Graphical Analysis In Exercises 9–12, use the values on the number line to find the sampling error.
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 6.1.22
Textbook Question
In Exercises 21–24, construct the indicated confidence interval for the population mean μ.
c = 0.95, xbar = 31.39, σ = 0.80, n = 82.

1
Step 1: Identify the components needed for constructing the confidence interval. These include the sample mean (x̄ = 31.39), population standard deviation (σ = 0.80), sample size (n = 82), and the confidence level (c = 0.95).
Step 2: Determine the critical value (z*) corresponding to the confidence level of 0.95. For a 95% confidence level, the z* value can be found using a standard normal distribution table or calculator. The z* value is approximately 1.96.
Step 3: Calculate the standard error of the mean (SE). The formula for SE is: . Substitute σ = 0.80 and n = 82 into the formula.
Step 4: Compute the margin of error (ME). The formula for ME is: . Use the z* value from Step 2 and the SE calculated in Step 3.
Step 5: Construct the confidence interval. The formula for the confidence interval is: . Substitute x̄ = 31.39 and the ME from Step 4 into the formula.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Confidence Interval
A confidence interval is a range of values, derived from sample statistics, that is likely to contain the population parameter (like the mean) with a specified level of confidence. For example, a 95% confidence interval suggests that if we were to take many samples and construct intervals in the same way, approximately 95% of those intervals would contain the true population mean.
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Sample Mean (x̄)
The sample mean, denoted as x̄, is the average of a set of observations from a sample. It serves as a point estimate of the population mean (μ). In the given question, x̄ = 31.39 indicates the average value calculated from the sample of size n = 82.
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Standard Deviation (σ) and Sample Size (n)
The standard deviation (σ) measures the dispersion or variability of a set of data points around the mean. In this case, σ = 0.80 indicates how spread out the sample values are. The sample size (n) refers to the number of observations in the sample, which is crucial for determining the reliability of the confidence interval; here, n = 82 provides a basis for estimating the population mean.
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