An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturin volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,000
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturin volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,000
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d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)? Do not round intermediate calculations.
![**Understanding Regression Analysis in Accounting**
An important application of regression analysis in accounting is the estimation of cost. By collecting data on volume and cost and using the least squares method, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation:
| Production Volume (units) | Total Cost ($) |
|---------------------------|----------------|
| 400 | 4,000 |
| 450 | 5,000 |
| 550 | 5,400 |
| 600 | 5,900 |
| 700 | 6,400 |
| 750 | 7,000 |
**Tasks:**
**a. Use this data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations.**
- Compute \( b_1 \) and \( b_0 \) (to 1 decimal). Do not round intermediate calculations.
- \( b_1 \) [Input box]
- \( b_0 \) [Input box]
- Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations.
- \(\hat{y}\) = [Input box] + [Input box] \( x \)
**b. What is the variable cost per unit produced (to 2 decimals)? Do not round intermediate calculations.**
- [Input box]
**c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report \( r^2 \) between 0 and 1.**
- \( r^2 \) = [Input box]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffc8e2d68-695c-49ad-a9b1-8d66f1d1c9ea%2F2ed70923-2ac9-4ff3-8d02-3b00f37bd7a9%2Ft52o74u_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Understanding Regression Analysis in Accounting**
An important application of regression analysis in accounting is the estimation of cost. By collecting data on volume and cost and using the least squares method, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation:
| Production Volume (units) | Total Cost ($) |
|---------------------------|----------------|
| 400 | 4,000 |
| 450 | 5,000 |
| 550 | 5,400 |
| 600 | 5,900 |
| 700 | 6,400 |
| 750 | 7,000 |
**Tasks:**
**a. Use this data to develop an estimated regression equation that could be used to predict the total cost for a given production volume. Do not round intermediate calculations.**
- Compute \( b_1 \) and \( b_0 \) (to 1 decimal). Do not round intermediate calculations.
- \( b_1 \) [Input box]
- \( b_0 \) [Input box]
- Complete the estimated regression equation (to 1 decimal). Do not round intermediate calculations.
- \(\hat{y}\) = [Input box] + [Input box] \( x \)
**b. What is the variable cost per unit produced (to 2 decimals)? Do not round intermediate calculations.**
- [Input box]
**c. Compute the coefficient of determination (to 3 decimals). Do not round intermediate calculations. Note: report \( r^2 \) between 0 and 1.**
- \( r^2 \) = [Input box]
![### Educational Resource: Understanding Regression Analysis
#### Table of Data
- Production Volume (Units): 600, 700, 750
- Total Cost ($): 5,900, 6,400, 7,000
#### Regression Analysis Tasks
**a. Develop an Estimated Regression Equation:**
- **Compute \(b_1\) and \(b_0\):** Calculate both coefficients to 1 decimal place. Do not round intermediate calculations.
- \(b_1 = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\)
- \(b_0 = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\)
- **Complete the Estimated Regression Equation:** Format the equation as \( \hat{y} = b_0 + b_1 x \) with 1 decimal precision. Do not round intermediate calculations.
- \( \hat{y} = \_\_\_\_\_\_ + \_\_\_\_\_\_ x \)
**b. Determine the Variable Cost Per Unit Produced:**
- Calculate to 2 decimal places, with no rounding of intermediate steps.
- Variable Cost per Unit = \_\_\_\_\_\_\_\_
**c. Compute the Coefficient of Determination:**
- **Calculate \( r^2 \):** Express to 3 decimal places, with values between 0 and 1.
- \( r^2 = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \)
- **Evaluate the Variation in Total Cost:** What percentage is explained by production volume? Express to 1 decimal place without rounding intermediate steps.
- Percentage Explained = \_\_\_\_\_%
**d. Estimate Total Cost for Future Production Volume:**
- Given a production schedule of 500 units next month, calculate the estimated total cost to the nearest whole number without rounding intermediate calculations.
- Estimated Cost = $ \_\_\_\_\_\_
*This exercise helps in understanding how to build and interpret a regression model, assess the predictive power of an independent variable, and determine cost-effectiveness in production strategies.*](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffc8e2d68-695c-49ad-a9b1-8d66f1d1c9ea%2F2ed70923-2ac9-4ff3-8d02-3b00f37bd7a9%2F0xjs8c8_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Educational Resource: Understanding Regression Analysis
#### Table of Data
- Production Volume (Units): 600, 700, 750
- Total Cost ($): 5,900, 6,400, 7,000
#### Regression Analysis Tasks
**a. Develop an Estimated Regression Equation:**
- **Compute \(b_1\) and \(b_0\):** Calculate both coefficients to 1 decimal place. Do not round intermediate calculations.
- \(b_1 = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\)
- \(b_0 = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\)
- **Complete the Estimated Regression Equation:** Format the equation as \( \hat{y} = b_0 + b_1 x \) with 1 decimal precision. Do not round intermediate calculations.
- \( \hat{y} = \_\_\_\_\_\_ + \_\_\_\_\_\_ x \)
**b. Determine the Variable Cost Per Unit Produced:**
- Calculate to 2 decimal places, with no rounding of intermediate steps.
- Variable Cost per Unit = \_\_\_\_\_\_\_\_
**c. Compute the Coefficient of Determination:**
- **Calculate \( r^2 \):** Express to 3 decimal places, with values between 0 and 1.
- \( r^2 = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \)
- **Evaluate the Variation in Total Cost:** What percentage is explained by production volume? Express to 1 decimal place without rounding intermediate steps.
- Percentage Explained = \_\_\_\_\_%
**d. Estimate Total Cost for Future Production Volume:**
- Given a production schedule of 500 units next month, calculate the estimated total cost to the nearest whole number without rounding intermediate calculations.
- Estimated Cost = $ \_\_\_\_\_\_
*This exercise helps in understanding how to build and interpret a regression model, assess the predictive power of an independent variable, and determine cost-effectiveness in production strategies.*
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