Suppose that you wanted to buy a new Honda Civic and you want to wait until you can get a good deal. You want to see if the usual/average price of a Honda Civic is below the blue book value of $21,550. You are unsure that the prices of the current listings around you are lower than the blue book value of a Honda Civic so you randomly sample 49 listings from the area and find their sample mean selling price is $20,015 and their sample standard deviation is $1750.

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**Evaluating Car Purchase: A Case Study**

Suppose you are interested in purchasing a new Honda Civic and want to ensure you get a good deal. To make an informed decision, you need to determine if the current average price of a Honda Civic in your area is below the blue book value, which is $21,550.

You are uncertain whether the local listings reflect lower prices than the blue book value. To investigate, you randomly sample 49 car listings from the area. From this sample, you find that the average selling price is $20,015, with a sample standard deviation of $1,750.

- **Blue book value:** $21,550
- **Sample size:** 49
- **Sample mean price:** $20,015
- **Sample standard deviation:** $1,750

This data will help you perform statistical analysis to determine if the current prices are indeed lower than the blue book value, which will guide you in making a well-informed car purchasing decision.
Transcribed Image Text:**Evaluating Car Purchase: A Case Study** Suppose you are interested in purchasing a new Honda Civic and want to ensure you get a good deal. To make an informed decision, you need to determine if the current average price of a Honda Civic in your area is below the blue book value, which is $21,550. You are uncertain whether the local listings reflect lower prices than the blue book value. To investigate, you randomly sample 49 car listings from the area. From this sample, you find that the average selling price is $20,015, with a sample standard deviation of $1,750. - **Blue book value:** $21,550 - **Sample size:** 49 - **Sample mean price:** $20,015 - **Sample standard deviation:** $1,750 This data will help you perform statistical analysis to determine if the current prices are indeed lower than the blue book value, which will guide you in making a well-informed car purchasing decision.
### Determining the Appropriate Null and Alternative Hypotheses

**Question**: What is the null and alternative hypothesis that will address your goal?

**Answer Choices**:

1. \( H_0 : \mu \geq 21550 \) vs. \( H_a : \mu < 21550 \)
2. \( H_0 : \mu \geq 20015 \) vs. \( H_a : \mu < 20015 \)
3. \( H_0 : \mu = 21550 \) vs. \( H_a : \mu \neq 21550 \)
4. \( H_0 : \bar{X} \geq 21550 \) vs. \( H_a : \bar{X} < 21550 \)

### Explanation:
In hypothesis testing, the null hypothesis (denoted as \( H_0 \)) is a statement that there is no effect or no difference, and it is the hypothesis that researchers aim to test against. The alternative hypothesis (denoted as \( H_a \) or \( H_1 \)) is what researchers want to prove.

Each answer choice involves different parameters:

- **Choice 1** and **Choice 2**: These choices frame the hypotheses in terms of the population mean (\( \mu \)).
- **Choice 3**: This choice considers the possibility that the population mean is equal to or not equal to a specific value.
- **Choice 4**: This choice uses the sample mean (\( \bar{X} \)) instead of the population mean (\( \mu \)).

Students should select the hypothesis pair that aligns with their specific research goals and questions. For example, if the goal is to determine if the population mean is less than a specific value ($21,550), then Choice 1 might be appropriate. 

**Note**: In choosing hypotheses, ensure to match the variable of interest and the direction of the conjecture (greater than, less than, or not equal).
Transcribed Image Text:### Determining the Appropriate Null and Alternative Hypotheses **Question**: What is the null and alternative hypothesis that will address your goal? **Answer Choices**: 1. \( H_0 : \mu \geq 21550 \) vs. \( H_a : \mu < 21550 \) 2. \( H_0 : \mu \geq 20015 \) vs. \( H_a : \mu < 20015 \) 3. \( H_0 : \mu = 21550 \) vs. \( H_a : \mu \neq 21550 \) 4. \( H_0 : \bar{X} \geq 21550 \) vs. \( H_a : \bar{X} < 21550 \) ### Explanation: In hypothesis testing, the null hypothesis (denoted as \( H_0 \)) is a statement that there is no effect or no difference, and it is the hypothesis that researchers aim to test against. The alternative hypothesis (denoted as \( H_a \) or \( H_1 \)) is what researchers want to prove. Each answer choice involves different parameters: - **Choice 1** and **Choice 2**: These choices frame the hypotheses in terms of the population mean (\( \mu \)). - **Choice 3**: This choice considers the possibility that the population mean is equal to or not equal to a specific value. - **Choice 4**: This choice uses the sample mean (\( \bar{X} \)) instead of the population mean (\( \mu \)). Students should select the hypothesis pair that aligns with their specific research goals and questions. For example, if the goal is to determine if the population mean is less than a specific value ($21,550), then Choice 1 might be appropriate. **Note**: In choosing hypotheses, ensure to match the variable of interest and the direction of the conjecture (greater than, less than, or not equal).
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