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 Data1h 14m
- 2. Describing Data with Tables and Graphs1h 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope31m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression15m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 28m
7. Sampling Distributions & Confidence Intervals: Mean
Confidence Intervals for Population Mean
Problem 5.R.51b
Textbook Question
In Exercises 51 and 52, a population and sample size are given. (b) List all samples (with replacement) of the given size from the population and find the mean of each. (c) Find the mean and standard deviation of the sampling distribution of sample means and compare them with the mean and standard deviation of the population.
The goals scored in a season by the four starting defenders on a soccer team are 1, 2, 0, and 3. Use a sample size of 2.
Verified step by step guidance1
Step 1: Understand the problem. We are given a population of values {1, 2, 0, 3} representing the goals scored by four defenders. We need to consider all possible samples of size 2 (with replacement) from this population, calculate the mean of each sample, and then analyze the sampling distribution of sample means.
Step 2: List all possible samples of size 2 with replacement. Since there are 4 elements in the population and sampling is done with replacement, the total number of samples is 4 × 4 = 16. The samples are: (1,1), (1,2), (1,0), (1,3), (2,1), (2,2), (2,0), (2,3), (0,1), (0,2), (0,0), (0,3), (3,1), (3,2), (3,0), (3,3).
Step 3: Calculate the mean of each sample. For each sample, compute the mean using the formula: , where x and y are the two values in the sample. For example, for the sample (1,1), the mean is (1+1)/2 = 1. Repeat this for all 16 samples.
Step 4: Find the mean and standard deviation of the sampling distribution of sample means. The mean of the sampling distribution is the average of all sample means. The standard deviation of the sampling distribution is calculated using the formula: , where x is each sample mean, μ is the mean of the sampling distribution, and n is the number of samples.
Step 5: Compare the mean and standard deviation of the sampling distribution with the population mean and standard deviation. The population mean is calculated as , where x represents the population values and N is the population size. The population standard deviation is calculated using the formula: . Compare these values to the mean and standard deviation of the sampling distribution to observe the relationship.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sampling with Replacement
Sampling with replacement means that after selecting an item from a population, it is returned to the population before the next selection. This allows for the same item to be chosen multiple times in different samples. In the context of the question, it means that when forming samples of size 2 from the defenders' goals, each goal can be selected more than once, leading to a larger number of possible samples.
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Sampling Distribution of Sample Proportion
Mean of a Sample
The mean of a sample is calculated by summing all the values in the sample and dividing by the number of values. It provides a measure of central tendency for the sample. In this exercise, after listing all possible samples of size 2, you will compute the mean for each sample to understand the average goals scored in those samples.
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Sampling Distribution of Sample Proportion
Sampling Distribution of Sample Means
The sampling distribution of sample means is the distribution of the means of all possible samples of a given size from a population. It is important because it allows us to understand how sample means vary and how they relate to the population mean. The mean and standard deviation of this distribution can be compared to the population's mean and standard deviation to assess the accuracy and reliability of the sample estimates.
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Sampling Distribution of Sample Proportion
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