The univariate t statistic is well-known to most data analysts. If a random sample of n observations is taken from a normal distribution with mean μ and variance σ 2, the form of the statistic is given by
where \(\overline{x}\) is the sample mean and s is the sample standard deviation. This statistic has a Student t-distribution with (n − 1) degrees of freedom. Squaring the statistic, we obtain
This value can be defined as the squared Euclidean distance between \(\overline{x}\) and μ.
A multivariate analogue to the t statistic can easily be constructed. Suppose a random sample of n observation vectors, given by x 1, x 2, …, x n , where x i ′ = (x i1, x i2, …, x ip )′, is taken from a p-variate multivariate normal distribution (see Multivariate Normal Distributions) with mean vector u and covariance matrix, Σ. Then the multivariate version of the tstatistic...
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References and Further Reading
Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 8:376–378
Hotelling H (1931) The generalization of Student’s ratio. Ann Math Stat 2:360–378
Hotelling H (1947) Multivariate quality control-illustrated by the air testing of sample bombsights. Tech Stat Anal (Eisenhart C, Hastay MW, Wallis WA (eds)) McGraw-Hill, New York, pp 111–184
Johnson RA, DW Wichern J (2008) Applied multivariate statistical analysis, 6th edn. Prentice Hall, New Jersey
Mahalanobis PC (1930) On tests and measures of group divergence. I.F. Proc. Asiat. Soc. Bengal 26:541
Mason RL, Young JC (2002) Multivariate statistical process control with industrial applications. ASA-SIAM, Philadelphia, PA
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Mason, R.L., Young, J.C. (2011). Hotelling’s T 2Statistic. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_292
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