Assume that wve want to construct a confidence interval. Do one of the following, as appropriate: (a) find the critical value ta/2. (b) find the critical value za/2, or (c) state that neither the normal distribution nor the t distribution applies. Here are summary statistics for randomly selected weights of newborn girls: n= 190, x 29 7 hg, s = 6.6 hg. The confidence level is 95%.

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### Constructing a Confidence Interval

#### Problem Statement

Assume that we want to construct a confidence interval. Do one of the following, as appropriate: 
- (a) find the critical value \( t_{\alpha/2} \)
- (b) find the critical value \( z_{\alpha/2} \)
- (c) state that neither the normal distribution nor the t distribution applies.

#### Provided Data

Here are summary statistics for randomly selected weights of newborn girls:
- Sample size (\( n \)): 190
- Sample mean (\( \bar{x} \)): 29.7 hg
- Sample standard deviation (\( s \)): 6.6 hg

The confidence level is 95%.

#### Instructions

Select the correct choice below and, if necessary, fill in the answer box to complete your choice.

#### Options

- **A.** \( t_{\alpha/2} = \) [   ]  
  (Round to two decimal places as needed.)

- **B.** \( z_{\alpha/2} = \) [   ]  
  (Round to two decimal places as needed.)

- **C.** Neither the normal distribution nor the t distribution applies.

---

### Detailed Explanation of the Options

- **Option A: Using the t-distribution**
  - The t-distribution is typically used when the sample size is small (\( n < 30 \)) and/or the population standard deviation is unknown.
  - You need to find the critical value \( t_{\alpha/2} \) for the given confidence level and degrees of freedom (\( df = n - 1 \)).

- **Option B: Using the z-distribution**
  - The z-distribution is used when the sample size is large (\( n \geq 30 \)) and the population standard deviation is known or can be approximated well by the sample standard deviation.
  - You need to find the critical value \( z_{\alpha/2} \) for the given confidence level.

- **Option C: Neither distribution applies**
  - This would be applicable in cases where the underlying assumptions for both the normal distribution (z-distribution) and t-distribution are violated or not met.

This kind of decision-making process is fundamental when performing inferential statistics to ensure that the resulting confidence intervals are valid based on the specified criteria and known data properties.
Transcribed Image Text:### Constructing a Confidence Interval #### Problem Statement Assume that we want to construct a confidence interval. Do one of the following, as appropriate: - (a) find the critical value \( t_{\alpha/2} \) - (b) find the critical value \( z_{\alpha/2} \) - (c) state that neither the normal distribution nor the t distribution applies. #### Provided Data Here are summary statistics for randomly selected weights of newborn girls: - Sample size (\( n \)): 190 - Sample mean (\( \bar{x} \)): 29.7 hg - Sample standard deviation (\( s \)): 6.6 hg The confidence level is 95%. #### Instructions Select the correct choice below and, if necessary, fill in the answer box to complete your choice. #### Options - **A.** \( t_{\alpha/2} = \) [ ] (Round to two decimal places as needed.) - **B.** \( z_{\alpha/2} = \) [ ] (Round to two decimal places as needed.) - **C.** Neither the normal distribution nor the t distribution applies. --- ### Detailed Explanation of the Options - **Option A: Using the t-distribution** - The t-distribution is typically used when the sample size is small (\( n < 30 \)) and/or the population standard deviation is unknown. - You need to find the critical value \( t_{\alpha/2} \) for the given confidence level and degrees of freedom (\( df = n - 1 \)). - **Option B: Using the z-distribution** - The z-distribution is used when the sample size is large (\( n \geq 30 \)) and the population standard deviation is known or can be approximated well by the sample standard deviation. - You need to find the critical value \( z_{\alpha/2} \) for the given confidence level. - **Option C: Neither distribution applies** - This would be applicable in cases where the underlying assumptions for both the normal distribution (z-distribution) and t-distribution are violated or not met. This kind of decision-making process is fundamental when performing inferential statistics to ensure that the resulting confidence intervals are valid based on the specified criteria and known data properties.
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