Average returns the arithmetic average of parameters. AVERAGE(number 1,number2, ...)
AVERAGEA calculates the average (arithmetic average) of the values in the parameter list. Not only numbers, but also text and logical values (such as TRUE and FALSE) will be counted. AVERAGEA(value 1,value2,...)
BETADIST returns the function value of Beta distribution cumulative function. Beta distribution accumulation function is usually used to study the occurrence and change of something in a sample set. BETADIST(x,alpha,beta,A,B)
BETAINV returns the inverse function value of beta distribution cumulative function. That is, if probability = BETADIST(x, ...), then betainv (probability, ...) = X. Beta distribution accumulation function can be used in project design to simulate the possible completion time after giving the expected completion time and variation parameters. BETAINV(probability,alpha,beta,A,B)
BINOMDIST returns the probability value of binomial distribution. BINOMDIST(number_s,trials,probability_s,cumulative)
CHIDIST returns the one-tailed probability of γ2 distribution. γ2 distribution is related to γ2 test. The γ2 test can be used to compare the observed value with the expected value. CHIDIST(x,degrees_freedom)
CHIINV returns the inverse function of the single-tailed probability of γ2 distribution. CHIINV(probability,degrees_freedom)
CHITEST returns the independence test value. The function CHITEST returns the statistical value of γ2 distribution and the corresponding degrees of freedom. CHITEST(actual_range,expected_range)
Confidence returns the confidence interval of the population average. The confidence interval is the area on either side of the sample average. CONFIDENCE(alpha,standard_dev,size)
CORREL returns the correlation coefficient between array 1 and array2. The correlation coefficient can be used to determine the relationship between two attributes. CORREL(array 1,array2)
COUNT returns the number of parameters. The function COUNT can be used to calculate the number of digital items in an array or cell range. COUNT(value 1,value2, ...)
COUNTA returns the number of non-null values in the parameter group. The function COUNTA can be used to calculate the number of data items in an array or cell area. COUNTA(value 1,value2, ...)
COVAR returns covariance, that is, the average of deviation products per data point, which can be used to determine the relationship between two data sets. COVAR(array 1,array2)
CRITBINOM returns the minimum value that makes the cumulative binomial distribution greater than or equal to the critical value. This function can be used for quality inspection. CRITBINOM(trials,probability_s,alpha)
DEVSQ returns the sum of squares of deviations between data points and their respective sample means. DEVSQ(number 1,number2,...)
EXPONDIST returns an exponential distribution. The function EXPONDIST can be used to model the time interval between events. EXPONDIST(x,lambda,cumulative)
FDIST returns the F probability distribution. Use this function to determine whether there are differences in the degree of change between two data series. FDIST(x,degrees_freedom 1,degrees_freedom2)
FINV returns the inverse function value of f probability distribution. FINV(probability,degrees_freedom 1,degrees_freedom2)
Fisher returns the fisher transformation of point X. The transformation generates a function that is approximately normal distribution rather than skewed. FISHER(x)
FISHERINV returns the inverse function value of Fisher transform. Use this transformation to analyze the correlation between data regions or arrays. FISHERINV(y)
FORECAST calculates or predicts future values based on given data. FORECAST(x,known_y's,known_x's)
Frequency returns the frequency distribution of data in an area as a vertical array. FREQUENCY(data_array,bins_array)
FTEST returns the result of F test. F test returns the one-tailed probability when the variance of array 1 and array 2 are not significantly different. You can use this function to determine whether the variance of two samples is different. FTEST(array 1,array2)
GAMMADIST returns the gamma distribution. You can use this function to study variables with skewed distribution. Gamma distribution is usually used for queuing analysis. GAMMADIST(x,alpha,beta,cumulative)
GAMMAINV returns the inverse of the cumulative function of the gamma distribution. GAMMAINV(probability,alpha,beta)
GAMMALN returns the natural logarithm of the gamma function, γ (x). GAMMALN(x)
GEOMEAN returns the geometric average of a positive array or data range. GEOMEAN(number 1,number2, ...)
