Determine whether the High-Low method or Regression Analysis is better to predict monthly overhead costs. Explain which model is the better? Why or why not?

Use absolute cell address in y = a + bX formula (Use absolute August X Value) to predict January and December overhead costs. Do you think these predicted overhead costs are reasonable?

Observation Month Known X Known Y Overhead Cost Production Units $250,000.00 34000 $184,000.00 26000 $165,000.00 21000 $178,000.00 24000 $192,000.00 28000 $225,000.00 32000 $210,000.00 29000 $230,000.00 30000 $195,000.00 29000 $224,000.00 31000 $200,000.00 30000 12 $240,000.00 33000 Result Using Regression Using Lines 7.35 % 7.08 % 7.86 % 7.42 % 6.86 % 7.03 % 7.24 % 7.67 % 6.72 % 7.23 % 6.67 % 7.27 % High $250,000.00 $165,000.00 $34,000.00 $21,000.00 Low SUMMARY OUTPUT Regression Statistics Multiple R 0.934936081 R Square 0.874105475 Adjusted R Square 0.861516023 Standard Error 1396.53715 Observations 12 ANOVA df F Significance F 8.22744E-06 MS 135413506.6 1950316.01 Regression Residual Total 69.43157205 SS 135413506.6 19503160.1 154916666.7 10 Intercept X Variable 1 Coefficients 1247.961489 0.133182696 Standard Error 3344.93565 0.015983405 t Stat 0.373089835 8.332560954 P-value 0.716868393 8.22744E-06 Lower 95% -6205.019589 0.097569451 Upper 95% 8700.942567 0.168795942 Lower 95.0% -6205.019589 0.097569451 Upper 95.0% 8700.942567 0.168795942 Observation Month Known X Known Y Overhead Cost Production Units $250,000.00 34000 $184,000.00 26000 $165,000.00 21000 $178,000.00 24000 $192,000.00 28000 $225,000.00 32000 $210,000.00 29000 $230,000.00 30000 $195,000.00 29000 $224,000.00 31000 $200,000.00 30000 12 $240,000.00 33000 Result Using Regression Using Lines 7.35 % 7.08 % 7.86 % 7.42 % 6.86 % 7.03 % 7.24 % 7.67 % 6.72 % 7.23 % 6.67 % 7.27 % High $250,000.00 $165,000.00 $34,000.00 $21,000.00 Low SUMMARY OUTPUT Regression Statistics Multiple R 0.934936081 R Square 0.874105475 Adjusted R Square 0.861516023 Standard Error 1396.53715 Observations 12 ANOVA df F Significance F 8.22744E-06 MS 135413506.6 1950316.01 Regression Residual Total 69.43157205 SS 135413506.6 19503160.1 154916666.7 10 Intercept X Variable 1 Coefficients 1247.961489 0.133182696 Standard Error 3344.93565 0.015983405 t Stat 0.373089835 8.332560954 P-value 0.716868393 8.22744E-06 Lower 95% -6205.019589 0.097569451 Upper 95% 8700.942567 0.168795942 Lower 95.0% -6205.019589 0.097569451 Upper 95.0% 8700.942567 0.168795942