Dilbert Toys Manufacturing Company

Question: Discuss about the Dilbert Toysmanufacturing company. Answer: Introduction Dilbert Toys is a toy manufacturing company which makes the popular Flopping Freddy Frog and Jumpin Jill Junebug doll. Each doll is made in batches and thus incurs a set up cost for each batch of dolls they manufacture. The company currently uses number of set ups as the cost driver to determine the total set up costs. The company has recently hired Bec Williams as an accountant for the company. He has found that the time taken for set up for each product is different and hence recommended to use number of set up hours to determine the total set up costs. To find a better way to calculate total set up costs, he has collected data for the past 9 months and used regression analysis find the relation between the number of set ups and the total set up costs and the number of set up hours and the total set up costs. The regression output for the number of set ups and the total set up costs is given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.681718718 R Square 0.46474041 Adjusted R Square 0.388274754 Standard Error 51351.14094 Observations 9 ANOVA df SS MS F Significance F Regression 1 16026703955 16026703955 6.077766624 0.043128194 Residual 7 18458577734 2636939676 Total 8 34485281689 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 14256.33005 61323.4193 0.232477742 0.822817753 -130750.5144 159263.1745 -130750.5144 159263.1745 X Variable 1 421.4687192 170.9595388 2.465312683 0.043128194 17.21364777 825.7237907 17.21364777 825.7237907 The regression output for the number of set up hours and the total set up costs is given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.919609196 R Square 0.845681073 Adjusted R Square 0.823635512 Standard Error 27572.5839 Observations 9 ANOVA Df SS MS F Significance F Regression 1 29163550010 29163550010 38.3606056 0.000448041 Residual 7 5321731679 760247382.7 Total 8 34485281689 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7526.77784 26191.23859 0.287377697 0.782144324 -54405.66012 69459.2158 -54405.66012 69459.2158 X Variable 1 55.75526099 9.002085339 6.193593916 0.000448041 34.46871168 77.0418103 34.46871168 77.0418103 The graph plot for the number of set ups and the total set up costs The graph plot for the number of set up hours and the total set up costs The regression analysis of a data provides the value of equation of the regression line i.e. the co-efficient of dependent variable, co-efficient of constant term and the R square value of the regression analysis. The p value helps in checking the statistical significance of the regression analysis. The R square value is the percentage of the output data that can be predicted using the input data. The value of R square various between 0 and 1. If the input variables can predict the output with 100% accuracy the R square value is 1. If the input variables cannot predict the output at all then the R square value is 0. In the case of the regression analysis of the number of set ups and set up costs shows that the co-efficient of dependent variable is 421.46, co-efficient of constant term is 14256.33and the R square value is 0.464. Thus the equation of the line is Set up costs = 421.46* Number of set ups + 14256.33 In this case the number of set ups can estimate 46.4% of the total set up costs. The p value of the regression analysis shows that the co-efficient of dependent variable, co-efficient of constant term are statistically significant with confidence interval of 95% In the case of the regression analysis of the number of set up hours and set up costs shows that the co-efficient of dependent variable is 55.75, co-efficient of constant term is 7526.77 and the R square value is 0.845. Thus the equation of the line is Set up costs = 55.75* Number of set ups + 7526.77 In this case the number of set ups can estimate 84.5 % of the total set up costs. Thus the number of set up hours is a better estimator of the set up costs than the number of set ups. The p value of the regression analysis shows that the co-efficient of dependent variable, co-efficient of constant term are statistically significant with confidence interval of 95%. Conclusion After the regression analysis of both the number of set ups and set up costs and the number of set up hours and set up costs it has been found that the R square value of the first case was 0.464 and the second case was 0.845. Thus it can be concluded that the number of set up hours is a better estimator of the set up costs than the number of set ups. Hence Dilbert Toys should start using the number of set up hours as the cost driver to estimate the set up costs and calculate the cost of manufacturing of each toy. References Frost Jim. (2013). How to Interpret Regression Analysis Results: P-values and Coefficients. Kishore Aseem. (2010). Add a Linear Regression Trendline to an Excel Scatter Plot.