Refer to the dialogue at Bikes Unlimited presented at the beginning of the chapter. Explain how some costs can behave in a nonlinear way.

Why is it important to stay within the relevant range when estimating costs? Review Note 5.27 'Business in Action 5.3' Which costs at Lowe’s are likely to be variable costs?.How does the contribution margin income statement differ from the traditional income statement?.Describe how regression analysis is used to estimate costs.How can the scattergraph method be used to identify unusual data points?.Describe the five steps of the scattergraph method and how these steps are used to estimate costs.Why might the high-low method lead to inaccurate results?.Describe the four steps of the high-low method and how these steps are used to estimate costs.Explain how account analysis is used to estimate costs.Review Note 5.11 'Business in Action 5.2' Why was the school district’s administration surprised to find out that cost savings from closing a school would be much lower than initially anticipated?.Why is it important to identify how costs behave with changes in activity?.How is the cost equation Y = f + vX used to estimate future costs?.Describe the variables in the cost equation Y = f + vX.What is a mixed cost? Provide two examples.What is a variable cost? Provide two examples.What is the difference between a committed fixed cost and a discretionary fixed cost? Provide examples of each.What is a fixed cost? Provide two examples.To further enhance your knowledge of regression analysis and to provide for a more thorough analysis of the data, you should pursue the topic in an introductory statistics course. The discussion of regression analysis in this chapter is meant to serve as an introduction to the topic. According to the output, 96.29 percent of the variance in total production costs is explained by the level of units produced-further evidence that the regression results will be useful in predicting total production costs. measures the percent of the variance in the dependent variable ( total production costs, in this example) explained by the independent variable ( units produced, in this example). R-squared Measures the percent of the variance in the dependent variable explained by the independent variable. Another way to assess the accuracy of the regression output is to review the R-squared statistic shown in cell B5. Thus the cost equation created from the regression analysis is likely to be useful in predicting total production costs. Line Fit Plot and R-Squared: The plot shows that actual total production costs are very close to predicted total production costs calculated using the cost equation.

Thus the cost equation is: Y = $43,276 + $53.42X or Total Production Costs = $43,276 + ($53.42 × Units Produced) Here, we discuss key items shown in the regression output provided in step 3.Ĭost Equation: The output shows that estimated fixed costs (shown as the Intercept coefficient in cell B17) total $43,276, and the estimated variable cost per unit (shown as the Units Produced coefficient in B18) is $53.42. Thus use one column (column A) to enter Total Production Costs data and another column (column B) to enter Units Produced data. The monthly data in Table 5.4 'Monthly Production Costs for Bikes Unlimited' includes Total Production Costs and Units Produced. Using a new Excel spreadsheet, enter the data points in two columns. Look for the Load the Analysis ToolPak option and follow the instructions given. If Data Analysis does not appear, go to the help button (denoted as a question mark in the upper right-hand corner of the screen) and type Analysis ToolPak. If Data Analysis appears, you are ready to perform regression analysis. Go to the Data tab on the top menu bar and look for Data Analysis. Confirm that the Data Analysis package is installed. We now describe the steps to be performed in Excel to get this equation. The resulting equation to estimate production costs is Y = $43,276 + $53.42X.

Excel data analysis regression slope