Given a test statistic in terms of the normal, , Student's , or F-ratio distribution, a -value can be computed using the appropriate -value function. For example, NormalPValue computes a -value for a test statistic using a normal distribution with mean zero and unit variance. A two-sided -value can be obtained by setting TwoSided->True.
|NormalPValue[teststat]||give the -value for teststat in terms of the normal distribution with mean 0 and unit variance|
|StudentTPValue[teststat,dof]||give the -value for teststat in terms of Student's distribution with dof degrees of freedom|
|ChiSquarePValue[teststat,dof]||give the -value for teststat in terms of the distribution with dof degrees of freedom|
|FRatioPValue[teststat,numdof,dendof]||give the -value for teststat in terms of the F-ratio distribution with numdof numerator and dendof denominator degrees of freedom|
A confidence interval gives bounds within which a parameter value is expected to lie with a certain probability. Interval estimation of a parameter is often useful in observing the accuracy of an estimator as well as in making statistical inferences about the parameter in question.
|MeanCI[list]||give a confidence interval for the population mean estimated from list|
|MeanDifferenceCI[list1,list2]||give a confidence interval for the difference between the population means estimated from and|
Assumptions about variances of the populations from which the data were sampled will affect the distribution of the parameter estimate. The KnownVariance and EqualVariances options can be used to specify assumptions about population variances.
|EqualVariances||False||whether the unknown population variances are assumed equal|
Option for MeanDifferenceCI.
Confidence intervals for the difference between means are also based on Student's distribution if the variances are not known. If the variances are assumed equal, MeanDifferenceCI is based on Student's distribution with Length[list1]+Length[list2]-2 degrees of freedom. If the population variances are not assumed equal, Welch's approximation for the degrees of freedom is used.
|VarianceCI[list]||give a confidence interval for the population variance estimated from list|
|VarianceRatioCI[list1,list2]||give a confidence interval for the ratio of the population variances estimated from and from|
The default confidence level for confidence interval functions is . Other levels can be specified via the ConfidenceLevel option.
|ConfidenceLevel||.95||confidence level for the interval|
Given an estimate of the mean, variance, or ratio of variances and necessary standard deviations or degrees of freedom, confidence intervals can also be obtained for normal, chi-square, Student's , or F-ratio distributions.
|NormalCI[mean,sd]||give the confidence interval centered at mean with standard deviation sd|
|StudentTCI[mean,se,dof]||give the confidence interval centered at mean with standard error se and dof degrees of freedom|
|ChiSquareCI[variance,dof]||give the confidence interval for the population variance given the sample variance variance and dof degrees of freedom|
|FRatioCI[ratio,numdof,dendof]||give the confidence interval for the ratio of population variances, given the ratio of sample variances ratio and where the sample variances in the numerator and denominator have numdof and dendof degrees of freedom|