Find the left and right -values for a 99% confidence interval with a sample size of 25.
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
8. Sampling Distributions & Confidence Intervals: Proportion
Chi Square Distribution
Problem 9.4.3
Textbook Question
True or False: To construct a confidence interval about a population variance or standard deviation, either the population from which the sample is drawn must be normal, or the sample size must be large.
Verified step by step guidance1
Understand the context: When constructing a confidence interval for a population variance or standard deviation, the sampling distribution of the sample variance plays a crucial role.
Recall the key assumption: The sample variance follows a chi-square distribution only if the population is normally distributed. This is essential for exact confidence intervals based on the chi-square distribution.
Consider the sample size: For large sample sizes, the Central Limit Theorem helps approximate the distribution of the sample variance, but this approximation is not as straightforward or commonly used as for means.
Evaluate the statement: The condition that either the population must be normal or the sample size must be large is related to ensuring the validity of the confidence interval for variance or standard deviation.
Conclusion: Since the chi-square method requires normality and there is no widely accepted large-sample alternative for variance confidence intervals, the statement is generally considered true.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Confidence Interval for Population Variance
A confidence interval for population variance estimates the range within which the true variance lies with a certain level of confidence. It is typically constructed using the chi-square distribution when the population is normal, as variance is sensitive to distribution shape.
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Normality Assumption
The normality assumption means the population data follows a normal distribution. This assumption is crucial for variance confidence intervals because the chi-square method relies on normality to ensure the sampling distribution of variance is well-defined and accurate.
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Finding Z-Scores for Non-Standard Normal Variables
Sample Size and the Central Limit Theorem
For large sample sizes, the Central Limit Theorem allows approximation of sampling distributions to normality, reducing dependence on the population's shape. However, for variance estimation, large samples alone do not guarantee the chi-square distribution applies, so normality remains important.
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Central Limit Theorem
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