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.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
An important application of
| Production Volume (units) | Total Cost ($) |
| 400 | 4,200 |
| 450 | 5,200 |
| 550 | 5,600 |
| 600 | 6,100 |
| 700 | 6,600 |
| 750 | 7,200 |
- 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)?
%
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 1 images
