1) A null Hypothesis assumes that a process is producing no more than a maximum allowable rate of defective items. The Type II error is to conclude that the process?
2) If 95% confidence limits for the mean Mu turn out to be (6.5,8.5)
Plz help me
hey @malliene
the answer is on this website
www.jiskha.com/questions/1/1-A-null-Hypothesis-assumes-that-a-process-is-producing-no-more-than-a-maximum-allowable
1) In hypothesis testing, the null hypothesis (H0) assumes that a process is producing no more than a maximum allowable rate of defective items. A Type II error occurs when we incorrectly fail to reject the null hypothesis, even though the process is actually producing more than the maximum allowable rate of defective items. In other words, we incorrectly conclude that the process is acceptable when it is not.
To avoid committing a Type II error, we need to properly design our hypothesis test by selecting an appropriate sample size, determining the significance level (alpha), and specifying the desired power of the test. By doing so, we can reduce the chance of making a Type II error and ensure that our conclusions are valid in light of the evidence.
2) The 95% confidence limits for the mean Mu (μ) are given as (6.5, 8.5). This means that with 95% confidence, the true population mean falls between 6.5 and 8.5.
To calculate these confidence limits, you would typically use a sample mean and the margin of error. The margin of error is determined by the sample size, the standard deviation, and the desired level of confidence (often represented as alpha or 1 - significance level). By using a formula such as:
Margin of Error = Z * (Standard Deviation / sqrt(n))
where Z is the critical value based on the desired level of confidence and n is the sample size, you can determine the range within which the population mean is likely to fall.
In this case, the confidence limits (6.5, 8.5) suggest that based on the data and the chosen level of confidence, we can estimate that the population mean falls within this range.