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Question: A company is interested in estimating the mean number of days of sick leave taken by its employees. The firm's statistician randomly selects 200 personnel files and notes the number of sick days taken by each employee. The estimated sample mean is 12.2 days and the sample standard deviation is 10 days. How many personnel files would the director have to select in order to estimate μ to within 1 day with a 99% confidence interval?
A. 166
B. 200
C. 385
D. 550
E. 664
Using equation presented calculate SST,SSE and SSr. Determine least squares regression line for this data.
Managers at the Dew Drop Inn are concerned about the increasing number of guests who make reservations but fail to show up. They have decided to institute a policy of over-booking like larger hotel chains
I never understood the concept of the Empirical Rule and Chebyshev's Rule. Can you explain it to me and provide me with some sample problems please?
Your population mean has always been 9 customers per hour. The sample standard deviation is 4. The sample size is 25. The sample mean is 8.
If the mean number of serious accidents per year in a large factory (where the number of employees remains constant) is five, find the probability that in the current year there will be:
Explain how we test independent against dependent variables in a regression analysis? What's the set up? How are regression analyses often misused / misinterpreted at work or in the news? Cite an example.
What is the value of the standard error of the mean? What is the value of the confidence coefficient for a confidence interval with the indicated confidence level?
What is the standard error of estimate? Describe with an example. What are the coefficients of the linear regression model and what do they mean? What are the various statistics reported in a regression output?
It is known that the standard deviation of the population of checkout times is one minute. With a .95 probability, the sample mean will provide a margin of error of?
The task will take between 8 minutes & 11 minutes. Find this probability. The engineer would also like to know what the time is such that 40% of the task times are less than this value.
State the appropriate hypotheses. Obtain a test statistic and p-value. Interpret the results at ? = .01. Is normality assured? Is the difference large enough to be important?
What is the most probable number of patients arriving each Saturday and give the main assumptions we make when we model the above situation using a Poisson distribution.
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