Body Temperature Data Set 5 “Body Temperatures” in Appendix B includes a sample of 106 body temperatures having a mean of and a standard deviation of 0.62F (for day 2 at 12 AM). Construct a 95% confidence interval estimate of the standard deviation of the body temperatures for the entire population.
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8. Sampling Distributions & Confidence Intervals: Proportion
Confidence Intervals for Population Variance
Problem 7.4.23a
Textbook Question
Analysis of Last Digits Weights of respondents were recorded as part of the California Health Interview Survey. The last digits of weights from 50 randomly selected respondents are listed below.

a. Use the bootstrap method with 1000 bootstrap samples to find a 95% confidence interval estimate of .
Verified step by step guidance1
Step 1: Understand the bootstrap method. The bootstrap method involves resampling the given data with replacement to create multiple simulated samples. These samples are used to estimate the variability of a statistic, such as the mean or median, and construct confidence intervals.
Step 2: Extract the last digits from the image provided. The last digits are: 5, 0, 1, 0, 2, 0, 5, 0, 5, 0, 3, 8, 5, 0, 5, 6, 0, 0, 0, 0, 0, 8, 5, 5, 0, 4, 5, 0, 4, 0, 0, 0, 0, 8, 0, 9, 5, 3, 0, 5, 0, 0, 0, 5, 8.
Step 3: Generate 1000 bootstrap samples. For each bootstrap sample, randomly select 50 values from the original dataset with replacement. This means some values may appear multiple times in a single sample, while others may not appear at all.
Step 4: Calculate the statistic of interest (e.g., mean, median, etc.) for each of the 1000 bootstrap samples. This will give you a distribution of the statistic based on the resampled data.
Step 5: Construct the 95% confidence interval. Sort the bootstrap statistics in ascending order and identify the values at the 2.5th percentile and the 97.5th percentile of the distribution. These values form the lower and upper bounds of the confidence interval.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Bootstrap Method
The bootstrap method is a resampling technique used to estimate the distribution of a statistic by repeatedly sampling with replacement from the observed data. This approach allows for the estimation of confidence intervals and standard errors without relying on strong parametric assumptions. In this case, it involves creating 1000 bootstrap samples from the last digits of weights to assess the variability and derive a 95% confidence interval.
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Confidence Interval
A confidence interval is a range of values, derived from sample statistics, that is likely to contain the true population parameter with a specified level of confidence, typically 95%. It provides an estimate of uncertainty around a sample statistic, indicating how much the sample might differ from the actual population. In this context, the confidence interval will help quantify the uncertainty in estimating the mean or proportion of the last digits of weights.
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Sampling Distribution
The sampling distribution is the probability distribution of a statistic obtained from a large number of samples drawn from a specific population. It describes how the statistic varies from sample to sample and is fundamental in inferential statistics. Understanding the sampling distribution is crucial for applying the bootstrap method, as it allows for the estimation of the variability of the sample statistic and the construction of confidence intervals.
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