Data driven fault detection in chemical processes: Application to Tennessee Eastman Plant

A simple framework for ANOVA on various types of Julia statistical models

This repository is created for storing the components of Statistical Tests of One Pop, Two Pops and Three or more pops using Python.

Implementation of various feature selection methods using TensorFlow library.

Conduct one-way and multi-way anova in Julia with GLM.jl

Making use of R programming, the analysis is focussed on the problem which insurance providers are facing today to define their target market and plan their sale strategies which helps them increase their market share and thereby, maximize their profitability. The analysis techniques used in the project are learnt through Data Analysis and Decis…

Conduct one-way and multi-way anova in Julia with FixedEffectModels.jl

ASIS is a web application developed for the compilation of impact report PDF documents for the tutorial programme (A-STEP) at the University of the Free State.

An automated, user-interactive tutorial attendance impact report generator. The code conducts hypothesis testing to compare the student performance with tutorial attendance, and outputs a PDF report.

Examples of using the test statistics to test hypotheses

Performed graphical and numerical EDA, correlation and residual error analysis, log normalization

Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.

Implementation of some r programs using R studio.

Assessment of UK healthcare performance in 2011 compared to healthcare in other OECD Countries: University of Sheffield, MAS223 Practical 4 code and files

We present a detailed study of the asymptotic behavior of the distribution of the tails of these, perhaps, most commonly used statistical tests under non-standard conditions, that is, releasing the underlying assumptions of normality, independence and identical distribution and considering a more general case where one only assumes that the vect…

Wine dataset statistical analysis using Hypothesis testing (F-test, T-test, ANOVA, ANCOVA). Using machine learning to predict wine quality. Using regularization with 10 fold cross validation to overcome overfitting. Created different visualizations on the dataset.

A basic comparison of different statistical methods for data understanding and exploration.

OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation ,Statistical Significance of the Model (F test, T test) ,Multiple Regression , Qualitative Explanatory Variables (X) , heteroscedasticity and etc.

Conduct one-way and multi-way anova in Julia with MixedModels.jl

Create barplots or boxplots with significant level annotations.