Archives for Statistics - Page 3
Linear regression’s real-time applications include analysing pricing elasticity, predicting trends and sales estimates, for sports analysis and assessing insurance risks.
The post 6 Courses To Learn Linear Regression In 2021 appeared first on Analytics India Magazine.
“But to measure cause and effect, you must ensure that simple correlation, however tempting it may be, is not mistaken for a cause. In the 1990s, the stork population in Germany increased and the German at-home birth rates rose as well. Shall we credit storks for airlifting the babies?” Neil deGrasse Tyson, American astrophysicist. One…
The post Correlation & Causation: The Couple That Wasn’t appeared first on Analytics India Magazine.
Techniques in AI/ML have made great advances and it is a great exploratory step. But for now, it has not matured enough to reach an inferential step.
The post The Role Of Statistics In The Era Of Big Data? appeared first on Analytics India Magazine.
ANOVA is one of the statistical tools that helps determine whether two or more data samples o have significantly identical properties
The post A Complete Python Guide to ANOVA appeared first on Analytics India Magazine.
Data is everywhere. The present human lifestyle relies heavily on data. Machine learning is a huge domain that strives hard continuously to make great things out of the largely available data. With data in hand, a machine learning algorithm tries to find the pattern or the distribution of that data. Machine learning algorithms are usually…
The post Python Guide to Maximum Likelihood Estimation appeared first on Analytics India Magazine.
Probabilistic Graphical Models(PGM) are a very solid way of representing joint probability distributions on a set of random variables. It allows users to do inferences in a computationally efficient way. PGM makes use of independent conditions between the random variables to create a graph structure representing the relationships between different random variables. Further, we can…
The post Guide to pgmpy: Probabilistic Graphical Models with Python Code appeared first on Analytics India Magazine.
Probabilistic Graphical Models(PGM) are a very solid way of representing joint probability distributions on a set of random variables. It allows users to do inferences in a computationally efficient way. PGM makes use of independent conditions between the random variables to create a graph structure representing the relationships between different random variables. Further, we can…
The post Guide to pgmpy: Probabilistic Graphical Models with Python Code appeared first on Analytics India Magazine.
In this article, we will learn in detail about what Cohen’s kappa is and how it can be useful in machine learning problems.
The post Understanding Cohen’s Kappa Score With Hands-On Implementation appeared first on Analytics India Magazine.