Chapter 13, Problem 13.7AC

The business problem facing the director of broadcasting operations for a television station was the issue of standby hours and what factors were related to standby hours. The study included the following variables: Standby hours (Y) – Total number of standby hours in a week Weekly staff count (X1) – Weekly total of people-days Remote engineering hours (X2) – Total number of engineering hours worked by employees at locations away from the central plant Data were collected for 26 weeks. (See image attached) A)Interpret the meaning of the coefficients b1 and b2 in this problem B) What is the significance of b0? C) Predict the mean standby hours for a week in which the weekly staff count was 310 people-days and the remote-engineering hours total was 400

Managers in the Stamping Department have been studying overhead cost and the relationship with machine hours. Data from the most recent 12 months follow. Overhead (RM) Machine Hours 5,030 1,600 7,210 4,560 6,880 6,520 6,230 5,570 7,728 5,810 4,580 6,010 Month January February March 2,730 600 3,403 2,200 3,411 2,586 3,364 2,411 3,960 2,897 2,207 2,864 April Мay June July August September October November December The manager of the department has requested a regression analysis of these two variables (labeled no. 1 below). However, the staff person performing the analysis decided to run another regression that excluded February (labeled no. 2). She observed that the volume of activity was very low for that month because of two factors: a severe flu outbreak and an electrical fire that disrupted operations for about 10 working days. Regression No. 1 Constant Regression No. 2 428.00 Constant 550.00 R? 0.79 0.74 b coefficient 1.90 R? b coefficient 1.86 Required: i. Prepare an overhead cost…

The business problem facing the director of broadcasting operations for a television station was the issue of standby hours and what factors were related to standby hours. The study included the following variables:Standby hours (Y) –Total number of standby hours in a week weekly staff count (X1) –Weekly total of people-days. Remote engineering hours (X2) –Total number of engineering hours worked by employees at locations away from the central plant data were collected for 26 weeks. These data are organized and stored in. A)Interpret the meaning of the coefficients b1and b2 in this problem B)What is the significance of b0? C)Predict the mean standby hours for a week in which the weekly staff count was 310 people-days and the remote-engineering hours total was 400 Standby_hours_(Y) Total_Staff_Present(X1) Remote_Hours_(X2) 247 338 414 177 333 600 269 358 656 211 372 631 196 337 528 135 289 419 195 334 382 118 293 399 116 325 343 147 311 338 154 304 353 146 312…

The data below have been taken from the cost records of the Halifax General Hospital. The data relate to the costs of admitting patients at various levels of patient activity: Month                                                      Number of Patients                     Admitting                                                                                 Admitted                    Department CostsMay.....................................................................     3,600                                  $ 29,400June ....................................................................   3,800                                   $ 28,700July ......................................................................   2,900                                  $ 27,400August ................................................................   3,200                                   $ 28,000September ..........................................................   3,300…

The sales manager of a large automotive parts distributor wants to estimate the total annual sales for each of the company’s regions.  Three factors appear to be related to regional sales: the number of retail outlets in the region, the total personal income of the region, and the number of cars registered in the region. The following table shows the data for 10 regions that were gathered for last year sales.    The excel data file for this problem is:final exam question 6B spring 2021.xlsx     Region Annual sales  ($ million) Number of retail outlets Number of automobiles registered (million) Personal income ($ billion) 1 37.702 1,739 9.27 85.4 2 24.196 1,221 5.86 60.7 3 32.055 1,846 8.81 68.1 4 3.611 120 4.81 20.1 5 17.625 1,096 10.31 33.8 6 45.919 2,290 11.62 95.1 7 29.600 1,687 8.96 69.3 8 8.114 241 6.28 16.5 9 20.116 649 7.77 34.9 10 12.994 1,427 10.92…

What unit of analysis is reflected: "There is a relationship between unemployment rates and crime rates in a city such that cities with lower unemployment rates experience less crime."

The staff of controller of McCourt Company added the variable "pounds moved" to the ten-month data set: Month Materials Handling Cost Number of Moves Pounds Moved January $5,600 475 12,000 February   3,090 125 15,000 March   2,780 175   7,800 April   8,000 600 29,000 May   1,990 200      600 June   5,300 300 23,000 July   4,300 250 17,000 August   6,300 400 25,000 September   2,000 100   6,000 October   6,240 425 22,400 Required: 1. Using the data on material handling, use regression software such as Microsoft Excel to complete the missing data in the table below (round regression parameters to the nearest cent and other answers to three decimal places): McCourt CompanySUMMARY OUTPUT Regression Statistics Multiple R 0.999 R Square fill in the blank 491c8af8afe4f84_1 Adjusted R Square fill in the blank 491c8af8afe4f84_2 Standard Error 119.600 Observations 10   ANOVA   df SS MS F Significance F Regression 2 37,768,070.16 18,884,035…

Spring is a peak time for selling houses. Suppose the data below contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018. Selling Price Baths Sq Ft Selling Price 160,000 170,000 178,000 182,500 195,100 212,500 245,900 250,000 255,000 258,000 267,000 268,000 275,000 1.5 1,786 2 1,768 1 1,219 1 1,578 1.5 1,125 2 1,196 2 2,128 N W 3 2 1,280 1,596 3.5 2,374 2.5 2,439 2 1,470 2 1,688 Beds 3 3 3 2 3 2 3 3 3 4 3 4 4 295,000 325,000 325,000 328,400 331,000 344,500 365,000 385,000 395,000 399,000 430,000 430,000 454,000 Baths Sq Ft 2.5 1,860 3 2,056 3.5 2,776 2 1,408 1.5 1,972 2.5 1,736 2.5 1,990 2.5 3,640 2.5 1,928 2 2,108 2 2,462 2 2,615 3.5 3,700 Beds 3 4 4 4 3 3 4 4 4 3 4 4 4 Consider the estimated regression equation we developed that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house. (x, denotes number of bathrooms, x₂ denotes…

The Deinell-Forging-Corporation manufactures four highly confidential products that it supplies to ammunition firms that hold RSA government contracts. Each product must pass through the following departments before being shipped: forging, cleaning, weighing, and inspection. The time requirement in hours for each unit and its corresponding profit value are summarised in the following table: Department Product Forging Cleaning Weighing Inspection |хм122 Unit profit (Rand) 0.6 0.3 0.3 0.5 11000 XJ312 1.5 4 2. 14000 RM122 RM126 1.5 2. 1. 0.5 17000 1. 1.5 13000 Monthly available production in each department and the minimum monthly production requirement to fulfil these contracts are: Сараcity Departme (hours) Product Minimum production level Forging Cleaning Weighing Inspectior 10000 RM126 13000 XM122 130 15000 XJ312 80 24000 RM122 260 320 The production manager needs to specify production levels for each product for the next month. Formulate this problem to help him maximise the total…