# Difference between revisions of "Eleisha's Segment 19: The Chi-Square Statistic"

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1. Prove the assertion on lecture slide 5, namely that, for a multivariate normal distribution, the quantity <math> ({\mathbf x-\mathbf\mu})^T{\mathbf\Sigma}^{-1}({\mathbf x-\mathbf\mu}), </math> where <math> \mathbf x </math>is a random draw from the multivariate normal, is <math> \chi^2 </math> distributed. | 1. Prove the assertion on lecture slide 5, namely that, for a multivariate normal distribution, the quantity <math> ({\mathbf x-\mathbf\mu})^T{\mathbf\Sigma}^{-1}({\mathbf x-\mathbf\mu}), </math> where <math> \mathbf x </math>is a random draw from the multivariate normal, is <math> \chi^2 </math> distributed. | ||

## Revision as of 13:02, 20 April 2014

** To Calculate **

1. Prove the assertion on lecture slide 5, namely that, for a multivariate normal distribution, the quantity **Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle ({\mathbf x-\mathbf\mu})^T{\mathbf\Sigma}^{-1}({\mathbf x-\mathbf\mu}), }**
where **Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mathbf x }**
is a random draw from the multivariate normal, is **Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \chi^2 }**
distributed.

**To Think About **

1. Why are we so interested in t-values? Why do we square them?

2. Suppose you measure a bunch of quantities **Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x_i }**
, each of which is measured with a measurement accuracy **Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \sigma_i }**
and has a theoretically expected value **Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \mu_i }**
. Describe in detail how you might use a chi-square test statistic as a p-value test to see if your theory is viable? Should your test be 1 or 2 tailed?

**Back To: ** Eleisha Jackson