The Linear Regression Model

Gauss Theory

Solving regression questions

Sigma two estimation

Mathematics

Regression analysis is an important tool for modelling and analysing data; it is essential to find the relationship between two or more variables. Regression helps to place the data points within a curve that helps in modelling and analysing the data. Regression allows to measure and characterise the variables on different scales for evaluation of predictive models and data setsThis course formalizes the most important model in econometrics, the linear regression model. The entire chapter is restricted to a special case, namely when you have only one explanatory variable. The key assumption of the linear regression model, exogeneity is introduced. Then, the OLS formula  is reinterpreted as an estimator of unknown parameters in the linear regression model. This course also introduces the variance of the OLS estimator under an important set of assumptions, the Gauss-Markov assumptions. The course also concludes with inference in the linear regression model, specifically discussing hypothesis testing and confidence intervals.The regression model is a linear condition that consolidates a particular arrangement of informatory values (x) the answer for which is the anticipated output for that set of information values (y). Both the information values (x) and the output are numeric.The linear equation allots one scale factor to each informational value or snt, called a coefficient and denoted by the capital Greek letter Beta (B). One extra coefficient is likewise added, giving the line an extra level of opportunity (for example going all over on a two-dimensional plot) and this is frequently called the capture or the inclination coefficient.

Students

Statisticians

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