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How to run R-style linear regressions in Python the easy way
The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...
Simply collecting data is not enough. You can fill spreadsheets with data, but it's useless if you can't act on it. Regression is one of the most powerful statistical tools for finding relationships ...
Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...
Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...
During the course of operation, businesses accumulate all kinds of data such as numbers related to sales performance and profit, and information about clients. Companies often seek out employees with ...
One key to efficient data analysis of big data is to do the computations where the data lives. In some cases, that means running R, Python, Java, or Scala programs in a database such as SQL Server or ...
In a recent write-up, [David Delony] explains how he built a Wolfram Mathematica-like engine with Python. Core to the system is SymPy for symbolic math support. [David] said being able to work ...
Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
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