Statistical Functions

Question 1

SAMPLE(n_value, table, orderBy_expression, [order[, orderBy_expression, [order]]…])

Answer 1

Returns a sample of N rows from the specified table.

Question 2

BETA.DIST(x,alpha,beta,cumulative,[A],[B])

Answer 2

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.

Question 3

CHISQ.DIST(x, deg_freedom, cumulative)

Answer 3

Returns the chi-squared distribution.

Question 4

CONFIDENCE.T(alpha,standard_dev,size)

Answer 4

Returns the confidence interval for a population mean, using a Student’s t distribution.

Question 5

EXPON.DIST(x,lambda,cumulative)

Answer 5

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.

Question 6

GEOMEANX(table, expression)

Answer 6

Returns the geometric mean of an expression evaluated for each row in a table.

Question 7

GEOMEAN(column)

Answer 7

Returns the geometric mean of the numbers in a column.

Question 8

BETA.INV(probability,alpha,beta,[A],[B])

Answer 8

Returns the inverse of the beta cumulative probability density function (BETA.DIST).

Question 9

CHISQ.INV(probability,deg_freedom)

Answer 9

Returns the inverse of the left-tailed probability of the chi-squared distribution.

Question 10

CHISQ.INV.RT(probability,deg_freedom)

Answer 10

Returns the inverse of the right-tailed probability of the chi-squared distribution.

Question 11

NORM.S.INV(Probability)

Answer 11

Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one.

Question 12

PERCENTILE.EXC(column, k)

Answer 12

Returns the k-th percentile of values in a range, where k is in the range 0..1, exclusive.

Question 13

PERCENTILE.INC(column, k)

Answer 13

Returns the k-th percentile of values in a range, where k is in the range 0..1, inclusive.

Question 14

T.INV(Probability,Deg_freedom)

Answer 14

Returns the left-tailed inverse of the Student’s t-distribution.

Question 15

MEDIANX(table, expression)

Answer 15

Returns the median number of an expression evaluated for each row in a table.

Question 16

MEDIAN(column)

Answer 16

Returns the median of numbers in a column.

Question 17

NORM.DIST(X, Mean, Standard_dev, Cumulative)

Answer 17

Returns the normal distribution for the specified mean and standard deviation.

Question 18

COMBINA(number, number_chosen)

Answer 18

Returns the number of combinations (with repetitions) for a given number of items.

Question 19

COMBIN(number, number_chosen)

Answer 19

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.

Question 20

PERMUT(number, number_chosen)

Answer 20

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.

Question 21

PERCENTILEX.EXC(table, expression, k)

Answer 21

Returns the percentile number of an expression evaluated for each row in a table.

Question 22

PERCENTILEX.INC(table, expression;, k)

Answer 22

Returns the percentile number of an expression evaluated for each row in a table.

Question 23

POISSON.DIST(x,mean,cumulative)

Answer 23

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.

Question 24

RANKX(table, expression[, value[, order[, ties]]])

Answer 24

Returns the ranking of a number in a list of numbers for each row in the table argument.

Question 25

RANK.EQ(value, columnName[, order])

Answer 25

Returns the ranking of a number in a list of numbers.

Question 26

CHISQ.DIST.RT(x, deg_freedom)

Answer 26

Returns the right-tailed probability of the chi-squared distribution.

Question 27

T.DIST.RT(X,Deg_freedom)

Answer 27

Returns the right-tailed Student's t-distribution.

Question 28

STDEVX.S(table, expression)

Answer 28

Returns the standard deviation of a sample population.

Question 29

STDEV.S(ColumnName)

Answer 29

Returns the standard deviation of a sample population.

Question 30

STDEVX.P(table, expression)

Answer 30

Returns the standard deviation of the entire population.

Question 31

STDEV.P(ColumnName)

Answer 31

Returns the standard deviation of the entire population.

Question 32

NORM.S.DIST(Z, Cumulative)

Answer 32

Returns the standard normal distribution (has a mean of zero and a standard deviation of one).

Question 33

T.DIST(X,Deg_freedom,Cumulative)

Answer 33

Returns the Student's left-tailed t-distribution.

Question 34

T.INV.2T(Probability,Deg_freedom)

Answer 34

Returns the two-tailed inverse of the Student's t-distribution.

Question 35

T.DIST.2T(X,Deg_freedom)

Answer 35

Returns the two-tailed Student's t-distribution.

Question 36

VARX.S(table, expression)

Answer 36

Returns the variance of a sample population.

Question 37

VAR.S(columnName)

Answer 37

Returns the variance of a sample population.

Question 38

VAR.P(columnName)

Answer 38

Returns the variance of the entire population.

Question 39

VARX.P(table, expression)

Answer 39

Returns the variance of the entire population.

Question 40

CONFIDENCE.NORM(alpha,standard_dev,size)

Answer 40

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.

Question 41

NORM.INV(Probability, Mean, Standard_dev)

Answer 41

The inverse of the normal cumulative distribution for the specified mean and standard deviation.

Question 42

LINESTX ( table, expressionY, expressionX[, …][, const] )

Answer 42

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 = Slope1*x1 + Slope2*x2 + ... + Intercept.

Question 43

LINEST ( columnY, columnX[, …][, const] )

Answer 43

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 = Slope1*x1 + Slope2*x2 + ... + Intercept.