Growth predicts the exponential growth value according to the given data. GROWTH(known_y's,known_x's,new_x's,const)
HARMEAN returns the harmonic mean of a data set. The arithmetic mean of harmonic mean and reciprocal is reciprocal. HARMEAN(number 1,number2, ...)
HYPGEOMDIST returns a hypergeometric distribution. HYPGEOMDIST(sample_s,number_sample,
population_s,number_population)
Intercept uses the known values of x and y to calculate the intercept of a straight line and the y axis. INTERCEPT(known_y's,known_x's)
KURT returns the peak of the data set. KURT(number 1,number2, ...)
LARGE returns the k-th largest value in the data set. Use this function to select values according to relative standards. LARGE(array,k)
LINEST uses the least square method to calculate the best straight line fitting for known data, and returns an array describing this straight line. LINEST(known_y's,known_x's,const,stats)
In regression analysis, LOGEST calculates an exponential regression fitting curve that best fits the observed data set, and returns an array describing the curve. LOGEST(known_y's,known_x's,const,stats)
LOGINV returns the inverse of the lognormal cumulative function of x. LOGINV(probability,mean,standard_dev)
LOGNORMDIST returns the cumulative function of the lognormal distribution of x. LOGNORMDIST(x,mean,standard_dev)
MAX returns the maximum value in the dataset. MAX(number 1,number2,...)
MAXA returns the maximum value in the parameter list. MAXA(value 1,value2,...)
Median returns the median of a given set of values. The median is the number in the middle of a set of data. MEDIAN(number 1,number2, ...)
MIN Returns the minimum value in the given parameter table. MIN(number 1,number2, ...)
MINA returns the smallest value in the parameter list. MINA(value 1,value2,...)
MODE returns the value that appears most frequently in an array or data range. MODE(number 1,number2, ...)
NEGBINOMDIST returns a negative binomial distribution. NEGBINOMDIST(number_f,number_s,probability_s)
NORMDIST returns the cumulative function of the normal distribution of the given mean and standard deviation. NORMDIST(x,mean,standard_dev,cumulative)
NORMINV returns the inverse function of the cumulative function of the normal distribution given the mean and standard deviation. NORMINV(probability,mean,standard_dev)
NORMSDIST returns the cumulative function of a standard normal distribution with a mean of 0 and a standard deviation of 1. NORMSDIST(z)
NORMSINV returns the inverse of the standard normal distribution cumulative function. The average value of this distribution is 0 and the standard deviation is 1. NORMSINV(probability)
Pearson returns the Pearson product moment correlation coefficient, r, which is a dimensionless index ranging from-1.0 to 1.0 (including-1.0 and 1.0), reflecting the linear correlation between two data sets. PEARSON(array 1,array2)
PERCENTILE returns the k percentage value point of the value range. You can use this function to establish an acceptance threshold. For example, you can determine the detection candidates whose scores rank above 90%. PERCENTILE(array,k)
PERCENTRANK returns the percentage ranking of a specific value in a data set. This function can be used to see where specific data is located in the dataset. For example, you can use the function PERCENTRANK to calculate the position of a particular competency test score among all competency test scores. PERCENTRANK(array,x,significance)
PERMUT returns the number of permutations of several objects selected from a given set of objects. An arrangement can be an object with internal order or an arbitrary set or subset of events. Arrangement is different from combination, and the internal order of combination is meaningless. This function can be used for probability in lottery calculation. PERMUT(number,number_chosen)
Poisson returns Poisson distribution. Poisson distribution is usually used to predict the number of events in a period of time, such as the number of cars passing through a toll booth in one minute. POISSON(x,mean,cumulative)
PROB returns the sum of probabilities corresponding to events in a probability event group that fall within a specified area. If upper_limit is not given, the probability that the value in x _range is equal to lower_limit is returned. PROB(x_range,prob_range,lower_limit,upper_limit)
Quartile returns the quartile of a data set. Quartiles are commonly used to group populations in sales and measurement data sets. For example, you can use the QUARTILE function to find the income value of the top 25% in the population. QUARTILE(array,quart)
Rank returns the rank of a number in a set of numbers. The ranking of a value is relative to other values in the data list (if the data list has been sorted, the ranking of the value is its current position). RANK(number,ref,order)
RSQ returns the square of Pearson product moment correlation coefficient calculated from data points in known_y's and known _ x 's. See the function REARSON for more information. R squared value can be interpreted as the ratio of y variance to x variance. RSQ(known_y's,known_x's)
SKEW returns the skewness of the distribution. Skewness reflects the degree of asymmetry of distribution centered on the average value. Positive skewness indicates that the distribution of asymmetric edges tends to be positive. Negative skewness means that the distribution of asymmetric edges tends to be negative. SKEW(number 1,number2,...)
