Question

Tilbert Toys (TT) makes the popular Floppin's Freddy Frog and Jumpin' Jill Junebug dolls in batches. TT has recently adopted activity-based costing. TT incurs setup costs for each batch of dolls that it produces. TT uses "number of setup" as the cost driver for setup costs. TT has just hired Bebe Williams, an accountant. Bebe thinks that number of setup hours" might be a better cost driver because the setup time for each product is different. Bebe collects the following data. Month Number of setups Setup Number of setup hours 110 90 5500 4500 4400 5000 6000 5600 110 4000 3200 3400 2400 1. Use the linear regression method to estimate cost behavior. Excel has a function that you can use (I have posted these data in excel on Blackboard for your convenience). Print out the regression output and attach to this report. Data plotting is optional and may help assess the superiority of different models. 2. Interpret and evaluate your regression results (make sure you evaluate all necessary statistics). According to the criteria of determining appropriate cost driver, which cost driver do you recommend to use and why? Write out the correct cost formula. 3. Estimate total costs in a month when 100 batches of Floppin's Freddy Frog and 20 batches Jumpin's Jill Junebug are produced. To set up 1 batch of Floppin's Freddy Frog, it takes 1 setup hours. To set up 1 batch of Jumpin's Jill Junebug, it takes 0.5 setup hours. 4. You are working on the budget for November 2012 and expect 250 setup hours to be used in total. Will you use the cost formula to estimate set up cost in your budget? Why? 5. How does linear regression differ from the high-low method in estimating fixed and variable costs? Discuss the pros and cons of each.

Answer 1

Solution : of behavioris U using linear regression method, estimation of cost of follows: in which we can estimate the - Here there are two ways setup costs, first by taking noof setups as independent Variable and second by taking noof setups his as independent Variable. option (I): when numbers or setup are independent variable The scatter plot and regreffion output are of below! ² figure u and table (1) Conclusion: from tablel cue can see that Ivalue of the testis 0.2945 which is not significant to the linear model is not not significant and pvalue for number of setups is not significant in nature. option (2): when we use hoof setup hours

FIGURE 1:

scatter plot

Setup costs 7000 6000 5000 4000 Setup costs 3000 2000 1000 50 100 150

TABLE 1:

The regression output from Excel is

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.18844387
R Square	0.03551109
Adjusted R Square	-0.08505
Standard Error	1211.78973
Observations	10
ANOVA
	df	SS	MS	F
Regression	1	432525.1	432525.1	0.294548
Residual	8	11747475	1468434
Total	9	12180000
	Coefficients	Standard Error	t Stat	P-value
Intercept	3886.98882	1019.975	3.810868	0.005156
Number of setups	6.99878824	12.89569	0.542723	0.602108

FIGURE 2:

Setup costs 7000 6000 5000 4000 Setup costs 3000 2000 1000 30 50 70 90 110 130

TABLE 2:

The regression output is

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.984247
R Square	0.968742
Adjusted R Square	0.964834
Standard Error	218.1534
Observations	10
ANOVA
	df	SS	MS	F
Regression	1	11799273	11799273	247.9312
Residual	8	380727.3	47590.91
Total	9	12180000
	Coefficients	Standard Error	t Stat	P-value
Intercept	1185.455	215.4929	5.50113	0.000573
Number of setup hours	37.81818	2.40179	15.74583	2.64E-07

The statter plot and regression output are of above, figure (2) and table (2) Conclusion: Here I value is 247.93 and pvalue is in order of (t) so we can conclude that this independent variable is significantin nature. (2) Interpretation and evaluation of regression results: - Here we translate both relapse comes about as should beobrious that R2 esteem for the second relapse display i8 exceptionally solid. are in centrality .80 also, the Frefteem and t-esteem level. • Different translatims are done to a limited extenti. free factor. we prescribe number of setup hours as i setup cost = 1185.455+ 37.82* detop hours loo batches of FFF! (3) Total Cost in a month with

and ao batches of JIJ: - No of setup hours = Batches of flippins freddy frog (setup-time) + Batches of Jumping;ll Jone poto bug (fetup time) De = 100 (1) + 2010.5) 100 -llo hours Cliohrs) setup costs = 1185,455+37.82 (110) =($*8345166 then W for budget 2012 expected 250 Setuphors - financial plan for november 2012 anticipates that aso setop hours will be utilized as a part of aggregate .so, i will not use this model to predict the setup costs as this 250 setup Hours is an outlies for this model and if we predict it that would not be called a prediction, it would be called extra polation in

So which is not suitable in regressin outputs. (5) Difference of linear regression from high low method : The high low technique includes taking the most abnormal amount of movement and the last level of action and looking at the aggregate Expenses at each level. on the off chance that the variable cost is a settled charge for each unit and settled expenses continue os before, it is conceivable to decide the settled and variable expenses by illomunating the arrangement of conditions. pros and corns are: This is a systematic method which works on the difference on total cost used to predict the fixed and variable costs.

If the marginal rate of production is normally same, then we use this method. The high low method is not preferred because it can gield an incorrect understanding of the data if there are changes in variable or fixed costs. - o-