Analysis of Cost Accounting

Question: Describe about theCost Accounting. Answer: Introduction In the current business environment, mangers maintain and use accounting information to make strategic business decisions. For making effective decisions in the business, it is critical for managers to analyze accounting information through using different tools and techniques (Drury, 2013). In this paper, regression analysis technique is used through excel tool to determine the relevant cost-driver for the Dilbert Toys (DT), which produces toys in batches. Below is the analysis for the given data of number of set-up, number of set-up hours and set-up cost. 1. Regression Equation and Regression Output (a) Set-up costs and Number of set-ups Regression analysis technique is applied to determine the relationship of two variables. In the given problem, manger needs to understand the relation of number of set-up and number of set-up hours with set-up costs of the business (Anderson, 2015). In order to determine the relations Regression equation y = 25277x (Please refer excel file) Regression Output Below table depicts regression output: Regression Statistics Multiple R 0.681719 R Square 0.46474 Adjusted R Square 0.388275 Standard Error 51351.14 Observations 9 (b) Set-up costs and Number of Set-up Hours Regression equation= y= 27395x (Please refer excel file) Regression Output Following is the table of regression output: Regression Statistics Multiple R 0.919609 R Square 0.845681 Adjusted R Square 0.823636 Standard Error 27572.58 Observations 9 2. Graphs and Regression Lines for the Data of Question A Graph and regression line for set-up costs and number of set-ups Graph and regression line for set-up costs and number of set-up hours 3. Evaluation of Regression Analysis for Business Decision-Making and Suggestions Regression analysis is a method to analyze the two variables of decisions mainly dependent and independent. It allows managers a way to make prediction and forecasting output accordingly. By determining relationship of variable, managers are able to interpret changes in dependent variables due to the changes within independent variable (Kinney and Raiborn, 2012). In the given problem, relationship of number of set-up and set-up hours with the set-up costs are needed to analyze for informing DT about the most relevant cost driver. Currently, this organization uses number of set-up as cost driver to forecast the set-up cost on monthly basis. The manager identifies a new cost driver namely number of set-up hours to forecast the set-up cost. By identifying the relationship of both cost drivers, it could be effective for manger of DT to determine more relevant cost driver. Through this, manager can make accurate estimation and forecast of potential set-up cost for business. Multiple R value of regression output means correlation coefficient that helps to determine the liner relationship between the variables. A value of 1 indicates that variables have perfect positive relationship, whereas value of 0 indicates absence of any relationship (Anderson, 2015). It can be interpreted from the correlation coefficient value for the number of set-up hours and number of set-ups that former one has more positive relationship with start-up cost. The multiple R value for both variables is 0.91 and 0.68 respectively. The value of 0.91 is more close to one than 0.68 and due to this it can be said that number of set-up hours may more relevant cost driver to estimate the start-up cost for the business in DT (Simmons and Hardy, 2011). On the other hand, the value of R squared also provides an effective floor to access the number of values of a given data set fall into the regression line. The more values falls into this category means that data set has more positive relationship. From the obtained regression results, it is identified that the values of R Squared for number of set-ups and number of set-up hours are 0.46 and 0.84. It can be interpreted from the regression output that approximately 84% data of set-up cost is explained by the number of start-up hours, whereas in case of number of start-up, the value is quite low. This can be stated in the obtained model 84% values of number of start-up values fit the model. It could be stated on the basis of regression output that number of start-up is less relevant cost driver to estimate the set-up cost (Davis and Davis, 2011). On the basis of these values, it would be difficult for managers to make accurate and more relevant estimation of cost. On the other hand, h ours of start-up would be more useful for estimating relevant cost and to make right decision in business in future. In addition to this, significance F value of ANOVA test from the regression output is also analyzed to make comparable analysis of relationship between the variables and to forecast the business cost. The value of significance should be less than 0.1 (10%) to demonstrate the presence of meaningful correlations. It is determined from the significance F value of the given problem that more meaningful correlations exists between the number of start-up hours and start-up cost than the number of start-up (Besley and Brigham, 2014). Significance F value for the regression of number of start-up is 0.0431, whereas it is 0.000 in case of the number of start-up hours. It indicates that the significance value of number of start-up hours is lower than the number of start-ups. On the basis of this, it can be stated that number of start-ups has less meaningful correlations with the start-up cost. Its use as cost driver within the DT can affect ability of manager to ensure accuracy in estimation of start-up cost and to take relevant decisions (Drury, 2013). Overall, it is determined from the analysis of above regression output that the number of start-up hours has more strong and positive relationship with the start-up cost than the number of start-up. On the basis of this, it can be suggested that DT should consider number of start-up hours as cost driver as it would be more significant to develop financial plan. The trend of start-up cost is more related to the number of start-up hours and due to this it may help this firm to make more proper estimation of potential costs and to bring accuracy in decision-making. Conclusion It can be concluded on the basis of above discussion that the suggestion of DTs accountant of using number of start-up hours as cost driver is quite useful as it would be more effective to estimate cost accurately. References Anderson, L. K. (2015) Accounting for Government Contracts: Cost Accounting Standards. USA: LexisNexis. Besley, S. and Brigham, E. (2014) Cfin4. USA: Cengage Learning. Davis, C.E. and Davis, E. (2011) Managerial accounting. USA: John Wiley Sons. Drury, C.M. (2013) Management and cost accounting. Germany: Springer. Kinney, M. and Raiborn, C. (2012) Cost accounting: Foundations and evolutions. USA: Cengage Learning. Simmons, A. and Hardy, R. (2011) Cambridge VCE Accounting Units 3 and 4. UK: Cambridge University Press.