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Confidence interval for normal sample variance

Lecture



The case of the famous middle [edit]

Let be   Confidence interval for normal sample variance - independent sampling from the normal distribution, where   Confidence interval for normal sample variance - known mean. Define arbitrary   Confidence interval for normal sample variance and build   Confidence interval for normal sample variance - confidence interval for unknown dispersion   Confidence interval for normal sample variance .

Statement. Random value

  Confidence interval for normal sample variance

has a distribution   Confidence interval for normal sample variance . Let be   Confidence interval for normal sample variance -   Confidence interval for normal sample variance -quantile of this distribution. Then we have:

  Confidence interval for normal sample variance .

After substitution of the expression for   Confidence interval for normal sample variance and simple algebraic transformations we get:

  Confidence interval for normal sample variance .

The case of the unknown mean [edit]

Let be   Confidence interval for normal sample variance - independent sampling from the normal distribution, where   Confidence interval for normal sample variance ,   Confidence interval for normal sample variance - unknown constants. Construct a confidence interval for the unknown variance   Confidence interval for normal sample variance .

Fisher's theorem for normal samples. Random value

  Confidence interval for normal sample variance ,

Where   Confidence interval for normal sample variance - unbiased sample variance, has a distribution   Confidence interval for normal sample variance . Then we have:

  Confidence interval for normal sample variance .

After substitution of the expression for   Confidence interval for normal sample variance and simple algebraic transformations we get:

  Confidence interval for normal sample variance .

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Probability theory. Mathematical Statistics and Stochastic Analysis

Terms: Probability theory. Mathematical Statistics and Stochastic Analysis