For several years, many utilities have employed regression analysis to forecast monthly utility usage by residential customers using weather forecasts, the number of holidays, the number of days in the month, and other factors. For example, the Connecticut Department of Public Utility Control (CDPUC) has determined that regression, properly used, can accurately predict natural gas usage. Most public gas utilities serving Connecticut have reported levels of accuracy from 4% to 10% using regression. One company, Dominion Natural Gas Company of Ohio, uses this approach not to forecast, but to explain to customers why their natural gas bills have gone up or down compared to the prior month and to the same month of the prior year. The bill shows total MCF (thousand cubic feet of natural gas) used by the customer for that month and why the total MCF usage has changed, based on three factors: 1. Change in temperature. Each degree increase in temperature causes an increase in the number of MCFs consumed. The relationship between the change in temperature and the usage of MCF is not linear, but the monthly bill shows the average change in temperature for the month and the increase or decrease in MCF related to that change. 2. Number of billing days in the period. 3. The residual-the change in usage by the customer that is not attributable to temperature or the number of days in the billing period. A customer of Dominion has used 13.6 MCF in December and is charged $10.60 per MCF for a total bill that month of $144.16. The following data are available to compare the current month's weather and billing period to the prior month and to the same month last year: Required: 1. Determine the amount of difference in the customer's bill from the prior month and from the current month last year. (Negative amounts should be indicated with minus sign. Round your answers to 2 decimal places.) For several years, many utilities have employed regression analysis to forecast monthly utility usage by residential customers using weather forecasts, the number of holidays, the number of days in the month, and other factors. For example, the Connecticut Department of Public Utility Control (CDPUC) has determined that regression, properly used, can accurately predict natural gas usage. Most public gas utilities serving Connecticut have reported levels of accuracy from 4% to 10% using regression. One company, Dominion Natural Gas Company of Ohio, uses this approach not to forecast, but to explain to customers why their natural gas bills have gone up or down compared to the prior month and to the same month of the prior year. The bill shows total MCF (thousand cubic feet of natural gas) used by the customer for that month and why the total MCF usage has changed, based on three factors: 1. Change in temperature. Each degree increase in temperature causes an increase in the number of MCFs consumed. The relationship between the change in temperature and the usage of MCF is not linear, but the monthly bill shows the average change in temperature for the month and the increase or decrease in MCF related to that change. 2. Number of billing days in the period. 3. The residual-the change in usage by the customer that is not attributable to temperature or the number of days in the billing period. A customer of Dominion has used 13.6 MCF in December and is charged $10.60 per MCF for a total bill that month of $144.16. The following data are available to compare the current month's weather and billing period to the prior month and to the same month last year: Required: 1. Determine the amount of difference in the customer's bill from the prior month and from the current month last year. (Negative amounts should be indicated with minus sign. Round your answers to 2 decimal places.)