61.SKEW

Purposes: to return to a degree of asymmetry in the distribution. It reflects the average of the asymmetry in the distribution center the degree of asymmetry is that the degree of asymmetry of the distribution side of a time when even more difficult. That the asymmetry degree of negative asymmetry in the distribution side has become more negative.

Syntax: SKEW (number1, number2 ,…)。

Parameters: Number1, number2 … is the need to calculate the degree of asymmetry 1-30 parameters. Including comma-separated values, such as a single array and name.

Example: the formula “= SKEW ((22,23,29,19,38,27,25), (16,15,19,17,15,14,34))” return .854631382.

62.SLOPE

Purposes: to return to the set of data points after the linear regression equation fitting the slope of line (it is the straight line distance between any two points of vertical and horizontal distance ratio, that is, the rate of change of linear regression).

Syntax: SLOPE (known_y’s, known_x’s)

Parameters: Known_y’s dependent variable for the number of type array or range, Known_x’s data points as variables collection.

Example: the formula “= SLOPE ((22,23,29,19,38,27,25), (16,15,19,17,15,14,34))” return -0.100680934.

63.SMALL

Purposes: to return to the first k data of the minimum value, resulting in a specific location on the data values.

Syntax: SMALL (array, k)

Parameters: Array is a need to find the first k-minimum number of the array or data region, K for the return of data or data in the array where the location of the region (from small to large).

Examples: If If A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85, while the formula “= SMALL (A1: A5, 3)” to return to 78.

64.STANDARDIZE

Purposes: to mean to the average return to standard-dev for the standard deviation of the distribution of normal values.

Syntax: STANDARDIZE (x, mean, standard_dev)

Parameters: X for the needs of normal values, Mean distribution of arithmetic mean, Standard_dev standard deviation for the distribution.

Example: the formula “= STANDARDIZE (62,60,10)” to return to 0.2.

65.STDEV

Purposes: to estimate the standard deviation of the sample. It reflects the data in relation to the average (mean) the degree of dispersion.

Syntax: STDEV (number1, number2 ,…)

Parameters: Number1, number2, … correspond to the overall sample for the parameter 1-30. Can use comma-separated parameter form can also use the array, that is, the reference cell array.

Note: STDEV function parameters by assuming that the sample overall. All the samples if the data is in general, they should be calculated using standard deviation STDEVP function. At the same time, ignore the parameters of function of logical values (TRUE or FALSE) and text. If you can not ignore the logic of value and the text should be used STDEVA function.

Example: Suppose a particular sample of test scores for A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85, then to estimate the standard deviation of all results of formula “= STDEV (A1: A5)”, its results equal to 33.00757489.

66.STDEVA

Use: calculation of a given sample based on the standard deviation. STDEV function which is the difference between the value of the text and logical values (TRUE or FALSE) will also participate in the calculation.

Syntax: STDEVA (value1, value2 ,…)

Parameters: value1, value2, … as the overall sample parameters 1-30. Can use comma-separated form of parameters, you can use a single array, that is, the reference cell array.

Example: Suppose a particular part of the examination results for A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85, then to estimate the standard deviation of all results of formula “= STDEVA (A1: A5)”, its results equal to 33.00757489.

67.STDEVP

Use: return of the overall sample standard deviation. It reflects the overall sample compared with the average (mean) the degree of dispersion.

Syntax: STDEVP (number1, number2 ,…)

Parameters: Number1, number2, … for the overall sample corresponds to the 1-30 parameters. Can use comma-separated form of parameters, you can use a single array, that is, the reference cell array.

Note: STDEVP function ignored in the calculation of the process of logical values (TRUE or FALSE) and text. If logical values and text can not be ignored, should be used STDEVPA function.

STDEVP function at the same time assuming that the overall parameters for the entire sample. If the data in a representative sample of the overall sample, STDEV function should be used to calculate the standard deviation. When the sample size were more, STDEV function and STDEVP small difference between the calculated results.

Example: If a second examination, only five students to participate in, score of A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85, then calculated the standard deviation of all the formula “= STDEVP (A1: A5) “, the returned results equivalent to 29.52287249.

68.STDEVPA

Purposes: the calculation of standard deviation of the overall sample. STDEVP function it is the difference between the value of the text and logical values (TRUE or FALSE) to participate in the calculation.

Syntax: STDEVPA (value1, value2 ,…)

Parameters: value1, value2, … as a sample of the overall parameters of 1-30. Can use comma-separated form of parameters, you can use a single array (that is, cells of the array reference).

Note: STDEVPA function parameters for the assumption that the overall sample. If the data represent part of the overall sample, you must use STDEVA function to estimate the standard deviation.

Example: If a second examination, only five students to participate in, score of A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85, then calculated the standard deviation of all the formula “= STDEVP (A1: A5) “, the returned results equivalent to 29.52287249.

69.STEYX

Purposes: to 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.

Syntax: STEYX (known_y’s, known_x’s)

Parameters: Known_y’s data points as the dependent variable group or region, Known_x’s data points as variables or regional group.

Example: the formula “= STEYX ((22,13,29,19,18,17,15), (16,25,11,17,25,14,17))” return to 4.251584755.

70.TDIST

Purposes: student’s t-returned to the distribution of 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 of hypothesis testing, the use of this function can replace the critical value of t distribution table.

