with different combinations of internal order meaningless. This function can be used for the calculation of the probability of lottery tickets. |
PERMUT (number, number_chosen) |
| POISSON |
return to Poisson distribution. Poisson distribution is usually a period of time used to predict the number of incidents, such as within a minute through the toll booths of the number of cars. |
POISSON (x, mean, cumulative) |
| PROB |
probability events to return to a designated group in the region fall events and the corresponding probability. If you do not give upper_limit, the return value of x _range with the probability of lower_limit. |
PROB (x_range, prob_range, lower_limit, upper_limit) |
| QUARTILE |
return quartile data sets. Quartile are commonly used in the sales data and measurements of the overall group. For example, you can use QUARTILE function obtained in the overall income of the top 25% of the value. |
QUARTILE (array, quart) |
| RANK |
returns a numerical value in a group of qualifying. Numerical data on the ranking list with the relative size of other values (if data has been waiting over a list of the sequence, then the value of the current ranking is its location). |
RANK (number, ref, order) |
| RSQ |
return in accordance with known_y's and known_x's in the data points calculated by Pearson product moment correlation coefficient of the square. For more information, please refer to function REARSON. R-square value of y can be interpreted as the variance and x the ratio of variance. |
RSQ (known_y's, known_x's) |
| SKEW |
skewness of return distribution. Reflects the average skewness of the distribution as the center of the degree of asymmetry. That the asymmetry is skewness of the distribution side of a time when even more difficult. That the asymmetry degree of negative skewness of the distribution side of a more negative trend. |
SKEW(number1,number2,...) |
| SLOPE |
return in accordance with known_y's and known_x's fitting the data points of the linear regression slope of a straight line. Slope of a straight line any two points on the straight distance and the weight ratio of horizontal distance, that is, the rate of change of linear regression. |
SLOPE (known_y's, known_x's) |
| SMALL |
return data the first k-minimum. Use this function can return data values on the specific location. |
SMALL (array, k) |
| STANDARDIZE |
to mean for the average return to standard-dev for the standard deviation of the distribution of normal values. |
STANDARDIZE (x, mean, standard_dev) |
| STDEV |
to estimate the standard deviation of the sample. Reflect the standard deviation relative to the average (mean) the degree of dispersion. |
STDEV (number1, number2 ,...) |
| STDEVA |
estimates based on samples of a given standard deviation. Standard deviation reflects the values compared with the average (mean) the degree of dispersion. Text values and logical values (such as TRUE or FALSE) will also be taken into account. |
STDEVA (value1, value2 ,...) |
| STDEVP |
to return to the form given to the parameters of the overall sample standard deviation. Reflect the standard deviation relative to the average (mean) the degree of dispersion. |
STDEVP (number1, number2 ,...) |
| STDEVPA |
calculation of the overall sample standard deviation. Standard deviation reflects the values compared with the average (mean) the degree of dispersion. |
STDEVPA (value1, value2 ,...) |
| STEYX |
return through the linear regression method to calculate the forecast value of y generated by the standard error. According to the standard error of measurement used to calculate a single variable x in y amount of predictive value of the error. |
STEYX (known_y's, known_x's) |
| TDIST |
return to Students t-distribution percentage points (probability), t distribution value (x) is the calculated t value (the calculation of its percentage points). t distribution for small sample data set the hypothesis testing. This function can be used in place of t distribution critical values table. |
TDIST (x, degrees_freedom, tails) |
| TINV |
return probability and the degree of freedom as a function of the students t value of t distribution. |
TINV (probability, degrees_freedom) |
| TREND |
to return to a linear regression line fitting a set of vertical coordinates (y value). Find that a given array known_y's and known_x's a straight line (least square method), and return to the specified array new_x's value in a straight line on the corresponding y value. |
TREND (known_y's, known_x's, new_x's, const) |
| TRIMMEAN |
return data sets within the average. Function TRIMMEAN data sets from the head and tail to remove a certain percentage of data points, and then the average demand. When the hope that removed part of the analysis of calculation data, you can use this function. |
TRIMMEAN (array, percent) |
| TTEST |
back to student's - t test associated probabilities. TTEST function can be used to determine whether two samples may come from the two with the same overall average. |
TTEST (array1, array2, tails, type) |
| VAR |
sample variance estimates. |
VAR (number1, number2 ,...) |
| VARA |
estimate based on the variance of a given sample. Not only the number, text values and logical values (such as TRUE and FALSE) will also be taken into account. |
VARA (value1, value2 ,...) |
| VARP |
calculation of the overall sample variance. |
VARP (number1, number2 ,...) |
| VARPA |
calculation of the overall sample variance. Not only the number, text values and logical values (such as TRUE and FALSE) will also be taken into account. |
VARPA (value1, value2 ,...) |
| WEIBULL |
distribution of the return of Webber. Use this function can be reliability analysis, such as computing devices the average time to failure. |
WEIBULL (x, alpha, beta, cumulative) |
| ZTEST |
to return to the two-tailed z test P value. Z test based on data sets or to generate x array of standard scores, and the return of the two-tailed probability distribution. You can use this function to return samples from a specific observation in the overall value of the likelihood estimation. |
ZTEST (array, x, sigma) |
