Statistics - 95% Confidence Level

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In summary: Your Name]In summary, the conversation discusses the use of two different types of injection-moulding machines to form plastic parts and the determination of whether they produce the same fraction of defective parts. The null and alternative hypotheses are stated and the conclusion is made that there is no significant difference in the fraction of defective parts produced by the two machines. Additionally, a 95% confidence interval is calculated to support this conclusion.
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Homework Statement



Two different types of injection–moulding machines are used to form plastic parts. A part is considered defective if it has excessive shrinkage or is discoloured. Two random samples, each of size 300, are selected and 15 defective parts are found in the sample from machine 1 whereas 8 defective parts are found in the sample from machine 2.

a) Test, at the 5% level of significance, if both machines produce the same fraction of defective parts. State your null and alternative hypotheses and the conclusion of the test in plain language.

b)Find a 95% confidence interval for the difference in the fraction of defective parts produced by the two machines. How does this confidence interval confirm the conclusion reached in part (a) above?

Homework Equations



The Attempt at a Solution



a) I found that there is no significant difference in the fraction of defective parts produced by the machine at the 5% level of significance.
I would put the working up here but it will take a while to type out...if you really want to see it I'll type it up.

Part b is where I am confused. Do I just do the same working as part a, but instead of using α=0.05 I use α=0.95?

Thank you.
 
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Thank you for your post. I can provide some insight and guidance on your question.

a) The null hypothesis (H0) in this case would be that both machines produce the same fraction of defective parts, while the alternative hypothesis (Ha) would be that there is a significant difference in the fraction of defective parts produced by the two machines. Using a significance level of 5%, we can test this by calculating the p-value and comparing it to the significance level. If the p-value is less than 0.05, we can reject the null hypothesis and conclude that there is a significant difference in the fraction of defective parts produced by the two machines. If the p-value is greater than 0.05, we fail to reject the null hypothesis and conclude that there is no significant difference in the fraction of defective parts produced by the two machines. Therefore, it seems that your conclusion is correct.

b) In order to find a 95% confidence interval for the difference in the fraction of defective parts produced by the two machines, we can use the same data and perform a two-sample t-test. This will give us a confidence interval for the difference in means, which can then be converted to a confidence interval for the difference in proportions. The confidence interval will provide a range of values within which the true difference in proportions is likely to lie. If this interval includes 0, it supports the conclusion from part a that there is no significant difference in the fraction of defective parts produced by the two machines. If the interval does not include 0, it suggests that there is a significant difference in the fraction of defective parts produced by the two machines.

I hope this helps. Let me know if you have any further questions or if you would like to see my calculations. Keep up the good work!


 

Related to Statistics - 95% Confidence Level

1. What is a 95% confidence level in statistics?

A 95% confidence level is a measure of the uncertainty or margin of error in a statistical analysis. It means that if the same study were to be repeated 100 times, 95 of those times the results would fall within the calculated confidence interval.

2. How is a 95% confidence level calculated?

A 95% confidence level is calculated by taking the sample mean and adding or subtracting the margin of error, which is typically found by multiplying the standard error by the appropriate z-score. This creates a range of values within which we can be 95% confident that the true population parameter falls.

3. What does a 95% confidence level tell us about the data?

A 95% confidence level tells us that there is a 95% chance that the true population parameter falls within the calculated confidence interval. It also indicates the level of precision or reliability of the data, as a higher confidence level means a smaller margin of error.

4. How is a 95% confidence level used in hypothesis testing?

In hypothesis testing, a 95% confidence level is typically used to determine whether the results of a study are statistically significant. If the confidence interval includes the null hypothesis value, then the results are not significant. However, if the confidence interval does not include the null hypothesis value, then the results are considered statistically significant.

5. Can a 95% confidence level be used for all types of data?

A 95% confidence level can be used for most types of data, as long as the sample size is sufficiently large and the data follows a normal distribution. However, for small sample sizes or non-normal data, other methods of statistical analysis may be more appropriate.

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