21.FINV

Purposes: to return to F of the inverse probability distribution function, that is, the critical value of F distribution. If p = FDIST (x, …), while FINV (p, …) = x.

Syntax: FINV (probability, degrees_freedom1, degrees_freedom2)

Parameters: Probability is the probability of the cumulative value of F distribution, Degrees_freedom1 molecular degrees of freedom, Degrees_freedom2 is the denominator degrees of freedom.

Example: the formula “= FINV (0.1,86,74)” to return to 1.337888023.

22.FISHER

Purposes: to return to point x of the Fisher transform. Generate a transformation similar to the normal distribution rather than a function of skew, use this function to complete the assumption that the correlation coefficient test.

Syntax: FISHER (x)

Parameters: X for a number of points in the transform.

Example: the formula “= FISHER (0.55)” to return to .618381314.

23.FISHERINV

Purposes: to return to the inverse Fisher transform function value, if y = FISHER (x), then FISHERINV (y) = x. Transform the above-mentioned data can be analyzed or the correlation between array.

Syntax: FISHERINV (y)

Parameters: Y as a value point in the inverse transform.

Example: the formula “= FISHERINV (0.765)” to return to .644012628.

24.FORECAST

Uses: In accordance with a linear regression line fit to return to a predictive value. This function can be used for future sales, demand or inventory to predict consumer trends.

Syntax: FORECAST (x, known_y’s, known_x’s).

Parameters: X to the needs of the data points to predict the X coordinate (variable value). Known_y’s is a straight line to meet the linear fit y = kx + b of the point set of a group of selected known y values, Known_x’s is a straight line to meet the linear fit y = kx + b of the point set of a group of elected known x value.

Example: the formula “= FORECAST (16, (7,8,9,11,15), (21,26,32,36,42))” return to 4.378318584.

25.FREQUENCY

Purposes: to return to a vertical array of data in a regional frequency distribution. It can be calculated in a given range and reception range of each interval contains the number of data.

Syntax: FREQUENCY (data_array, bins_array)

Parameters: Data_array is used to calculate the frequency of an array, or array element reference region. Bins_array receive interval data for a region array or array reference, set the frequency of data_array sub-point calculations.

26.FTEST

Purposes: to return to the results of F tests. It returned when the array 1 and array 2 had no significant variance between the single-tail probability, can determine whether the variance of the two different samples. For example, two classes are given the same subject area examinations in order to test whether there are differences.

Syntax: FTEST (array1, array2)

Parameters: Array1 is the first array or data region, Array2 is the second array, or data.

Example: If A1 = 71, A2 = 83, A3 = 76, A4 = 49, A5 = 92, A6 = 88, A7 = 96, B1 = 59, B2 = 70, B3 = 80, B4 = 90, B5 = 89 , B6 = 84, B7 = 92, while the formula “= FTEST (A1: A7, B1: B7)” to return to .519298931.

27.GAMMADIST

Purposes: to return to the gamma distribution. It can be used with a skewed distribution of study variables used in queuing analysis.

Syntax: GAMMADIST (x, alpha, beta, cumulative).

Parameters: X for the gamma distribution used to calculate the value, Alpha is the distribution of the parameters γ, Betaγ distribution of a parameter. If beta = 1, GAMMADIST function returns the standard gamma distribution. Cumulative is a logical value, the form of a decision function. If cumulative is TRUE, GAMMADIST function returns the cumulative distribution function; If FALSE, the return probability density function.

Example: the formula “= GAMMADIST (10,9,2, FALSE)” equivalent to the calculation 0.032639, = GAMMADIST (10,9,2, TRUE) return 0.068094.

28.GAMMAINV

Purposes: to return with a given probability distribution of the gamma-range point, there used to study the distribution of skewed variables. If P = GAMMADIST (x ,…), then GAMMAINV (p ,…)= x.

Syntax: GAMMAINV (probability, alpha, beta)

Parameters: Probability distribution for the probability of gamma value, Alphaγ distribution parameters, Betaγ distribution parameters. If beta = 1, return to the standard gamma function distribution GAMMAINV.

