Statistical package in Python based on Pandas

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

:dragon: Compute and work with indices of effect size and standardized parameters

📦 BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.

Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.

Machine learning for beginner(Data Science enthusiast)

Repository containing Jupyter Notebooks for most of the blog posts on my blog. For example, there are data visualization, data manipulation, and data analysis code examples in the Notebooks

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Lesson files used in the Analysis of Factorial Designs for Psychologists.

🍲 Node.js API to control Anova water circulators.

A Python wrapper library for interacting with the Anova 2 over Bluetooth LE on Linux.

R Package for Aligned Rank Transform for Nonparametric Factorial ANOVAs

Python 3.6+ module to interface with the Anova 👨🏽‍🍳 Sous-vide cooker ⏱️ private API.

Reverse engineered iOS library for controlling the Anova 2 via Bluetooth

Easy grafs, ANOVAs and posthoc comparisons.

Pre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.

Basic statistical modelling examples.

A Python 3 library for programmatically accessing Anova WiFi-enabled sous vide cookers through the Anova API.

GAMLj: GLM, Mixed, Generalized and Generalized mixed models for jamovi

Simulation-Based Power Analysis for ANOVA Designs

R package for computing multiple hypothesis tests on rows/columns of a matrix or a data.frame

Going beyond the t-test in R and introducing linear regression and ANOVA (http://tinyurl.com/linear-models-r)

Collection of Matlab functions for the computation of measures of effect size

Algorithmic Trading project that examines the Fama-French 3-Factor Model and the Fama-French 5-Factor Model in predicting portfolio returns. The respective factors are used as features in a Machine Learning model and portfolio results are evaluated and compared.

Collect data from Anova Precision Oven

Fast regression and mediation analysis of vertex or voxel MRI data with TFCE

In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.

Anova Sous Vide Bluetooth API Python Wrapper

Functional ANOVA

Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.