Complete A Three-Part Assessment In A Supplied Excel Templat

Complete A Three Part Assessment In A Supplied Excel Template In Which

Complete a three-part assessment in a supplied Excel template in which you apply regression analysis to decision making, determine unit costs, and analyze overhead using a predetermined rate.

Paper For Above instruction

Introduction

Cost estimation is a fundamental aspect of managerial accounting, enabling organizations to make informed decisions regarding resource allocation, pricing strategies, and operational improvements. Accurate cost estimation facilitates the comparison of alternatives by expressing costs in terms of variable and fixed components, along with their respective drivers. Regression analysis, a statistical tool, plays a crucial role in estimating cost functions and understanding the relationships between costs and activity levels. This paper explores the application of regression analysis in managerial decision-making, investigates unit cost determination through operations costing, and examines overhead analysis using predetermined rates. Each component demonstrates how fundamental accounting tools support strategic planning and operational efficiency.

Part 1: Regression Analysis for Delivery Cost Estimation

The first part involves interpreting regression results obtained from a delivery company's overhead cost analysis. The regression equation provided is: Monthly overhead = $26,501 + $10.70 per delivery. This model implies a fixed overhead cost of $26,501 and a variable cost of $10.70 per delivery. Data used for this estimate includes overhead costs and the number of deliveries over several months, providing a basis for regression analysis. The controller's estimate, on the other hand, assumes fixed costs of $9,900 and variable costs of $12 per delivery.

Interpreting the regression results involves examining the coefficients and assessing their validity. The intercept of $26,501 suggests a higher fixed overhead than the controller's estimate of $9,900. Conversely, the variable cost per delivery is estimated at $10.70, slightly lower than the controller's $12. This discrepancy may arise from differences in data sets or the presence of other overhead factors not captured by the fixed costs in the regression model. Supporting the regression model requires evaluating its fit, typically through R-squared and residual analysis, to determine whether it better explains overhead variations than the simple estimator used by the controller.

Supporting the regression model is appropriate when the statistical analysis indicates a good fit and the residuals are randomly dispersed, suggesting no systematic error. If the model's coefficients are statistically significant with p-values below acceptable thresholds, it reinforces its reliability. The higher fixed costs indicated by the regression could reflect additional overhead expenses not captured by the controller's simpler analysis, which might underestimate fixed costs or ignore fluctuations revealed through regression.

To determine the volume needed to achieve $11,000 operating profit before taxes, we first calculate the total cost using the regression equation. The total overhead per month is estimated as: $26,501 + $10.70 * number of deliveries. The revenue per delivery is $22, so the number of deliveries (Q) required to meet profit goals can be derived from the profit equation:

Profit = (Price per delivery Q) - (Total fixed overhead + Variable overhead per delivery Q) - Other costs (if any).

Assuming other costs are incorporated within the overhead, the equation simplifies to:

$11,000 = (22 Q) - (26,501 + 10.70 Q).

Simplifying yields:

11,000 + 26,501 = 22Q - 10.70Q, or

37,501 = 11.30Q, leading to Q ≈ 3,318 deliveries per month.

This volume indicates that the company must process approximately 3,318 deliveries monthly to attain an operating profit of $11,000 before taxes, based on the regression model. The management can use this estimate to plan operational capacities and monitor actual performance against forecasts.

Part 2: Unit Cost Calculation Using Operations Costing

The second part involves calculating the unit cost of two calculator models—Financial 5 and Scientific 6—using operations costing. Operations costing combines features of job costing and process costing, suitable when products share common processes but differ in parts or specifications.

For August, data indicates that Nevada Instruments produced 11,000 units of each model, with parts costs of $25 for the Financial 5 and $30 for the Scientific 6. Total direct labor costs amounted to $68,200, and additional indirect costs such as materials, overhead, and other expenses totaled $165,000.

The calculation begins by determining the total conversion costs (direct labor plus overhead), which are then allocated per unit. Since both models are assembled under similar processes, the conversion cost per unit can be estimated by dividing total conversion costs by the total units produced.

Assuming the total direct labor cost of $68,200 encompasses the assembly for all units, the conversion cost per unit for each model can be approximated by adding the allocated overhead per unit. If overhead is $17,550 and direct labor wages are $68,200, total conversion costs are $85,750. Dividing by the total units (11,000 + 11,000) yields an average conversion cost per unit of approximately $3.89.

Calculating the per-unit cost involves summing parts cost and allocated conversion costs:

- Financial 5: $25 (parts) + $3.89 (conversion) = $28.89.

- Scientific 6: $30 (parts) + $3.89 (conversion) = $33.89.

This estimation provides a basis for pricing, cost control, and profitability analysis, illustrating how operations costing aids in understanding product-specific costs in a manufacturing environment.

Part 3: Overhead Analysis with Predetermined Rate

The third component entails calculating and analyzing factory overhead using a predetermined overhead rate. The company uses a rate based on direct labor-hours, with an estimate of total overhead costs at different volumes, and applies a rate of $10 per direct labor-hour during the year.

From the estimated overhead costs for the year (fixed and variable), the total direct labor-hours are expected to be 180,000 hours. During September, Jobs 6023 and 6024, along with Job 6025, were completed. The actual direct labor-hours for these jobs sum to 10,500 + 9,000 + 6,000 = 25,500 hours.

Using the predetermined rate, the applied overhead for each job and total jobs is calculated as:

- Job 6023: $10 * 10,500 = $105,000.

- Job 6024: $10 * 9,000 = $90,000.

- Job 6025: $10 * 6,000 = $60,000.

The total overhead applied during September is $255,000. Actual overhead incurred, based on incurred costs, was $1,155,000 + $712,800 + other overheads, summing to approximately $1,867,800 (matching the original estimate). Comparing applied to actual overhead reveals whether overhead was overapplied or underapplied.

Given the overapplied overhead of $3,300, the company can adjust cost of goods sold and inventory accounts accordingly. The appropriate treatment involves closing the overapplied amount to the cost of goods sold, decreasing expenses and increasing net income. After adjustment, the new account balances reflect more accurate costing information, supporting better managerial decisions.

Conclusion

Accurate cost estimation through regression analysis, operations costing, and overhead analysis enables organizations to make strategic decisions that enhance profitability and operational efficiency. Regression models provide valuable insights into cost behaviors, especially when fixed and variable components need precise estimation. Operations costing facilitates detailed product cost determination, essential for pricing and product mix decisions. Overhead analysis using predetermined rates simplifies resource application and supports effective cost control. Together, these managerial accounting tools contribute significantly to sound decision-making processes in contemporary business environments.

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