ABC Industries wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here): Regression Statistics Multiple R .973273 R Square .94726 Adjusted R Square 941986 Standard Error 8053.744 Observations 12 Anova DF MS F 1 11650074512 179.6110363 Regression Residual Total SS 11650074512 648627988.2 12298702500 10 64862799 11 P-value COEFFICIENT STANDARD ERROR STAT 14538.05 11898.3624 1.221853 .249783701 Intercept X Variable 1 7.849766 585720166 13.4019 00010269 Using the cost equation from this regression analysis, and an "Xof 3,276, what would total predicted costs be? Hint: Before calculating your final answer, round fixed costs and variable costs to FOUR decimal places. (FOR EXAMPLE 3945.45689 ROUNDS TO 3945.4569) information (note: only a portion of the regression analysis results is presented here): Regression Statistics Multiple R .973273 R Square .94726 Adjusted R Square 941986 Standard Error 8053.744 Observations 12 Anova DF SS 1 11650074512 Regression Residual Total MS F 11650074512 179.6110363 64862799 648627988.2 10 11 12298702500 P-value Intercept X Variable 1 COEFFICIENT STANDARD ERROR I STAT 14538.05 11898.3624 1.221853 7.849766 .585720166 13.4019 .249783701 .00010269 Using the cost equation from this regression analysis, and an "X" of 3,276, what would total predicted costs be? Hint: Before calculating your final answer, round fixed costs and variable costs to FOUR decimal places. (FOR EXAMPLE 3945.45689 ROUNDS TO 3945.4569) Then, round your final answer to two decimal places, (FOR EXAMPLE 4569.1568 ROUNDS TO 4569.16)