https://file.aiquickdraw.com/mathbot/file/1730034465557370s5ezx.pdf

The uploaded document contains a problem set from a hypothesis testing course, covering uniformly most powerful (UMP) tests and related statistical inference techniques. Here’s a brief summary of each question in the problem set:

1. **Bernoulli Distribution Testing**: Derive a UMP test for testing \( H_0: \theta = \frac{1}{2} \) against \( H_1: \theta < \frac{1}{2} \), with a random sample from a Bernoulli distribution.

2. **Normal Distribution Testing (Mean)**: Find a UMP test for testing \( H_0: \theta = 75 \) versus \( H_1: \theta > 75 \), given a sample from \( N(\theta, 100) \), with \( \alpha = 0.05 \).

3. **Sample Size and Power Function**: Determine the sample size and derive the UMP test for testing \( H_0: \theta = 25 \) versus \( H_1: \theta < 25 \) from a \( N(\theta, 16) \) distribution, ensuring specific power values at given points.

4. **Testing for Variance in Normal Distribution**: Show that a particular set forms a UMP critical region for testing \( H_0: \theta = 2 \) against \( H_1: \theta < 2 \), with a normal distribution sample and derive the critical value for \( n = 10 \), \( \alpha = 0.05 \).

5. **Testing for Monotone Likelihood Ratio (MLR)**: Use the MLR property of the normal distribution to derive the UMP test for \( H_0: \theta = 4 \) versus \( H_1: \theta > 4 \).

6. **Geometric Distribution Testing**: Derive a UMP test for \( H_0: \theta \geq \theta_0 \) versus \( H_1: \theta < \theta_0 \), where \( \theta \in (0,1) \).

7. **Normal Distribution Testing (Variance)**: Show that the family of normal distributions has an MLR in terms of variance and use it to derive the UMP test for \( H_0: \sigma^2 \leq \sigma_0^2 \) versus \( H_1: \sigma^2 > \sigma_0^2 \).

8. **Exponential Distribution Testing**: Derive a UMP test for testing \( H_0: \beta = \frac{1}{2} \) versus \( H_1: \beta < \frac{1}{2} \), based on a sample from an exponential distribution.

9. **Poisson Distribution Testing**: Derive a UMP test for \( H_0: \lambda \leq 4.0 \) versus \( H_1: \lambda > 4.0 \).

10. **Distribution with Specific Density Function**: Derive a UMP test for \( H_0: \theta = 6 \) versus \( H_1: \theta > 6 \), given a sample from a distribution with a provided density function.

11. **Significance Testing for Poisson Random Variable**: Determine whether a reduction in the average number of accidents is significant, based on a change from \( \lambda = 3.0 \) to \( \lambda = 2.5 \).

12. **Test for a Population with a Given Density Function**: Derive the most powerful test for \( H_0: \theta = 3 \) versus \( H_1: \theta > 3 \), using a specific form of density function.

Would you like a detailed explanation or solution for any of these problems?

### Related Questions:
1. How do you derive a UMP test for a given hypothesis?
2. What is the Monotone Likelihood Ratio (MLR) property, and why is it important for hypothesis testing?
3. How do power functions influence the design of hypothesis tests?
4. What are critical regions, and how are they determined in hypothesis testing?
5. How can changes in sample size affect the power of a hypothesis test?

### Tip:
When solving hypothesis testing problems, always verify if the distribution satisfies the conditions for using a UMP test or if an alternative test is more appropriate.

Problem set from a hypothesis testing course covering uniformly most powerful (UMP) tests.

Explore solutions to hypothesis testing problems focused on uniformly most powerful (UMP) tests. Topics include Bernoulli distribution testing, normal distribution tests for mean and variance, sample size considerations, and critical region derivations. Suitable for undergraduate students in advanced statistics.

Math Problem Statement

Solution

Ask a new question for Free

By Image

Math Problem Analysis

Mathematical Concepts

Formulas

Theorems

Suitable Grade Level

Related Recommendation