Question

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 manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,700 450 5,700 550 6,100 600 6,600 700 7,100 750 7,700 Compute b1 and b0 (to 1 decimal). b1 b0 Complete the estimated regression equation (to 1 decimal). = + x What is the variable cost per unit produced (to 1 decimal)? $ Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1. r2 = What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? % 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)? $

Answer 1

Explanation:

The regression model is defined as,

where Y = Total COst and X = Production Volume

The least-square estimate of the intercept and slope are obtained in excel. The screenshot is shown below,

The estimated regression equation is,

coefficient of determination = 0.959

Explanation:

The coefficient of determination (r square) value is obtained in excel using the function =RSQ(). The screenshot is shown above,

95.9% of the variation in total cost can be explained by the production volume.

Explanation:

The r square value tells how well the regression model fits the data values. The r squared value is 0.959 which means 95.9% of the variation in total cost can be explained by the production volume.

Estimated total cost = $5747.

Explanation:

using the regression equation,

For X = 500,