In: Accounting
| The following is information pertaining to a local fitness club, Fitness for Life. | |||||||||
| Month | Club Membership (number of members) | Total Operating Costs | |||||||
| July | 450 | $ 8,900 | |||||||
| August | 480 | $ 9,800 | |||||||
| September | 500 | $ 10,100 | |||||||
| October | 550 | $ 10,150 | |||||||
| November | 560 | $ 10,500 | |||||||
| December | 525 | $ 10,200 | |||||||
| 1. By looking at the Total Operating Costs and the Average Operating Costs Per Member, can you tell whether the club's operating costs are variable, fixed, or mixed? | |||||||||
| 2. Perform Regression analysis using Microsoft Excel. What is the monthly operating cost equation? What is the R-square? | |||||||||
| 3. Use the high-low method to determine the club's monthly operating cost equation. | |||||||||
| 4. Can we predict total montly operating costs if the club has 3,000 members? | |||||||||
1.
The cost is mixed cost, since cost is increasing with increase in
output but not with direct proportion
2. Cost equation = $3912.35 + $11.80 x
R Square = 0.8007
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.894816 | |||||||
| R Square | 0.800696 | |||||||
| Adjusted R Square | 0.75087 | |||||||
| Standard Error | 278.0903 | |||||||
| Observations | 6 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 1 | 1242747 | 1242747 | 16.06982 | 0.016014 | |||
| Residual | 4 | 309336.8 | 77334.19 | |||||
| Total | 5 | 1552083 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 3912.354 | 1508.329 | 2.593834 | 0.060439 | -275.438 | 8100.146 | -275.438 | 8100.146 |
| Number of Members | 11.8029 | 2.944307 | 4.008718 | 0.016014 | 3.62819 | 19.9776 | 3.62819 | 19.9776 |
3. High low method
Variable cost per unit = ($10500-8900)/(560-450) = $14.55 per
unit
Fixed Cost = 10500 - 560 x 14.55 = $2352
Cost Equation = $2352 + 14.55 x
4. Cost for 3000 members
using high low equation = $2352 + 14.55 x 3000 = $46002
using regression = $3912.35 + 11.80 x 3000 = $39312.35
If you have any query regarding this, kindly comment with your query