The owner of an air conditioner business wants to investigate the relationship between the weekly number of air conditioners sold, temperature and the seasons of the year.
A random sample of 14 weeks is taken, with the average temperature of that week (in degrees Celsius) and the quarter from which that week belonged, noted.
There are three indicator variables, March, September and December.
Excel is used to generate the following multiple linear regression output. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted RSquare Standard Error Observations ANOVA Regression Residual Total Intercept Temperature March September Deomber 0.990.970.964.5414.00 df 4.009.0013.00 Coefficients −17.941.001.017.2227.87SS6999.27185.587184.86 Standard Error 8.540.354.605.586.55MS1749.8220.62 tStat −2.102.840.221.294.26F84.86 P-value 0.070.020.830.230.00 Significance 0.00 Lower 95% −37.270.20−9.39−5.4013.06 Upper 95%1.381.7911.4019.8442.68 Test the significance of the overall regression equation.
Understand how to calculate over- or under-applied overhead and its implications.
Distinguish between different types of product costs and their classifications.
Recognize the role of job order costing in service organizations and manufacturing.
Understand the significance of various ledgers and documents in cost accounting.
SUTA Tax Rate
The state unemployment tax rate that employers pay to fund unemployment benefits, which can vary based on the employer's industry and experience with layoffs.