Statistical Functions Flashcards
SAMPLE(n_value, table, orderBy_expression, [order[, orderBy_expression, [order]]…])
Returns a sample of N rows from the specified table.
BETA.DIST(x,alpha,beta,cumulative,[A],[B])
Returns the beta distribution. The beta distribution is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.
CHISQ.DIST(x, deg_freedom, cumulative)
Returns the chi-squared distribution.
CONFIDENCE.T(alpha,standard_dev,size)
Returns the confidence interval for a population mean, using a Student’s t distribution.
EXPON.DIST(x,lambda,cumulative)
Returns the exponential distribution. Use EXPON.DIST to model the time between events, such as how long an automated bank teller takes to deliver cash. For example, you can use EXPON.DIST to determine the probability that the process takes at most 1 minute.
GEOMEANX(table, expression)
Returns the geometric mean of an expression evaluated for each row in a table.
GEOMEAN(column)
Returns the geometric mean of the numbers in a column.
BETA.INV(probability,alpha,beta,[A],[B])
Returns the inverse of the beta cumulative probability density function (BETA.DIST).
CHISQ.INV(probability,deg_freedom)
Returns the inverse of the left-tailed probability of the chi-squared distribution.
CHISQ.INV.RT(probability,deg_freedom)
Returns the inverse of the right-tailed probability of the chi-squared distribution.
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.
PERCENTILE.EXC(column, k)
Returns the k-th percentile of values in a range, where k is in the range 0..1, exclusive.
PERCENTILE.INC(column, k)
Returns the k-th percentile of values in a range, where k is in the range 0..1, inclusive.
T.INV(Probability,Deg_freedom)
Returns the left-tailed inverse of the Student’s t-distribution.
MEDIANX(table, expression)
Returns the median number of an expression evaluated for each row in a table.
MEDIAN(column)
Returns the median of numbers in a column.
NORM.DIST(X, Mean, Standard_dev, Cumulative)
Returns the normal distribution for the specified mean and standard deviation.
COMBINA(number, number_chosen)
Returns the number of combinations (with repetitions) for a given number of items.
COMBIN(number, number_chosen)
Returns the number of combinations for a given number of items. Use COMBIN to determine the total possible number of groups for a given number of items.
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 where internal order is significant. Permutations are different from combinations, for which the internal order is not significant. Use this function for lottery-style probability calculations.
PERCENTILEX.EXC(table, expression, k)
Returns the percentile number of an expression evaluated for each row in a table.
PERCENTILEX.INC(table, expression;, k)
Returns the percentile number of an expression evaluated for each row in a table.
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.
RANKX(table, expression[, value[, order[, ties]]])
Returns the ranking of a number in a list of numbers for each row in the table argument.
RANK.EQ(value, columnName[, order])
Returns the ranking of a number in a list of numbers.
CHISQ.DIST.RT(x, deg_freedom)
Returns the right-tailed probability of the chi-squared distribution.
T.DIST.RT(X,Deg_freedom)
Returns the right-tailed Student’s t-distribution.
STDEVX.S(table, expression)
Returns the standard deviation of a sample population.
STDEV.S(ColumnName)
Returns the standard deviation of a sample population.
STDEVX.P(table, expression)
Returns the standard deviation of the entire population.
STDEV.P(ColumnName)
Returns the standard deviation of the entire population.
NORM.S.DIST(Z, Cumulative)
Returns the standard normal distribution (has a mean of zero and a standard deviation of one).
T.DIST(X,Deg_freedom,Cumulative)
Returns the Student’s left-tailed t-distribution.
T.INV.2T(Probability,Deg_freedom)
Returns the two-tailed inverse of the Student’s t-distribution.
T.DIST.2T(X,Deg_freedom)
Returns the two-tailed Student’s t-distribution.
VARX.S(table, expression)
Returns the variance of a sample population.
VAR.S(columnName)
Returns the variance of a sample population.
VAR.P(columnName)
Returns the variance of the entire population.
VARX.P(table, expression)
Returns the variance of the entire population.
CONFIDENCE.NORM(alpha,standard_dev,size)
The confidence interval is a range of values. Your sample mean, x, is at the center of this range and the range is x ± CONFIDENCE.NORM. For example, if x is the sample mean of delivery times for products ordered through the mail, x ± CONFIDENCE.NORM is a range of population means. For any population mean, μ0, in this range, the probability of obtaining a sample mean further from μ0 than x is greater than alpha; for any population mean, μ0, not in this range, the probability of obtaining a sample mean further from μ0 than x is less than alpha. In other words, assume that we use x, standard_dev, and size to construct a two-tailed test at significance level alpha of the hypothesis that the population mean is μ0. Then we will not reject that hypothesis if μ0 is in the confidence interval and will reject that hypothesis if μ0 is not in the confidence interval. The confidence interval does not allow us to infer that there is probability 1 – alpha that our next package will take a delivery time that is in the confidence interval.
NORM.INV(Probability, Mean, Standard_dev)
The inverse of the normal cumulative distribution for the specified mean and standard deviation.
LINESTX ( table, expressionY, expressionX[, …][, const] )
Uses the Least Squares method to calculate a straight line that best fits the given data, then returns a table describing the line. The data result from expressions evaluated for each row in a table. The equation for the line is of the form: y = Slope1x1 + Slope2x2 + … + Intercept.
LINEST ( columnY, columnX[, …][, const] )
Uses the Least Squares method to calculate a straight line that best fits the given data, then returns a table describing the line. The equation for the line is of the form: y = Slope1x1 + Slope2x2 + … + Intercept.
