Second-Hand Smoke Refer to Data Set 15 “Passive and Active Smoke” and construct a 95% confidence interval estimates of the mean cotinine level in each of three samples: (1) people who smoke; (2) people who don’t smoke but are exposed to tobacco smoke at home or work; (3) people who don’t smoke and are not exposed to smoke. Measuring cotinine in people’s blood is the most reliable way to determine exposure to nicotine. What do the confidence intervals suggest about the effects of smoking and second-hand smoke?
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
7. Sampling Distributions & Confidence Intervals: Mean
Confidence Intervals for Population Mean
Problem 5.4.26
Textbook Question
Interpreting the Central Limit Theorem In Exercises 19–26, find the mean and standard deviation of the indicated sampling distribution of sample means. Then sketch a graph of the sampling distribution.
SAT Italian Subject Test The scores on the SAT Italian Subject Test for the 2018–2020 graduating classes are normally distributed, with a mean of 628 and a standard deviation of 110. Random samples of size 25 are drawn from this population, and the mean of each sample is determined.

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Step 1: Recall the Central Limit Theorem (CLT). The CLT states that the sampling distribution of the sample mean will be approximately normal if the sample size is sufficiently large, regardless of the population's distribution. In this case, the population is already normally distributed, so the sampling distribution of the sample mean will also be normal.
Step 2: Identify the population mean (μ) and population standard deviation (σ) from the problem. Here, the population mean is μ = 628, and the population standard deviation is σ = 110.
Step 3: Calculate the mean of the sampling distribution of the sample mean. According to the CLT, the mean of the sampling distribution is equal to the population mean. Therefore, the mean of the sampling distribution is μₓ̄ = μ = 628.
Step 4: Calculate the standard deviation of the sampling distribution of the sample mean, also known as the standard error (SE). The formula for the standard error is: , where n is the sample size. Substitute σ = 110 and n = 25 into the formula to find the standard error.
Step 5: Sketch the graph of the sampling distribution. Since the sampling distribution is normal, draw a bell-shaped curve centered at the mean μₓ̄ = 628. Label the x-axis with values representing the mean and standard deviations (e.g., μₓ̄ ± σₓ̄, μₓ̄ ± 2σₓ̄, etc.).

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Central Limit Theorem (CLT)
The Central Limit Theorem states that the distribution of the sample means will approach a normal distribution as the sample size increases, regardless of the population's distribution, provided the sample size is sufficiently large (typically n ≥ 30). This theorem is fundamental in statistics as it allows for the use of normal probability techniques to make inferences about population parameters based on sample statistics.
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Calculating the Mean
Sampling Distribution of the Sample Mean
The sampling distribution of the sample mean is the probability distribution of all possible sample means from a population. It is characterized by its mean, which equals the population mean, and its standard deviation, known as the standard error, which is the population standard deviation divided by the square root of the sample size. Understanding this concept is crucial for estimating population parameters and conducting hypothesis tests.
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Mean and Standard Deviation of Sampling Distribution
For a given population with mean (μ) and standard deviation (σ), the mean of the sampling distribution of the sample mean is equal to μ, while the standard deviation of the sampling distribution (standard error) is calculated as σ/√n, where n is the sample size. In the context of the SAT Italian Subject Test, this means that for samples of size 25, the mean will remain 628, and the standard deviation will be 110/√25 = 22.
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Sampling Distribution of Sample Proportion
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