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 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
7. Sampling Distributions & Confidence Intervals: Mean
Introduction to Confidence Intervals
Problem 5.4.17
Textbook Question
Finding Probabilities In Exercises 15–18, the population mean and standard deviation are given. Find the indicated probability and determine whether the given sample mean would be considered unusual.
For a random sample of n=45, find the probability of a sample mean being greater than 551 when mu=550 and sigma=3.7.

1
Step 1: Identify the given values in the problem. The population mean (μ) is 550, the population standard deviation (σ) is 3.7, the sample size (n) is 45, and we are tasked with finding the probability of the sample mean (̄) being greater than 551.
Step 2: Calculate the standard error of the mean (SE). The formula for the standard error is: . Substitute the values: .
Step 3: Compute the z-score for the sample mean. The formula for the z-score is: . Substitute the values: .
Step 4: Use the z-score to find the probability. Look up the z-score in the standard normal distribution table or use statistical software to find the cumulative probability. Since the problem asks for the probability of the sample mean being greater than 551, calculate .
Step 5: Determine if the sample mean is unusual. A sample mean is considered unusual if the probability is less than 0.05 (5%). Compare the calculated probability to this threshold to make the determination.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Central Limit Theorem
The Central Limit Theorem states that the distribution of 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 crucial for calculating probabilities related to sample means.
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Calculating the Mean
Standard Error
Standard Error (SE) measures the dispersion of sample means around the population mean. It is calculated as the population standard deviation divided by the square root of the sample size (SE = sigma / √n). In this case, it helps determine how much variability to expect in the sample mean.
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Calculating Standard Deviation
Z-Score
A Z-score indicates how many standard deviations an element is from the mean. It is calculated using the formula Z = (X - mu) / SE, where X is the sample mean. This score is essential for finding probabilities in a standard normal distribution and determining if a sample mean is unusual.
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Z-Scores From Given Probability - TI-84 (CE) Calculator
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