Make a confidence interval for given the following values.
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically1h 48m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables2h 55m
- 6. Normal Distribution & Continuous Random Variables1h 48m
- 7. Sampling Distributions & Confidence Intervals: Mean2h 8m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 20m
- 9. Hypothesis Testing for One Sample2h 23m
- 10. Hypothesis Testing for Two Samples3h 25m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 30m
- 14. ANOVA1h 4m
8. Sampling Distributions & Confidence Intervals: Proportion
Confidence Intervals for Population Proportion
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A factory manager wants to estimate the proportion of defective items produced. In a batch of 20 items, the factory has produced 6 with defects. Find the margin of error for a 98% confidence interval for the true proportion of defective items.
A
E=0.3
B
E=0.238
C
E=0.169
D
E=0.062

1
First, calculate the sample proportion (p̂) of defective items. This is done by dividing the number of defective items by the total number of items in the sample. In this case, p̂ = 6/20.
Next, determine the z-score associated with a 98% confidence level. You can find this value in a standard normal distribution table or use a calculator. For a 98% confidence interval, the z-score is approximately 2.33.
Now, calculate the standard error (SE) of the sample proportion using the formula: SE = sqrt((p̂ * (1 - p̂)) / n), where n is the sample size.
Use the z-score and the standard error to calculate the margin of error (E) for the confidence interval. The formula is: E = z * SE.
Finally, interpret the margin of error in the context of the problem. The margin of error provides a range within which the true proportion of defective items is likely to fall, given the sample data and confidence level.
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