est i ng the relationship between electricity costs and machine hours. Dat have been collected and a regression analysis prepared using Excel The monthly data and the regression output follow Machine Hours Electricity costs January February 2.900 $ 21,000 March 1,900 $ 13,500 April 3,100 $ 23,000 May 3,800 $ 28,250 June 3,300 $ 22.000 4,100 $ 24,750 August 3,500 $ 22.750 September 2,000 $ 15,500 October 3,700 $ 26,000 November 4,700 $ 31,000 December 4,200 $ 27.750 July Summary Output Regression Statistics Multiple R R Square Adjusted R2 Standard Error Observations 965 .932 .925 1,425.18 12.00 Intercept Machine Hours Coefficients 3,726.88 5.77 Standard Error 1,682.82 0.49 Stat 2.21 11.7 P-value 0.05 0.00 Lower 95% Upper 95% (22.69) 7,476.45 4. 676 .87 ercent of the total variance that can be explained by the regression is: 0.965 March 1.000 1100 DO May 4.100 July August September October November December 2.000 3.700 4,700 4,200 000 34.70 550 55,500 520.000 $31.000 $ 27,750 Summary Output Regression Statistics Multiple R R Square Adjusted R? Standard Error Observations 965 .932 .925 1,425.18 12.00 t I Standard Error 1,682.82 0.49 Coeficients 3,726.88 5.77 Stat P-value 2.21 L 10.05 117 0 .00 Lower 95% (22.69) 4.67 Intercept Machine Hours The percent of the total variance that can be explained by the regression is: O 0.965 O 0.982. 0 0.925 0.932