The Cary Plant produces a part for agricultural equipment. The plant produces to demand rather than maintaining significant Inventories, so there can be significant fluctuation in monthly output. One of the significant cost Items is maintenance and repair (M&R) costs. M&R consists of both routine and unscheduled costs. The plant manager is trying to understand what effect production volume has on M&R costs and is getting conflicting Information from the machine operators (who believe that higher output is related to higher M&R costs) and the financial staff (who say that their analyses do not show this). Data on monthly output (in machine hours) and monthly M&R costs for the most recent two fiscal years follow: Month October November December January February March April May June July August September October November December January February March April May June July August September Regression Statistics Multiple R R Square Standard Error Machine-Hours 223,560 235,980 397,440 310,500 385,020 558,900 273,240 Observations Coefficients 360,180 285,660 298,080 322,920 186,300 Intercept Revenues 211,140 322,920 372,600 385,020 447,120 633,420 335,340 298,080 422,280 477,120 360,180 285,660 M&R Cost $56,518 59,320 24,150 34,230 36, 340 Required: b. Using Excel, estimate a linear regression with maintenance and repair (M&R) cost as the dependent variable and production (measured in machine hours) as the independent variable. 9,140 36,580 Note: Negative amounts should be Indicated by a minus sign. Round "Multiple R, R Square and Revenues to 5 decimal places, "Standard Error" to 1 decimal place, and "Intercept" to the nearest whole number. 43,380 53,230 52,298 30,250 55,570 48,070 29,780 42,670 38, 220 15,940 230 41,748 35,870 22,040 28,140 28,140 42,190