Answer:
From the summary output, we have, coefficients for:
Intercept = $970.12
Machine hours = 36.138 or 36.14 (rounded to 2 decimals)
Hence, the cost equation is:
Interpreting Regression Output Rikki Bake, the controller for XYZ Incorporated, suspects that factory overhead costs are...
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Significance df SS MS F F Regression 102.1488 148.9539 Residual Total 12.0000 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept % MGT 0.4543 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 67.0000 67.8474 65.8779 69.8169 72.0000 70.1189 68.2003 72.0375 76.0000 71.9361 69.7884 74.0838 Dep.= % MGT...
Below is the data from a regression analysis performed by Earth Right Spa on its overhead costs and clients for the past year. Use this information to answer the following questions. SUMMARY OUTPUT Regression Statistics Multiple R 0.949 R Square 0.901 Adjusted R Square 0.891 Standard Error 1102.512 Observations 12.000 ANOVA df ss MS f Significance F Regression 1 111011767.37 111011767.37 91.33 0.00 Residual 10 12155332.63 1215533.26 Total 11 123167100.00 Ceofficients Standard Error tStar P-value Lower 95% Upper 95% Lower...
Figure 2 Regression Output SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.921261 0.848722 0.8055 0.711125 10 ANOVA Significance MS 0.001347 Regression Residual Total 19.86011 9.930053 19.63628 3.539894 0.505699 23.4 Standard Error Upper 95% Coefficients 0.20018 2.211198 0.07185 tStat P-value Lower 95% Intercept Size (cubic Metres) Weight (00's kg 2.19481 1.794453 0.676122 3.270412 0.013667 0.612423 3.809974 0.47295 0.329255 0.84353 -0.23731 0.819212 0.169626 0.42356 0.684594 (a)Based on the above regression output, interpret the regression coefficients...
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...
Consider the following excel regression analysis output. Explain the significance of the r, p, F value; Give the regression equation SUMMARY OUTPUT Regression Statistics MultipleR 0.875179 R Square 0.765938 Adjusted R Square 0.73668 3.802138 Standard Error Obserations 10 ANOVA MS egression Residual 8 115.65 14.45625 494.1 Total p-value ehzandard Error t Stat Lower 95% U e, 95% Lower950% Upper 9509e 75.4 2.08251736.2062 3.71058E-10 70.59770833 80.20229167 70.59770833 80 20229167 Interoept X Variable1 4.35 0.850184 -5.11654 0.000911066 6.310527365 -2.389472635 -6.310527365 -2.389472635
In relation to the below output from the Regression Analysis of the S&P/ASX200 Index (X) and from the company ABC Shares derived from weekly data over a 12 month period, can you please explain the key measures and what this all means eg. Number of Observations, R Square, Value of the Slope and the P-Value of the Slope etc. SUMMARY OUTPUT Regression Statistics Multiple R 0.045274332 R Square 0.002049765 Adjusted R Square -0.01790924 Standard Error 0.023996449 Observations 52 ANOVA df...
We are doing regression analysis for business analytics class and I am having a hard time reading this data. Please help. SUMMARY OUTPUT Regression Statistics Multiple R 0.999964 R Square 0.999928 Adjusted R Square 0.9999248 Standard Error 267.074107 Observations 48 ANOVA df SS MS F Significance F Regression 2 44576676715 2.23E+10 312474.2 6.1672E-94 Residual 45 3209786.045 71328.58 Total 47 44579886501 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -42159057 121894.4727 -345.865 1.04E-78 -42404564.6...
Dep.- WRK Indep.- MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted RSquare Standard Error Observations ANOVA Regression 102.1488 Residual Total 12.00001 Standard Coefficients P.valuell Lower 95 Upper 9524 LUV Upper 95 Intercept 6 MGT 0.4543 Predicted Predicted Lower Upper Lower XO Value Value 67.0000 65.8779 69.8169 72.0000 67.8474 70.1189 71.9361 68.2003 22.0375 74,0828 76.0000 69.7884 Dep.-% MGT Indep96 WRK SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Observations ANOVA Regression 460.8873 148.9539...
Hi I was wondering if i could have some help with some distribution questions. 1. show where zero and one fall on a normal distribution based on thedata. 2.is the coefficient sufficiently different than zero? explain 3. is the coefficient sufficiently different than one? explain. Regression Statistics Multiple R 0.806174983 0.649918103 R Square Adjusted R Square Standard Error Observations 0.636952107 13.57635621 29 ANOVA Significance F E SS MS df 9238.877183 9238.877 50.12481 1.30123E-07 Regression Residual 4976.571093 184.3174 27 14215.44828 Total...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations 253 ANOVA Significance 285.2516 MS 916816.787 3214.0637 Regression Residual Total 0.000 2750450.3598 800301.8665 3550752.226 252 Intercept Income Coefficients Standard Error 70.2382 15.8338 5.45850 .2485 t Stat P-value 4.4360 0.000014 21.96960 .000 Lower 3 9.053 4.969 "pper 95% 1.4234 479 HULLU LIIS TILIR. SUMMARY OUTPUT Regression Statistics Multiple R 0.8778 R Square Adjusted R Square 0.6558 Standard Error Observations ANOVA ANOVA Significance Regression 45.3528 de...