Statistics: finding a critical region

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SUMMARY

The discussion focuses on deriving a 5%-level critical region using the likelihood ratio test (LRT) for testing the null hypothesis H0: σ^2 = 1 against the alternative hypothesis H1: σ^2 ≠ 1, based on a sample from the Normal(3, σ^2) distribution. The observed sample values are 2.82, 2.35, 3.74, 1.93, 4.51, 3.17, 1.41, 4.23, 4.77, and 3.89. Participants analyze whether to reject H0 at the 5% significance level, requiring justification based on the calculated likelihoods. The definition of the likelihood ratio test is also discussed, emphasizing its reliance on maximum likelihood estimation (MLE).

PREREQUISITES
  • Understanding of Normal distribution properties, specifically Normal(3, σ^2).
  • Familiarity with likelihood ratio tests (LRT) and their application in hypothesis testing.
  • Knowledge of maximum likelihood estimation (MLE) techniques.
  • Basic statistical significance concepts, particularly the 5% significance level.
NEXT STEPS
  • Study the derivation of the likelihood ratio test for different distributions.
  • Learn how to calculate maximum likelihood estimates for parameters in a Normal distribution.
  • Explore the implications of Type I and Type II errors in hypothesis testing.
  • Investigate the use of statistical software (e.g., R or Python) for performing likelihood ratio tests.
USEFUL FOR

Statisticians, data analysts, and researchers involved in hypothesis testing and statistical inference, particularly those working with Normal distributions and likelihood ratio tests.

Aria1
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Let X1, X2, …, X10 be a random sample of size ten from the Normal(3, σ^2) distribution.
(a) Use the likelihood ratio test to derive a 5%-level critical region for testing H0 : σ^2 = 1
versus H1 : σ^2 ≠ 1.
(b)Suppose the following ten values from the Normal(3, σ^2) distribution are observed. Do we
reject the null hypothesis H0 : σ^2 = 1 at 5%-level? Please justify your answer.
2.82, 2.35, 3.74, 1.93, 4.51, 3.17, 1.41, 4.23, 4.77, 3.89
 
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Hey Aria1.

What is the definition of the LRT? (Hint: It involves the MLE likelihood and the likelihood of the null hypothesis).
 

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