Rupert Corporation is trying to predict its manufacturing overhead costs for the upcoming year. They are considering using the high-low method versus regression analysis. They have gathered information about their manufacturing overhead costs in each of the past six months. A table containing their cost data and the associated machine hours in each month (the cost driver) follows. Month Mfg. OH costs Machine Hours April $39,600 26,950 May $41,148 27,522 June $38,880 24,816 July $39,240 25,102 August $38,250 24,156 September $39,474 25,674 The company performed a regression analysis using the above data and had the following results. (Note: the results are excerpts so not all of the regression analysis results are presented.) SUMMARY OUTPUT Regression Statistics Multiple R 0.915617656 R Square 0.838355692 Adjusted R Square 0.797944615 Standard Error 436.4298155 Observations 6 Observations 6 ANOVA df Regression Residual Total SS MS F Significance F 1 3951452.0653951452.065 20.74569042 0.010380153 4 761883.9354 190470.9839 5 4713336 Intercept X Variable 1 Required: Coefficients Standard Error t Stat P-value Lower 95% 21809.05213 3873.2416575.6306975030.004894245 11055.20929 0.685628889 0.1505307284.5547437270.010380153 0.267688586 1. What is the cost equation if the high-low method is used to estimate costs? 2. Using the high-low method, predict total manufacturing overhead costs if Maxwell Corporation uses 25,000 hours. 3. What is the cost equation if regression analysis is used to estimate costs (use the results from the regression analysis provided)? 4. Using regression analysis, predict total manufacturing overhead costs if Maxwell Corporation uses 25,000 hours. 5. Which method is generally the better predictor of costs? Explain why