PR 8.2 (LO 1, 2, 3, 5) Data Financial Accounting Managerial Accounting Interpret Predictive Analyses All Care Hospital, which operates 2,000 hospitals in the United States, is preparing their operating budgets for 2026. Executive management would like to understand the factors driving total expenses. As a member of the data analytics team in the accounting department, you are asked to prepare and interpret data analyses to provide management with information about total hospital expenses. The data analytics team provided descriptive analyses summarizing the number of hospitals in each region and total hospital expenses by region. In 2025, total expenses for All Care Hospitals was $386.8 million. They also gave you the raw data file. An illustration depicts the interpretation of data analyses on total hospital expenses, in two tables that sit side-by-side. The table on the left has two columns, and the column headers are: Region, and Number of Hospitals. The data are presented as follows: Region, East North Central; Number of Hospitals, 262; Region, East South Central; Number of Hospitals, 136; Region, Mid Atlantic; Number of Hospitals, 168; Region, Mountain; Number of Hospitals, 160; Region, New England; Number of Hospitals, 100; Region, Pacific; Number of Hospitals, 259; Region, South Atlantic; Number of Hospitals, 252; Region, West North Central; Number of Hospitals, 214; Region, West South Central; Number of Hospitals, 449; Region, Grand Total; Number of Hospitals, 2,000. The table on the right titled, Total Expenses by Region-2025, has two columns, and the column headers are: Region, and no data. The data are presented as follows: Region, East North Central; $52,941,169; Region, East South Central; $19,037,967; Region, Mid Atlantic; $60,506,505; Region, Mountain; $26,438,517; Region, New England; $25,634,270; Region, Pacific; $71,210,066; Region, South Atlantic; $57,893,598; Region, West North Central; $26,015,206; Region, West South Central; $47,082,098. A scale below titled, Total Expense, ranges from 19 M to 71 M. According to management, the following variables are predictors of total hospital expenses: admissions, census, outpatient visits, births, payroll expenses, personnel, and whether there was a birth or not. The analytics team prepared a regression model using those variables with the following output. An excel sheet presents three tables under summary output. The first table titled, Regression Statistics, has two columns, and the data are as follows: Multiple R, 0.952051941; R Square, 0.964426014; Adjusted R Square, 0.964301005; Standard Error, 61360.99522; Observations, 2000. The second table below titled, A N O V A, has 6 columns, and the column headers are: no data, d f, S S, M S, F, and Significance F. The data are as follows: Regression: d f, 7; S S, 2.03 E+14; M S, 2.9 E+13; F, 7714.85352; Significance, 0; Residual: d f, 1992; S S, 7.5 E+12; M S, 3.77 E+09; F, no data; Significance, no data; Total: d f, 1999; S S, 2.11 E+14; M S, no data; F, no data; Significance, no data. The third table below has 5 columns, and the column headers are: no data, Coefficients, Standard Error, t Stat, P-value, Lower 95 percent, Upper 95 percent, Lower 95 percent, and Upper 95 percent. The data are as follows: Intercept: Coefficients, negative 4191.07786; Standard Error, 2275.171; t Stat, negative 1.84209; P-value, 0.06561005; Lower 95 percent, negative 8653.042868; Upper 95 percent, 270.8871; Lower 95 percent, negative 9653.04; Upper 95 percent, 270.8871; Admissions: Coefficients, 6.013726131; Standard Error, 0.387068; t Stat, 15.5366; P-value, 1.8108 E-51; Lower 95 percent, 5.254624717; Upper 95 percent, 6.772828; Lower 95 percent, 5.254625; Upper 95 percent, 6.772828; Census: Coefficients, negative 57.72122789; Standard Error, 19.55346; t Stat, negative 2.95197; P-value, 0.00319445; Lower 95 percent, negative 96.06861315; Upper 95 percent, negative 19.3738; Lower 95 percent, negative 96.0686; Upper 95 percent, negative 19.3738; Outpatient Visits: Coefficients, negative 0.019727175; Standard Error, 0.009985; t Stat, negative 1.97573; P-value, 0.04832264; Lower 95 percent, negative 0.039308781; Upper 95 percent, negative 0.00015; Lower 95 percent, negative 0.03931; Upper 95 percent, negative 0.00015; Births: Coefficients, negative 9.977260409; Standard Error, 1.656594; t Stat, negative 6.02276; P-value, 2.0367 E-09; Lower 95 percent, negative 13.22609855; Upper 95 percent, negative 6.72842; Lower 95 percent, negative 13.2261; Upper 95 percent, negative 6.72842; Payroll Expense: Coefficients, 1.904388653; Standard Error, 0.036122; t Stat, 49.95226; P-value, 0.00000000; Lower 95 percent, 1.733547274; Upper 95 percent, 1.87523; Lower 95 percent, 1.733547; Upper 95 percent, 1.87523; Personnel: Coefficients, 30.9359429; Standard Error, 2.697418; t Stat, 11.46872; P-value, 1.5657 E-29; Lower 95 percent, 25.64588566; Upper 95 percent, 36.226; Lower 95 percent, 25.64589; Upper 95 percent, 36.226; Births or Not: Coefficients, negative 5932.874462; Standard Error, 3329.682; t Stat, negative 1.78181; P-value, 0.07493183; Lower 95 percent, negative 12462.8985; Upper 95 percent, negative 597.1496; Lower 95 percent, negative 12462.9; Upper 95 percent, negative 597.1496; Identify the following: Who are the stakeholders ? What was the purpose for analysis? Are there alternative explanations or analyses? Are there any risks that should be considered? What knowledge did the preparer need and what knowledge does the interpreter need to understand the analysis? How can you use your prior experience or apply this experience to future analyses? Use the Summary Output for the following: Interpret the regression statistics. Is the model is significant? Why or why not? Identify the coefficients of the model. Do the variables used in the model make sense? Why or why not? Identify other variables that may improve the model. Prepare a regression model that predicts total expenses using the variables: admissions, outpatient visits, births, and personnel. Is this model better than the previous model? Interpret the regression statistics and the coefficients of the model