Question
Use the Neyman-Pearson Lemma to obtain the best critical region for testing =
against
=
>
in the case of a normal population
N(,
), where
is known. Hence find the power of the test.
Answer :
Word Count : 606
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The Neyman-Pearson Lemma provides a method for constructing the best test for simple hypotheses, i.e., a hypothesis testing problem where the null hypothesis \( H_0 \) and the alternative hypothesis \( H_1 \) specify exact values for the parameters. Let's break the problem into its components and solve it step by step: ### Problem Setup: We are given that the random variable \( X \) follows a normal distribution: \[ X \sim N(\theta, \sigma^2), \] where \( \theta \) is the mean of the population and \( \sigma^2 \) is the known variance. We are tasked with testing: \[ H_0: \theta = \theta_0 \quad \text{against} \quad H_1: \theta = \theta_1, \quad \theta_1 > \theta_0. \] ### Step 1: Likelihood Ratio Test According to the Neyman-Pearson Lemma, the best critical region (i.e., the most powerful test) is given by the likelihood ratio test, which uses the likelihood ratio statistic: \[ \Lambda(x) = \frac{L(\theta_0 | x)}{L(\theta_1 | x)}, \] where \( L(\theta | x) \) is the likelihood function for the parameter \( \theta \) given the observed data \( x \). For a
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