Here are summary statistics for randomly selected weights of newborn girls: n= 191, x 26.4 hg, s = 7.2 hg. Construct a confidence interval estimate of the mean. Use a 99% confidence level. Are these results very different from the confidence interval 24.4 hg

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### Confidence Interval for Population Mean

**Problem Statement:**
Here are summary statistics for randomly selected weights of newborn girls: 
- Sample size (n) = 191 
- Sample mean (x̄) = 26.4 hg 
- Sample standard deviation (s) = 7.2 hg

Construct a confidence interval estimate of the mean at a 99% confidence level. Determine if these results are significantly different from the confidence interval 24.4 hg < μ < 28.0 hg with only 18 sample values, x̄ = 26.2 hg, and s = 2.7 hg.

**Question:**
What is the confidence interval for the population mean (μ)?

\[ \text{__ hg} < \mu < \text{__ hg} \]
(Round to one decimal place as needed.)

### Solution:

We use the formula for the confidence interval for a population mean when the standard deviation is unknown:

\[ \text{Confidence Interval} = x̄ \pm t \frac{s}{\sqrt{n}} \]

Where:
- \( x̄ \) is the sample mean
- \( t \) is the t-score for the 99% confidence level and degrees of freedom (\( df = n-1 \))
- \( s \) is the sample standard deviation
- \( n \) is the sample size

Steps:
1. **Determine the t-score:**
   Given the degrees of freedom \( df = 191 - 1 = 190 \), and the confidence level of 99%, the t-score can be found using a t-distribution table or statistical software.

2. **Calculations:**
   - Sample mean \( x̄ = 26.4 \) hg
   - Sample standard deviation \( s = 7.2 \) hg
   - Sample size \( n = 191 \)

### Graphical Representation:
In this context, a detailed explanation of the confidence interval calculation is performed, often represented using error bars on a graph showing the sample mean and the confidence interval margins. Here is the breakdown:

**Formula Application:**
\[
\text{Margin of Error (E)} = t \times \frac{s}{\sqrt{n}}
\]

By finding the exact t-score (typically using software or t-distribution tables), input the values and solve for the confidence interval range. Round final answers to one decimal place for consistency
Transcribed Image Text:### Confidence Interval for Population Mean **Problem Statement:** Here are summary statistics for randomly selected weights of newborn girls: - Sample size (n) = 191 - Sample mean (x̄) = 26.4 hg - Sample standard deviation (s) = 7.2 hg Construct a confidence interval estimate of the mean at a 99% confidence level. Determine if these results are significantly different from the confidence interval 24.4 hg < μ < 28.0 hg with only 18 sample values, x̄ = 26.2 hg, and s = 2.7 hg. **Question:** What is the confidence interval for the population mean (μ)? \[ \text{__ hg} < \mu < \text{__ hg} \] (Round to one decimal place as needed.) ### Solution: We use the formula for the confidence interval for a population mean when the standard deviation is unknown: \[ \text{Confidence Interval} = x̄ \pm t \frac{s}{\sqrt{n}} \] Where: - \( x̄ \) is the sample mean - \( t \) is the t-score for the 99% confidence level and degrees of freedom (\( df = n-1 \)) - \( s \) is the sample standard deviation - \( n \) is the sample size Steps: 1. **Determine the t-score:** Given the degrees of freedom \( df = 191 - 1 = 190 \), and the confidence level of 99%, the t-score can be found using a t-distribution table or statistical software. 2. **Calculations:** - Sample mean \( x̄ = 26.4 \) hg - Sample standard deviation \( s = 7.2 \) hg - Sample size \( n = 191 \) ### Graphical Representation: In this context, a detailed explanation of the confidence interval calculation is performed, often represented using error bars on a graph showing the sample mean and the confidence interval margins. Here is the breakdown: **Formula Application:** \[ \text{Margin of Error (E)} = t \times \frac{s}{\sqrt{n}} \] By finding the exact t-score (typically using software or t-distribution tables), input the values and solve for the confidence interval range. Round final answers to one decimal place for consistency
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