In the earlier parts (part 1, part 2, and part 3) of this series, we have seen, how certain variables can change the outcomes of our regression model. We thoroughly explored such phenomena from basic (intercept dummies) to the two-way interactions. Likewise, three different variables (continuous or categorical) can also simultaneously impact our dependent or […]
In Part 1 and Part 2 of this series, we examined how an individual’s qualitative characteristics can affect the results of our regression model. First, we analysed how a categorical variable, such as [gender], changes the constant of our regression model (also known as intercept dummy). Subsequently, we examined the impact of multiple categorical and
In part 1 of this tutorial, we examined how a categorical variable can affect our regression model. However, most of the time, our model not only depends on more than one categorical variable, but also on a continuous variable; education, gender, and age can together play a significant role in predicting salary levels. This tutorial
In econometric models, some variables can play a very important role in the explanation of an economic phenomenon. For instance, gender can play a crucial role in determining salary levels. However, quantifying gender isn’t straightforward because gender lacks a numerical nature. This tutorial aims to explain methods for incorporating such information into econometric models, employing
When working with linear regression models, it’s essential to assess the model’s assumptions and diagnostic tests. These diagnostics help us in ensuring the reliability of our regression analysis. In simple terms, they ensure that the results are correct and unbiased. In this tutorial, we will only focus on three crucial diagnostic tests: normality of residuals,
