Suppose the average number of returns processed by employees of a tax preparation service during tax season is 11 per day with a standard deviation of 4 per day. A random sample of 36 employees taken during tax season revealed the number of returns processed daily shown below. Use these data to answer parts a through c. 17 14 10 12 12 13 16 13 15 10 11 13 15 14 9. 15 11 16 14 D 9 10 8. 17 13 10 10 11 8. 3 4 16 8 7. a. What is the probability of having a sample mean equal to or smaller than the sample mean for this sample if the population mean is 11 processed returns daily with The probability is. standard deviation of 4 returns per day? (Round to four decimal places as needed.) b. What is the probability of having a sample mean larger than the sample mean for this sample if the population mean is 11 processed returns daily with a standard deviation of 4 returns per day? The probability is. (Round to four decimal places as needed.) c. Explain how it is possible to answer parts a and b when the population distribution of daily tax returns at the tax firm is not known. Choose the correct answer below. O A. The Central Limit Theorem can be applied because the sample size is sufficiently large. Thus, the distribution of the simple means will be either skewed to the left or skewed to the right. O B. The Central Limit Theorem can be applied because the sample size is sufficiently large. Thus, the distribution of the sample means will be approximately normal. OC. All data are normally distributed. Thus, the distribution of the sample means will be approximately normal. O D. The Central Limit Theorem can be applied because the sample size is not large. Thus, the distribution of the sample means will be approximately normal.
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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