Use the standard normal distribution or the t-distribution to construct a 95% confidence interval for the population mean. Justify your decision. If neither distribution can be used, explain why. Interpret the results. In a random sample of 14 mortgage institutions, the mean interest rate was 3.41% and the standard deviation was 0.48%. Assume the interest rates are normally distributed. Which distribution should be used to construct the confidence interval? O A. Use a t-distribution because it is a random sample, o is unknown, and the interest rates are normally distributed. O B. Use a t-distribution because the interest rates are normally distributed and o is known. OC. Use a normal distribution because the interest rates are normally distributed and o is known. O D. Use a normal distribution because n< 30 and the interest rates are normally distributed. O E. Cannot use the standard normal distribution or the t-distribution because o is unknown, n< 30, and the interest rates are not normally distributed. Select the correct choice below and, if necessary, fill in any answer boxes to complete your choice. O A. The 95% confidence interval is (.). (Round to two decimal places as needed.) O B. Neither distribution can be used to construct the confidence interval. Interpret the results. Choose the correct answer below. O A. It can be said that 95% of institutions have an interest rate between the bounds of the confidence interval. O B. With 95% confidence, it can be said that the population mean interest rate is between the bounds of the confidence interval. OC. Ifa large sample of institutions are taken approximately 95% of them will have an interest rate between the bounds of the confidence interval. O D. Neither distribution can be used to construct the confidence interval.
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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