Residual refers to the remaining or leftover value or amount after certain calculations or processes have been completed. It can be used in a variety of contexts, including finance, statistics, and engineering.

One example of residual is in the field of finance, specifically in the calculation of capital expenditures. Capital expenditures refer to the money a company spends on long-term assets, such as equipment or property, that are expected to have a useful life of more than one year. When calculating capital expenditures, a company may also include a residual value, which represents the estimated value of the asset at the end of its useful life. For example, if a company purchases a new piece of equipment for $100,000 and estimates that it will have a useful life of 10 years and a residual value of $10,000, the company would be able to depreciate the asset for $9,000 per year for the next 10 years ($100,000 – $10,000 = $90,000, $90,000 / 10 = $9,000).

Another example of residual is in the field of statistics, specifically in the calculation of a regression model. A regression model is a statistical tool used to predict the value of a dependent variable based on the value of one or more independent variables. In order to build a regression model, a statistician may use a variety of different techniques to identify the most important variables and to determine the strength of the relationship between those variables. One technique that may be used is residual analysis, which involves calculating the difference between the predicted value of the dependent variable and the actual value of the dependent variable for each data point. The residuals can then be plotted to visualize the overall fit of the model, with a good fit being represented by a patternless scatter of residuals around the zero line.

In conclusion, residual refers to the remaining or leftover value or amount after certain calculations or processes have been completed. It can be used in a variety of contexts, including finance, statistics, and engineering, and can be helpful in understanding the performance of a given asset or model.