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Installing and Registering IBM SPSS Software IBM SPSS version 27 Installation and Registration (PC/Windows) IBM SPSS version 27 Installation and Registration (Mac) Licensing Your IBM SPSS Software Students may obtain the SPSS license code from this link: https://alaureatena.sharepoint.com/sites/walden-university/student-documents/spss/Pages/default.aspx IBM SPSS Statistics Version 27 License Code Version 27 is recommended if you are using one of the following Operating Systems: · Windows 10 · Windows 8 · Windows 7 · OS Big Sur 11.0 · OS Catalina 10.15 · OS Mojave 10.14 · OS High Sierra 10.13 · OS Sierra 10.12 Copy the following code into the "Enter Codes" screen in the SPSS installation wizard - d3704aa09c38f7262d2f​ ** If you encounter an error when copying and pasting the SPSS license code, please try manually entering the code before contacting Customer Care for installation and licensing support. This code will be valid through March 31, 2022. At that time, you must re-visit this site for an updated license code. IBM SPSS Statistics Version 25 License Code If you currently have v.25 installed, and choose not to upgrade to v. 27, that is fine. You are required to update your license. Copy the following code into the "Enter Codes" screen in the SPSS installation wizard - de22c58c282ab7eb0fe5 This code will be valid through March 31, 2022. At that time, you must re-visit this site for an updated license code.​ Assignment: Regression Modeling Regression modeling is a foundational skill for those conducting secondary data analysis, much like you will encounter in the completion of the Doctor of Healthcare Administration (DHA) program. Additionally, regression modeling assists in finding connections among multiple variables and one dependent variable. Consider, for example, a healthcare administrator who may be asked to develop or interpret models of patient satisfaction based on other quantitative or dichotomous variables. For this Assignment, review the resources for this week. Then, review your course text, and complete Case Study 11.2 on page 536 The case study is based on a real-world problem. For Chapter 11, case study 11.1, you will need to download the file C11_01.xlsx The Assignment: (5 pages) · Complete Case Study 11.2, on page 536 of your course text using SPSS. CASE 11.1: HEATING OIL AT DUPREE FUELS COMPANY 6 Dupree Fuels Company is facing a difficult problem. Dupree sells heating oil to residential customers. Given the amount of competition in the industry, both from other home heating oil suppliers and from electric and natural gas utilities, the price of the oil supplied and the level of service are critical in determining a company’s success. Unlike electric and natural gas customers. Oli customers are exposed to the risk of running out of fuel. Home heating oil suppliers therefore have to guarantee that the customer’s oil tank will not be allowed to run dry. In fact, Dupree’s service pledge is, “50 free gallons on us if we let you run dry” Beyond the cost of the oil, however, DUPREE is concerned about the perceived reliability of his service if a customer is allowed to run out of oil. To estimate customer oil use, the home heating oil industry uses the concept of a degree-day, equal to the difference between the average daily temperature and 68 degrees Fahrenheit. So if the average temperature on a given day is 50, the degree-days for that day will be 18. (if the degree-day calculation results in a negative number, the degree-day number is recorded as 0.) By keeping track of the number of degree-days since the customer’s last oil, knowing the size of the customer’s oil tank, and estimating the customer’s oil consumption as a function of the number of degree-days, the oil supplier can estimate when the customer is getting low on fuel and then resupply the customer. DUPREE has used this scheme in the past but is disappointed with the results and the computational burdens it places on the company. First, the system requires that a consumption-per-degree-day figure be estimated for each customer to reflect that customer’s consumption habits, size of home, quality of home insulation, and family size. Because DUPREE has more than 1,500 customers, the computational burden of keeping track of all these customers is enormous. Second, the system is crude and unreliable. The consumption per degree-day for each customer is computed by dividing the oil consumption during the preceding year by the degree-days during the preceding year. Customers have tended to use less fuel than estimated during the warmer months. This means that Dupree is making more deliveries than necessary during colder months and customers are running out of oil during the warmer months. DUPREE wants to develop a consumption estimation model that is practical and more