The expected value of the random error is E(e)=0ī.) The Error term, will have constant variance, Var(e) = constantĬ.) We need to assume that the average value of u doesn’t depend on x.
Our estimation starts with some assumptions about the error term.Ī.) The average value of u, the error term in the population is 0. One could estimate the parameters of this model using data of a random sample of working people. So, the corresponding regression equations becomeĮarnings= β1+β2Educ+e β2, β2 - Parameters of the model (constant) e - Error term/ Unobserved error (includes factors except for Education that affect Earnings) In this article, we find the relationship between earnings and education. X - Independent Variable/ Exploratory Variable/ Regressor Y - Dependent Variable / Explained Variable / Regressand Simple Linear Regression is useful for finding a relationship between two continuous variables. We can find the relationship between many real-life variables like income-expenditure, hours of study - score, height - weight…etc using a regression model. For a given value of a variable, these models are used to estimate/predict the corresponding value of another variable. Table of ContentsĮconomic theory suggests many relationships between economic variables using models. In this article let’s look into the econometrics behind the simple linear regression. This is called a simple linear regression.
The simplest situation is to check whether if a single action has any relationship to a response. Simple linear regression is the approach of forming a relationship between the dependent and independent variables. In this particular example, the family’s weekly income is the predictor variable ( independent variable X), and the amount spent on food would be the response variable (dependent variable Y). Sometimes when trying to solve the real-world problems using Machine Learning we may want to examine whether if certain factors have any correlation with a certain impact.įor instance, whether a family’s weekly income has a correlation with the amount of money spent on food in a certain place over a period of time. One of the ways to describe Machine Learning accurately is to figure out the mathematical optimization for real-world problems. Econometrics behind Simple Linear Regression
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