Example: the formula “= GAMMAINV (0.05,8,2)” to return to 7.96164386.

29.GAMMALN

Purposes: to return to the gamma function of the natural logarithm of Γ (x).

Syntax: GAMMALN (x)

Parameters: X to the needs of the numerical calculation GAMMALN function.

Example: the formula “= GAMMALN (6)” to return to 4.787491743.

30.GEOMEAN

Purposes: to return to positive data array or the geometric mean. Variable can be used to calculate the average growth rate of compound interest.

Syntax: GEOMEAN (number1, number2 ,…)

Parameters: Number1, number2, … for the need to calculate the average of 1-30 parameters, in addition to the use of the form of comma-separated values can also use the array or array reference.

Example: the formula “= GEOMEAN (1.2,1.5,1.8,2.3,2.6,2.8,3)” The result of this calculation is 2.069818248.

31.GROWTH

Purposes: to set the data value of the forecast growth index. According to the known x values and y values, GROWTH function to return a new set of x values corresponding y values. GROWTH fitting function used to satisfy a given x value and y value of the index curve.

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

Parameters: Known_y’s index return is to meet the curve y = b * m ^ x of a group known y values; Known_x’s index return is to meet the curve y = b * m ^ x of a set of known values of x collection (optional parameters); New_x’s is a new set of x values, GROWTH function can return to their corresponding y values; Const is a logical value specifying whether to set a coefficient b mandatory, if const is TRUE or omitted, b will be involved in the normal calculation. If const is FALSE, b will be set to 1, m values will be made to adjust y = m ^ x.

32.HARMEAN

Purposes: to return to the harmonic average of the data collection. Countdown to reconcile with the arithmetic average of the average of each countdown. Reconcile the total is less than the average of the geometric mean, and geometric mean less than the arithmetic average of the total.

Syntax: HARMEAN (number1, number2 ,…)

Parameters: Number1, number2, … is the need to calculate their average parameters 1-30. Can use comma-separated form of parameters, you can also use the array or array reference.

Example: the formula “= HARMEAN (66,88,92)” to return to 80.24669604.

33.HYPGEOMDIST

Purposes: to return to hypergeometric distribution. A given sample size, sample volume and sample the overall success of the overall number of, HYPGEOMDIST function to return to sample the frequency of a given probability of success.

Syntax: HYPGEOMDIST (sample_s, number_sample, population_s, number_population)

Parameters: Sample_s for the success of the number of samples, Number_sample for the sample size. Population_s for the whole sample the number of successful, Number_population capacity for the overall sample.

Example: If a class has 42 students. Of which 22 were male and 20 were women. 6 If the randomly selected people, then one of just three girls in the probability formula is: “= HYPGEOMDIST (3,6,20,42)”, return to the results of 0.334668627.

34.INTERCEPT

Use: Using the known x value and y value of a straight line with the y-axis intercept. When the known variables to zero, the intercept can be obtained using the value of the dependent variable.

Syntax: INTERCEPT (known_y’s, known_x’s)

Parameters: Known_y’s dependent variable is a set of data or data sets, Known_x’s argument is a set of data or data sets.

Example: If A1 = 71, A2 = 83, A3 = 76, A4 = 49, A5 = 92, A6 = 88, A7 = 96, B1 = 59, B2 = 70, B3 = 80, B4 = 90, B5 = 89 , B6 = 84, B7 = 92, while the formula “= INTERCEPT (A1: A7, B1: B7)” to return to 87.61058785.

35.KURT

Purposes: to return to the peak data set. It reflects a comparison with the normal distribution of the degree of sharp or flat, is that the relatively sharp peak of the distribution, the negative peak of the distribution that is relatively flat.

Syntax: KURT (number1, number2 ,…)

Parameters: Number1, number2, … for the need to calculate the peak parameters 1-30. They can use the form of comma-separated parameters, you can use a single array, that is, the reference cell array.

Example: If a particular student test scores for A1 = 71, A2 = 83, A3 = 76, A4 = 49, A5 = 92, A6 = 88, A7 = 96, while the formula “= KURT (A1: A7)” to return to -1.199009798 shows the relative performance of the normal distribution is a relatively flat distribution.

