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Name |
Description |
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Anova |
This analysis tool performs simple analysis of variance (anova) to test the hypothesis that means from two or more samples are equal (drawn from populations with the same mean). This technique expands on the tests for two means, such as the t-test. |
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BetaFunction |
Calculates Beta Function |
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Correlation |
This analysis tool and its formulas measure the relationship between two data sets that are scaled to be independent of the unit of measurement. The population correlation calculation returns the covariance of two data sets divided by the product of their standard deviations. You can use the Correlation tool to determine whether two ranges of data move together — that is, whether large values of one set are associated with large values of the other (positive correlation), whether small values of one set are associated with large values of the other (negative correlation), or whether values in both sets are unrelated (correlation near zero). |
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Covariance |
This analysis tool and its formula return the average of the product of deviations of data points from their respective means. Covariance is a measure of the relationship between two ranges of data. You can use the Covariance tool to determine whether two ranges of data move together — that is, whether large values of one set are associated with large values of the other (positive covariance), whether small values of one set are associated with large values of the other (negative covariance), or whether values in both sets are unrelated (covariance near zero). |
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FDistribution |
/// Returns the F probability distribution. You can use this function to determine whether two data sets have different degrees of diversity. For example, you can examine test scores given to men and women entering high school and determine if the variability in the females is different from that found in the males. |
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FTest |
This analysis tool performs a two-sample F-test to compare two population variances. For example, you can use an F-test to determine whether the time scores in a swimming meet have a difference in variance for samples from two teams. |
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GammaFunction |
Calculates Gamma Function |
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InverseFDistribution |
Returns the inverse of the F probability distribution. If p = FDIST(x,...), then FINV(p,...) = x. The F distribution can be used in an F-test that compares the degree of variability in two data sets. For example, you can analyze income distributions in the United States and Canada to determine whether the two countries have a similar degree of diversity. |
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InverseNormalDistribution |
Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation. |
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InverseTDistribution |
Returns the t-value of the Student's t-distribution as a function of the probability and the degrees of freedom. |
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Mean |
/// Returns the average (arithmetic mean) of the arguments. |
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Median |
Returns the median of the given numbers. The median is the number in the middle of a set of numbers; that is, half the numbers have values that are greater than the median, and half have values that are less. |
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NormalDistribution |
Returns the normal cumulative distribution for the specified mean and standard deviation. This function has a very wide range of applications in statistics, including hypothesis testing. |
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TDistribution |
Returns the Percentage Points (probability) for the Student t-distribution where a numeric value (x) is a calculated value of t for which the Percentage Points are to be computed. The t-distribution is used in the hypothesis testing of small sample data sets. Use this function in place of a table of critical values for the t-distribution. |
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TTestEqualVariances |
Two-Sample Assuming Equal Variances - This analysis tool performs a two-sample student's t-test. This t-test form assumes that the means of both data sets are equal; it is referred to as a homoscedastic t-test. You can use t-tests to determine whether two sample means are equal. |
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TTestPaired |
This analysis tool and its formula perform a paired two-sample student's t-test to determine whether a sample's means are distinct. This t-test form does not assume that the variances of both populations are equal. You can use a paired test when there is a natural pairing of observations in the samples, such as when a sample group is tested twice — before and after an experiment. |
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TTestUnEqualVariances |
This analysis tool and its formulas perform a two-sample student's t-test. This t-test form assumes that the variances of both ranges of data are unequal; it is referred to as a heteroscedastic t-test. You can use a t-test to determine whether two sample means are equal. Use this test when the groups under study are distinct. Use a paired test when there is one group before and after a treatment. |
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Variance |
Estimates variance based on a sample. |
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ZTest |
The z-test generates a standard score for x with respect to the data set, array, and returns the two-tailed probability for the normal distribution. You can use this function to assess the likelihood that a particular observation is drawn from a particular population. |
Public Constructors