What exactly Fama-French three-factor model is? Before introducing multi-factor asset pricing models, the Capital Asset Pricing Model (CAPM) played a significant role in explaining the changes in stock returns. According to CAPM (this model has only one independent variable, which is market premium i.e. market return minus risk-free rate), the market premium can explain the […]
Data cleaning is the most fundamental aspect of data analysis, ensuring the reliable and accurate results. Duplicate observations can, however, pose a challenge in this accuracy of data analysis, leading to skewed results. The handling of duplicate observations in R is a straightforward task, where the accuracy and reliability of data analysis can be ensured.
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Standardization, normalization and mean centering of variable are common data processing techniques in Statistics and data analysis. It is important to standardize variables in statistics to compare and analyze different variables on the same scale. If you have two variables, one in inches and the other in centimeters, it’s not possible to compare these variables
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The purpose of quietly command is to suppress results, such as when you prefer not to display results or tables in Stata’s output window. To explain it, we will use the Stata sample data, which can be accessed by using the below command: sysuse auto.dta, clear The data contain the following variables: Quietly Suppose you
Quietly and Noisily | Suppress the output in Stata Read More »
It is a big obstacle to deal with missing values during data analysis. Missing values can occur for a number of reasons like skipped response, incomplete data entry, lost files, data malfunctions, etc. These missing values have consequences like biased analysis, model instability, incorrect inference, and loss of information, and they mostly reduce the statistical
