Question

fx A B с D E F G H 1 J K L M N 1 2 3 High-Low Calculation High Low Change 4 5 6 7 Variable Cost per unit 8 9 Total Fixed Costs 10 11 12 Cost Function 13 14 Regression Cost Function 15 16 17 18 Professional Hours 225 113 387 412 258 146 275 364 190 484 251 377 264 169 338 437 165 358 471 492 328 359 174 394 386 279 314 283 198 226 341 Support Costs 4,241 3,435 6,398 6,502 4,140 3,368 3,820 6,396 3,946 8,189 4,506 6,744 4,645 6,073 6,290 9,113 3,856 5,936 8,615 9,639 5,968 7,115 3,287 7,515 7,374 5,376 5,784 5,426 4,418 4,506 6,488 Cost Estimation 19 20 21 Projected Hours 3,000 22 High-Low 23 24 Regression 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 AA

Answer 1

HIGH-LOW Calculations

Particulars	Professional Hours	Support Cost
High	492	9639
Low	113	3287
Change	379	6352

Variable Cost=Highest Support cost-Lowest support cost/ Highest Professional hours-lowest professional hours.

=9639-3287/492-113

=6352/379

=16.76 is the Variable cost per unit

Next we can calculate Fixed cost,It is identify from the total cost,so the formula as follows

Total Cost=(VC×Units Produced)+Total Fixed Cost

we can apply this formula in our mentioned problem...

9639=(16.76x492)+Total Fixed cost

=9639=8246+Total Fixed cost

Total Fixed cost=9639-8246

Total Fixed Cost=1393

For the Lowest Activity

Fixed Cost is calculated using the formula given below

Fixed Cost = Lowest Activity Cost – (Variable Cost Per Units * Lowest Activity Units)

Fixed cost=3287-(16.76x113)

Fixed cost=3287-1894

Fixed cost=1393

Total cost=Variable cost+Fixed cost

so in High level;9639=(16.76x492)+1393, 8246+1393=9639

so in low level; 3287=(16.76x113)+1393, 1894+1393=3287.

Regression Cost Function

Regression tasks deal with continuous data. Cost functions available for Regression are,

• Mean Absolute Error
• Mean Squared Error
• Root Mean Squared Error
• Root Mean Squared Logarithmic Error

Mean Absolute Error

Mean Absolute Error(MAE) is the mean absolute difference between the actual values and the predicted values.

Mean Squared Error

Mean Squared Error(MSE) is the mean squared difference between the actual and predicted values. MSE penalizes high errors caused by outliers by squaring the errors. The optimization algorithms benefit from penalization as it is helpful to find the optimal values for parameters.

Root Mean Squared Error

Root Mean Squared Error (RMSE) is the root squared mean of the difference between actual and predicted values. RMSE can be used in situations where we want to penalize high errors but not as much as MSE does.

Root Mean Squared Logarithmic Error

Root Mean Squared Logarithmic Error (RMSLE) is very similar to RMSE but the log is applied before calculating the difference between actual and predicted values. The large errors and small errors are treated equally. RMSLE can be used in situations where the target is not normalized or scaled.

Cost Estimation

Projected hours is,3000, so the cost estimation is i.e Total cost=(Variable cost x Projected hours)+Fixed cost

High-Low (16.76x3000)+1393 = 51,673