Syntax: TDIST (x, degrees_freedom, tails)

Parameters: X is the number of distribution requirements, Degrees_freedom integer degree of freedom to express, Tails of the distribution function specified in the return is a single-tailed or two-tailed distribution. If tails = 1, function return TDIST one-tailed distribution. If tails = 2, function to return to two-tailed distribution TDIST.

Example: the formula “= TDIST (60,2,1)” to return to .000138831.

71.TINV

Purposes: to return as a function of probability and the degree of freedom of the student’s t value of t distribution.

Syntax: TINV (probability, degrees_freedom)

Parameters: Probability for the corresponding two-tailed Student’s-t probability distribution, Degrees_freedom for the distribution of the degrees of freedom.

Example: the formula “= TINV (0.5,60)” to return to .678600713.

72.TREND

Purposes: 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.

Syntax: TREND (known_y’s, known_x’s, new_x’s, const)

Parameters: Known_y’s for the known relationship between the y = mx + b in the y value of the collection, Known_x’s for the known relationship between the y = mx + b in the optional set of x values, New_x’s need TREND function to return to the corresponding y value of the new x value , Const specified for the logical value of the constant term b is forced to 0.

73.TRIMMEAN

Purposes: to return to the internal data set on average. TRIMMEAN function from a data set of 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.

Syntax: TRIMMEAN (array, percent)

Parameters: Array for the need for screening and for the average of the array or data region, Percent for the purpose of calculating when to remove the proportion of data points. If percent = 0.2, while removal of 20 data 4, that is, remove the two rear head removed 2. Percent = 0.1,30 if data points equal to 10 percent of the three data points. TRIMMEAN function will be symmetric in the data set to remove the head and tail of the data.

Example: If A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85, while the formula “= TRIMMEAN (A1: A5, 0.1)” back to 62.

74.TTEST

Purposes: to return to s-t-student test associated probabilities. It can determine whether two samples come from two with the same overall average.

Syntax: TTEST (array1, array2, tails, type)

Parameters: Array1 is the first data set, Array2 is the second data set, Tails of the mantissa distribution curve specified. If tails = 1, TTEST function using one-tailed distribution. If tails = 2, TTEST function using two-tailed distribution. Type for the type of t test. If the type equivalent (1,2, 3) test (paired, two-sample test, such as variance, heteroscedastic two-sample test)

Example: the formula “= TTEST ((3,4,5,8,9,1,2,4,5), (6,19,3,2,14,4,5,17,1), 2,1 ) “to return to 0.196016.

75.VAR

Purposes: to estimate sample variance.

Syntax: VAR (number1, number2 ,…)

Parameters: Number1, number2, … correspond to the overall sample and 1-30 parameters.

Example: Suppose a particular examination taken in the five scores as a random sample, estimation results using VAR variance function, the sample value of A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85 , then the formula “= VAR (A1: A5)” to return to 1089.5.

76.VARA

Uses: used to estimate the variance of a given sample. VAR function with the difference between the text and logical values (TRUE and FALSE) will also participate in the calculation.

Syntax: VARA (value1, value2 ,…)

Parameters: value1, value2, … as a general sample parameters 1-30.

Example: Suppose a particular examination taken in the five scores as a random sample, estimation results using VAR variance function, the sample value of A1 = 78, A2 = 45, A3 = 90, A4 = 12, A5 = 85 , then the formula “= VARA (A1: A5, TRUE)” to return to 1491.766667.

77.VARP

Use: calculation of the overall sample variance.

Syntax: VARP (number1, number2 ,…)

Parameters: Number1, number2, … for the overall sample corresponds to the 1-30 parameters. The logic values (TRUE and FALSE) and the text will be ignored.

Example: If a second make-up, only five students to participate in, score of A1 = 88, A2 = 55, A3 = 90, A4 = 72, A5 = 85, with results VARP variance function estimation, then the formula “= VARP (A1: A5) “back to 214.5.

78.VARPA

Use: calculation of the overall sample variance. VARP function with the difference between the text and logical values (TRUE and FALSE) will also participate in the calculation.

Syntax: VARPA (value1, value2 ,…)

Parameters: value1, value2, … as a sample of the overall parameters of 1-30.

Example: If a second make-up, only five students to participate in, score of A1 = 88, A2 = 55, A3 = 90, A4 = 72, A5 = 85, with results VARPA variance function estimation, then the formula “= VARPA (A1: A5) “back to 214.5.

79.WEIBULL

Purposes: the distribution of the return of Webber. Use this function can be reliability analysis, such as equipment MTBF.

Syntax: WEIBULL (x, alpha, beta, cumulative)

Parameters: X is used to calculate the value function, Alpha parameter distribution, Beta distribution parameters, Cumulative specified in the form of function.

Example: the formula “= WEIBULL (98,21,100, TRUE)” to return to 0.480171231, = WEIBULL (58,11,67, FALSE) to return to .031622583.

80.ZTEST

Purposes: 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.

Syntax: ZTEST (array, x, sigma)

Parameters: Array for x as a test region of the array or data. X value to be tested. Sigma as a whole (known) standard deviation, if omitted, use the sample standard deviation.

Example: the formula “= ZTEST ((3,6,7,8,6,5,4,2,1,9), 4)” return 0.090574.