36.LARGE

Purposes: to return to a focus on a maximum value of data. Query can use the LARGE function test scores focus on first, second, third, etc. scores.

Syntax: LARGE (array, k)

Parameters: Array for the need to query the first k-value of the array or data region, K for the return value or data in the array position in the range (ie, ranking).

Examples: If B1 = 59, B2 = 70, B3 = 80, B4 = 90, B5 = 89, B6 = 84, B7 = 92,, the formula “= LARGE (B1, B7, 2)” back to 90.

37.LINEST

Uses: Use the least square method known data on the best fitting straight line and return to describe the linear array.

Syntax: LINEST (known_y’s, known_x’s, const, stats)

Parameters: Known_y’s is the expression y = mx + b in the y value of the collection known, Known_x’s is the relationship between the expression y = mx + b in the known set of optional x value, Const is a logical value specifying whether or not mandatory to make constant b is 0, if const is TRUE or omitted, b will be involved in the normal calculation. If const is FALSE, b will be set to 0, and at the same time making the adjustment m value y = mx. Stats is a logical value specifying whether or not to return additional regression statistics. If stats is TRUE, the return of function LINEST additional regression statistics. If stats is FALSE or omitted, only to return LINEST function m and the constant term coefficient b.

Example: If A1 = 71, A2 = 83, A3 = 76, A4 = 49, A5 = 92, A6 = 88, A7 = 96, B1 = 59, B2 = 70, B3 = 80, B4 = 90, B5 = 89 , B6 = 84, B7 = 92, the array formula “(= LINEST (A1: A7, B1: B7))” return -0.174244885, -0.174244885, -0.174244885, -0.174244885, -0.174244885, -0.174244885, -0.174244885.

38.LOGEST

Uses: In the regression analysis, the calculation of the best observational data group index regression curve fitting, and return to describe the curve of the array.

Syntax: LOGEST (known_y’s, known_x’s, const, stats)

Parameters: Known_y’s are a group of line with the y = b * m ^ x function of the y value of the collection, Known_x’s are a group of line with the y = b * m ^ x computing the relationship between the value of the optional x collection, Const is to specify whether you want to set up regular the number of b logical value of 1 if the const is set to TRUE or omitted, then the constant term b will be obtained by calculating.

Example: If a company’s sales of new products, exponential growth, followed by A1 = 33100, A2 = 47300, A3 = 69000, A4 = 102000, A5 = 150000 and A6 = 220000, while B1 = 11, B2 = 12, B3 = 13, B4 = 14, B5 = 15, B6 = 16. Using the array formula “(= LOGEST (A1: A6, B1: B6, TRUE, TRUE))”, in C1: D5 cells be the result of the calculation is: 1.463275628, 495.3047702,0.002633403,0.035834282,0.99980862,0.011016315,20896.8011 , 4, 2.53601883 and 0.000485437.

39.LOGINV

Purposes: to return x to the cumulative log-normal distribution function of the inverse function, where the ln (x) are those which are mean (average) and standard-dev (standard deviation) parameters of the normal distribution. If p = LOGNORMDIST (x ,…), then LOGINV (p ,…)= x.

Syntax: LOGINV (probability, mean, standard_dev)

Parameters: Probability is a log-normal distribution associated with the probability, Mean for ln (x) the average, Standard_dev for ln (x) the standard deviation.

Example: the formula “= LOGINV (0.036,2.5,1.5)” to return to .819815949.

40.LOGNORMDIST

Purposes: to return x to the cumulative log-normal distribution function, in which ln (x) is subject to parameters of the normal distribution mean and standard_dev. Use this function can be analyzed after a logarithmic transformation of data.

Syntax: LOGNORMDIST (x, mean, standard_dev)

Parameters: X is used to calculate the value function, Mean is the ln (x) the average, Standard_dev is ln (x) the standard deviation.

Example: the formula “= LOGNORMDIST (2,5.5,1.6)” to return to .001331107.