Final answer:
The statistical method used to identify cost behavior is regression analysis, which assesses the relationship between a dependent variable, like cost, and independent variables. A scatter plot, least-squares line, and correlation coefficient are used to analyze and predict relationships. The presence of outliers must also be considered in regression models.
Step-by-step explanation:
The statistical method for identifying cost behavior is regression analysis. This method is commonly used in various fields, including business and economics, to understand the relationship between two or more variables, with one being dependent and the others being independent. For instance, in a given scenario, the cost might be the dependent variable, while size could be the independent variable.
To analyze this relationship, one would typically :
- Draw a scatter plot using size as the independent variable and cost as the dependent variable.
- Inspect the scatter plot to ascertain if there appears to be a relationship between the variables.
- Calculate the least-squares line and put the equation in the form ลท = a + bx, where 'a' represents the y-intercept and 'b' represents the slope of the line. This line is also known as the line of best fit.
- Find the correlation coefficient, which measures the strength and direction of the linear relationship between the two variables. If this value is significant, it implies a strong relationship.
Once the regression equation is established, one can predict the cost for various sizes, such as the estimated total cost for a net taxable estate of $1,000,000 and $2,500,000, respectively.
Furthermore, in assessing the relevance of the regression model, one would look for outliers that could affect the regression analysis. An outlier is a data point that significantly deviates from the other data points and can be influential in the regression analysis. Deciding whether to remove an outlier involves considering its impact on the model and the rationale for its deviation.