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 the realm of data analysis, encountering missing values is a common challenge that analysts face. Whether you’re working with cryptocurrency data, conducting rolling regressions, or exploring Gaussian random variables, addressing missing values is crucial for accurate analysis. Additionally, mastering interpolation techniques can enhance your ability to fill in missing data points effectively. Understanding Missing
A Comprehensive Guide to Handling Missing Values and Interpolation Read More »
In this blog, we are going to introduce you to one of the most famous models in the asset pricing model. Back then in 1993 two researchers (Fama and French) in finance created a model, which proved that three risk factors (market risk premium, size, and value) can statistically and significantly explain the fluctuations of
Fama and French three-factor model | Detailed Explanation Read More »
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
Heat plots, also known as heatmaps, are one of the best visualization tools in a data science. It allows you to quickly assess a dataset, whether you’re just looking for patterns in a set of variables, or need to perform more complex multivariate analysis. A heatmap uses color gradients to create a visual representation of
