Supply the answers to the incorrect answers. Please write the question # and the question part (ex. 1a, 1b, 2a, 2b, etc...).

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Given are five observations for two variables, {B and y. Use Table 2 of Appendix B. Round your answers to two decimal places. a. Using the following equation: Estimate the standard deviation of Q * when (I! = 4. b. Using the following expression: Q * ita/ZSar Develop a 95% condence interval for the expected value of y when a: = 4. to c. Using the following equation: Estimate the standard deviation of an individual value of y when m = 4. d. Using the following expression: 33 * itm/Zspred Develop a 95% prediction interval for y when a: = 4. to Consider the data set below. Use Table 2 of Appendix B. Excel File: data 1433.x|sx 113i 3 12 6 20 14 3h 55 40 55 10 15 a. Estimate the standard deviation of 33* when :1: = 8 (to 4 decimals). b. Develop a 95% confidence interval for the expected value ofy when a: = 8 (to 2 decimals). ( I ) c.Estimate the standard deviation of an individual value of y when a: = 8 (to 4 decimals). d. Develop a 95% prediction interval for y when a: = 8 (to 2 decimals). ( I ) \fGiven are five observations for two variables, (I) and y. Develop the 95% confidence and prediction intervals when x = 4. 3 0 (to 4 decimals) t-value 0 (to 3 decimals) 5&3. 0 (to 4 decimals) awed 0 (to 4 decimals) Confidence Interval for a: = 4 ( 0, 0) (to 2 decimals) Prediction Interval for a: = 4 ( 0, 0) (to 2 decimals) The two intervals are different because there is more variability associated with predicting @ value than there is w Many small restaurants in Portland, Oregon, and other cities across the United States do not take reservations. Owners say that with smaller capacity, noshows are costly, and they would rather have their staff focused on customer service rather than maintaining a reservation system (pressherald.com). However, it is important to be able to give reasonable estimates of waiting time when customers arrive and put their name on the waiting list. The file RestaurantLine contains 10 observations of number of people in line ahead of a customer (independent variable :0) and actual waiting time (dependent variable y). The estimated regression equation is: 1] = 0.30 + 8.3293 and MSE = 80.1952. Click on the datafile logo to reference the data. DAT/lir Use Table 2 of Appendix B. 3. Develop a point estimate for a customer who arrive with three people on the waitlist (to 1 decimal). 0 minutes b. Develop a 95% confidence interval for the mean waiting time for a customer who arrives with three customers already in line (to 2 decimals). ( 0 minutes, 0 minutes) c. Develop a 95% prediction interval for Roger and Sherry Davy's waiting time if there are three customers in line when they arrive (to 2 decimals). ( 0 minutes, 0 minutes) d. Discuss the differences in your answers to parts (b) and (c). The prediction interval is M than the condence interval. A sales manager collected the following data on annual sales for new customer accounts and the number of years of experience for a sample of 10 salespersons. Years of Annual Sales Salesperson Experience ($10005) 1 1 BO 2 3 96 3 5 98 4 7 98 5 9 104 6 11 122 7 12 132 8 13 150 9 14 152 10 14 170 The data on y = annual sales ($10008) for new customer accounts and 1: = number of years of experience for a sample of 10 salespersons provided the estimated regression equation 3] = 68.02 + 5.86m. For these data 5 = 8.9, 2 (m,- E)2 = 198.90, and s = 11.9053. 3. Develop the 95% confidence interval for the mean annual sales ($1000s) for all salespersons with eight years of experience. ($ 0, $ 0) (to 2 decimals) b. The company is considering hiring Tom Smart, a salesperson with eight years of experience. Develop a 95% prediction interval of annual sales ($1000s) for Tom Smart. ($ 0, $ 0) (to 2 decimals) c. Discuss the differences in your answers to parts (a) and (b). As expected, the prediction interval is much Q0 than the confidence interval. This is due to the fact that it is more K20 to predict annual sales for one new salesperson with 8 years of experience than it is to estimate the mean annual sales for all salespersons with 8 years of experience. Data given below are on the adjusted gross income a: and the amount of itemized deductions taken by taxpayers. Data were reported in thousands of dollars. With the estimated regression equation 3,7 = 4.68 + 0.1613, the