A delivery service tracks the weights of its packages. A sample of 20 packages has a variance of 4.5 lbs2. Construct a 95% conf. int. for the population variance. Assume a normal distribution.
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- 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 20m
- 9. Hypothesis Testing for One Sample5h 13m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors15m
- 10. Hypothesis Testing for Two Samples5h 35m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 2m
- 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 Calculator15m
- 11. Correlation1h 24m
- 12. Regression1h 59m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 1m
8. Sampling Distributions & Confidence Intervals: Proportion
Confidence Intervals for Population Variance
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
What is wrong with expressing the confidence interval as ?
A
5.1 is not the midpoint between 3.8 and 6.4.
B
The values 3.8 and 6.4 are impossible because variance must be less than 3.
C
The point estimate for σ2 is not the midpoint of a confidence interval and 1.3 is not a margin of error since the χ2 distribution is asymmetric.
D
Confidence intervals can only be written for means or proportion, not for variance.
Verified step by step guidance1
Recognize that a confidence interval for a variance \(\sigma^2\) is typically expressed as an inequality, for example, \(L < \sigma^2 < U\), where \(L\) and \(U\) are the lower and upper bounds derived from the \(\chi^2\) distribution.
Understand that the variance confidence interval is not symmetric because it is based on the \(\chi^2\) distribution, which is asymmetric, so the midpoint \((L + U)/2\) is not the point estimate for \(\sigma^2\).
Note that expressing the confidence interval as \(\sigma^2 = \text{point estimate} \pm \text{margin of error}\) assumes symmetry and a normal distribution, which does not hold for variance intervals.
Recall that the point estimate for variance is usually the sample variance \(s^2\), but it is not necessarily the midpoint of the confidence interval bounds.
Therefore, the correct way to express a confidence interval for variance is by stating the interval bounds explicitly, not by using a midpoint plus or minus a margin of error.
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