reliable. The following data are available in the file C11_01.xlsx: · The number of degree-days since the last oil fill and the consumption amounts for 40 customers. · The number of people residing in the homes of each of the 40 customers. DUPREE thinks that this might be important in predicting the oil consumption of customers using oil-fired water heaters because it provides an estimate of the hot water requirements of each customer. Each of the customers in this sample uses an oil-fired water heater. · An assessment, provided by Dupree sales staff, of the home type of each of these 40 customers. The home type classification, which is a number between 1 and 5, is a composite index of the home size, age, exposure to wind, level of insulation, and furnace type. A low index implies a lower oil consumption per degree-day. Dupree thinks that the use of such an index will allow them to estimate a consumption model based on a sample data set and then to apply the same model to predict the oil demand of each of his customers. Data Customer Oil Usage Degree Days Home Index Number People 1 381 888 3 3 2 171 176 5 7 3 644 1073 5 4 4 19 126 2 4 5 394 645 5 5 6 153 326 4 6 7 7 1229 1 3 8 319 1218 2 4 9 40 570 2 1 10 121 334 1 7 11 243 738 3 3 12 200 1464 1 5 13 402 880 4 5 14 118 1134 1 5 15 319 1019 3 4 16 185 460 2 3 17 209 257 5 4 18 467 779 5 4 19 50 128 2 4 20 153 371 2 5 21 94 178 3 6 22 574 933 5 3 23 191 295 3 5 24 679 1358 4 5 25 305 626 4 5 26 85 237 2 7 27 87 813 1 6 28 170 385 3 5 29 92 678 1 4 30 35 54 2 3 31 60 314 1 5 32 507 898 4 3 33 148 966 1 6 34 83 84 5 3 35 318 919 3 4 36 85 379 1 4 37 245 512 3 4 38 56 355 2 3 39 303 759 3 3 40 10 777 1 4 This is fictitious data. CASE STUDY 11.2: Developing A Flexible Budget at The GUNDERSON Plant. The Gunderson plant manufactures the industrial product line of FGT Industries. Plant management wants to be able to get a good, yet quick estimate of the manufacturing overhead costs that can be expected each month. The easiest and simplest method to accomplish this task is to develop a flexible budget formula for the manufacturing overhead costs. The plant’s accounting staff has suggested that simple linear regression be used to determine the behavior pattern of the overhead costs. The regression data can provide the basis for the flexible budget formula. Sufficient evidence is available to conclude that manufacturing overhead costs vary with direct labor hours. The actual direct labor hours and the corresponding manufacturing overhead costs for each month of the last three years have been used in the linear regression analysis. The three-year period contained various occurrences not uncommon to many businesses. During the first year, production was severely curtailed during two months due to wildcat strikes. In the second year, production was reduced in one month because of material shortages, and increased significantly (scheduled overtime) during two months to meet the units required for a one-time sales order. At the end of the second year, employee benefits were raised significantly as the result of a labor agreement. Production during the third year was not affected by any special circumstances. Various members of Gunderson’s accounting staff raised some issues regarding the historical data collected for the regression analysis. These issues were as follows. · Some members of the accounting staff believed that the use of data from all 36 months would provide a more accurate portrayal of the cost behavior. While they recognized that any of the monthly data could include efficiencies and inefficiencies, they believed these efficiencies and inefficiencies would tend to balance out over a longer period of time. · Other members of the accounting staff suggested that only those months that were considered normal should be used so that the regression would not be distorted. · Still other members felt that only the most recent 12 months should be used because they were the most current. · Some members questioned whether historical data should be used at all to form the basis for a flexible budget formula. The accounting department ran two regression analyses of the data-one using the data from all 36 months and the other using only the data from the last 12 months. The information derived from the two linear regressions is shown below (t-values shown in parentheses). The 36-month regression is OHt=123,810 + 1.60 DLHt + R2=0.32 (1.64) The 12-month regression is OHt=109,020 + 3.00 DLHt + R2=0.48 (3.01) Questions 1. Which of the two results (12 months versus 36 months) would you use as a basis for the flexible budget formular? 2. How would the four specific issues raised by the members of GUNDERSON’S accounting staff influence your willingness to use the results of the statistical analyses as the basis for the flexible budget formula? Explain your answer