point estimate of a reasonable level of total itemized deductions for a taxpayer with an adjusted gross income of $52,500 is $13,080. Click on the datafile to reference the data. Excel File: data 1437.x|sx Adjusted Gross Reasonable Amount of Itemized Income ($10005) Deductions ($10005) 22 9.6 27 9.6 32 10.1 48 11.1 65 13.5 85 17.7 120 25.5 Use the estimated regression coefficients rounded to 2 decimals in your calculations. a. Develop a 95% confidence interval for the mean amount of total itemized deductions for all taxpayers with an adjusted gross income of $52,500 (to 2 decimals). $ 0 thousand to $ 0 thousand b. Develop a 95% prediction interval estimate for the amount of total itemized deductions for a particular taxpayer with an adjusted gross income of $52,500 (to 2 decimals). $ 0 thousand to $ 9 thousand c. If the particular taxpayer referred to in part (b) claimed total itemized deductions of $20,400, would the IRS agent's request for an audit appear to be justified? d. Use your answer to part (b) to give the IRS agent a guideline as to the amount of total itemized deductions a taxpayer with an adjusted gross income of $52,500 should claim before an audit is recommended (to the nearest whole number). Any deductions exceeding the $ 0 upper limit could suggest an audit. \fAn important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 3800 450 4500 550 5200 650 6000 750 7000 800 7400 The data on the production volume :10 and total cost 1/ for particular manufacturing operation were used to develop the estimated regression equation 1; = 411.54 + 8.7322. a. The company's production schedule shows that 700 units must be produced next month. Predict the total cost for next month. 73* = 0 (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. 0 (to 2 decimals) s t-value o (to 3 decimals) spud 0 (to 2 decimals) Prediction Interval for an individual Value next month ( 0, 0) (to whole number) c. If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,000, should managers be concerned about incurring such a high total cost for the month? Discuss. Based on one month, $6,000 \\'9 outside the upper limit of the prediction interval. A sequence of five to seven months with consistently high costs should cause concern. Jensen Tire & Auto is in the process of deciding whether to purchase a maintenance contract for its new computer wheel alignment and balancing machine. Managers feel that maintenance expense should be related to usage, and they collected the following information on weekly usage (hours) and annual maintenance expense (in hundreds of dollars). Weekly Usage Annual (hours) Maintenance Expense 20 24 17 29 27 37 35 44 39 54 24 38 31 40 38 46 47 59 45 47 a. Develop the estimated regression equation that relates annual maintenance expense (in hundreds of dollars) to weekly usage hours (to 3 decimals). Expense = 0 + 0 Weekly Usage b. Test the significance of the relationship in part (a) at a 0.05 level of significance. Compute the value of the F test statistic (to 2 decimals). The meme o What is your conclusion? Conclude that there is a significant relationship between expense and weekly usage V] w c. Jensen expects the new machine to be used 30 hours per week. What is the expected annual maintenance expense in hundreds of dollars (to 2 decimals)? $ 0 hundred Develop a 95% prediction interval for the company's annual maintenance expense for this machine (to 2 decimals). ($ 0 hundred, $ 0 hundred) d. If the maintenance contract costs $3000 per year, would you recommend purchasing the contract for the new machine in part (c)? Why or why not? Yes, the expected maintenance expense is greater than $3000 v M The regional transit authority for a major metropolitan area wants to determine whether there is any relationship between the age of a bus and the annual maintenance cost. A sample of 10 buses resulted in the following data. Click on the datafile logo to reference the data. DATAr Age of Bus (years) Maintenance Cost ($) 1 350 370 480 520 590 550 750 800 U'l-b-bLJNNNN 790 5 950 3. Develop the least squares estimated regression equation (to 4 decimals, if necessary). 2 0+ oAge . Test to see whether the two variables are signicantly related with a: = 0.05. Reject null hypothesis v w c. Did the least squares line provide a good fit to the observed data? M d. Develop a 95% prediction interval for the maintenance cost for a specific bus that is 4 years old (to 1 decimal). $ 0to$ 0 3E: