What is the error term?

In statistics, the error term is the sum of the deviations of each actual observation from the model regression line. Regression analysis is used to determine the degree of correlation between two variables, one independent and one dependent, resulting in a line that best suits the independent values ​​of dependent value in relation to an independent variable or variable. In other words, the error term is the term in the model regression equation, which increases and is responsible for the inexplicable difference between the actually observed values ​​of independent variable and the results predicted by the model. Therefore, an error term is a measure of how exactly the regression model reflects the real relationship between an independent and dependent variable or variable. The error term may indicate that the model can be improved, for example by adding another independent variable that explains some or all differences or randomness, which means scented and independent variables or variables are not correlated with a greater degree.

also known as a residual term or disturbing term, according to the mathematical Convention, the error term is the last term in the model regression equation and is represented by the Greek letter Epsilon (ε). Economists and financial industry experts regularly use regression models or at least their results to better understand and predict a wide range of relationships, such as how changes in the funds are related to inflation, how prices in the stock market are related to the level of unemployment or how changes in commodities affect specific companies in the economic sector. Therefore, the error term is an important variable to keep in mind and monitor that it measures the rate that any model does not reflect or meet the real relationship between the dependent and the independent variable.

In fact, there are two types of error terms commonly used in regression analysis: an absolute error and a relative error. AbroadThe error is the term error, as was previously defined, the difference between the actually observed values ​​of independent variables and the results of the predicted model. It is derived from this, the relative error is defined as an absolute error divided by the exact value of the predicted model. The relative error, which is expressed as a percentage, is known as a percentage error, which is useful because it states an error term into a greater perspective. For example, an error term 1, when a predicted value of 10, is much worse than an error impression 1 when the predicted value is 1 million when trying to accept a regression model that shows how well two or more variables are correlated.