Slope returns the slope of the linear regression line fitted according to the data points in known_y's and known _ x 's.. The slope is the ratio of the vertical distance of any two points on a straight line to the horizontal distance, that is, the rate of change of the regression straight line. SLOPE(known_y's,known_x's)
SMALL returns the k-th smallest value in the data set. Use this function to return a numeric value at a specific location in a dataset. SMALL(array,k)
STANDARDIZE returns the normalized value of the distribution with mean as the average and standard-dev as the standard deviation. STANDARDIZE(x,mean,standard_dev)
STDEV estimates the standard deviation of the sample. The standard deviation reflects the degree of dispersion relative to the mean. STDEV(number 1,number2,...)
STDEVA estimates the standard deviation based on a given sample. The standard deviation reflects the dispersion degree of the numerical value relative to the mean. Text values and logical values (such as TRUE or FALSE) will also be counted. STDEVA(value 1,value2,...)
STDEVP returns the standard deviation of the entire sample population given as a parameter. The standard deviation reflects the degree of dispersion relative to the mean. STDEVP(number 1,number2,...)
STDEVPA calculates the standard deviation of the sample population. The standard deviation reflects the dispersion degree of the numerical value relative to the mean. STDEVPA(value 1,value2,...)
STEYX returns the standard error when calculating the predicted value of y by linear regression method. The standard error is used to measure the error of the y prediction calculated from a single x variable. STEYX(known_y's,known_x's)
TDIST returns the percentage (probability) of the student's t- distribution, and the value (x) in the T-distribution is the calculated value of T (the percentage will be calculated). T distribution is used for hypothesis testing of small sample data sets. Use this function to replace the critical value table of t distribution. TDIST(x,degrees_freedom,tails)
TINV returns the t value of the student t distribution as a function of probability and degree of freedom. TINV(probability,degrees_freedom)
TREND returns a set of ordinate values (y values) of a linear regression fitting line. That is, find the straight line suitable for the given arrays known_y's and known_x's (by least square method), and return the Y value corresponding to the specified array new_x's value on the straight line. TREND(known_y's,known_x's,new_x's,const)
TRIMMEAN returns the internal average of a data set. TRIMMEAN removes a certain percentage of data points from the head and tail of the data set, and then averages them. You can use this function when you want to exclude the calculation of some data from the analysis. TRIMMEAN(array,percent)
TTEST returns the probability related to the student's T-test. The function TTEST can be used to judge whether two samples may come from two populations with the same mean value. TTEST(array 1,array2,tails,type)
VAR estimates sample variance. VAR(number 1,number2,...)
VARA estimates the variance based on a given sample. Not only numbers, but also text values and logical values (such as TRUE and FALSE) will be counted. VARA(value 1,value2,...)
VARP calculates the variance of the sample population. VARP(number 1,number2,...)
VARPA calculates the variance of the sample population. Not only numbers, but also text values and logical values (such as TRUE and FALSE) will be counted. VARPA(value 1,value2,...)
Weibull returns the Weibull distribution. This function can be used for reliability analysis, such as calculating the mean time to failure of equipment. WEIBULL(x,alpha,beta,cumulative)
ZTEST returns the two-tailed p value of z test. Z-test generates the standard score of X based on data set or array, and returns the two-tailed probability of normal distribution. You can use this function to return the likelihood estimate of specific observations extracted from a population. ZTEST(array,x,sigma)