Statistical Modelling and Data Visualization of a Climate Change Dataset (January 1984 to December 2008 ) Sourced from Kaggle
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Updated
Jun 14, 2024
Statistical Modelling and Data Visualization of a Climate Change Dataset (January 1984 to December 2008 ) Sourced from Kaggle
A modern Fortran statistical library.
Analysis of proportions using Anscombe transform
Quantium has had a data partnership with a supermarket brand for the last few years that provide transactional and customer data. You are an analyst within the Quantium analytics team and are responsible for delivering highly valued data analytics and insights to help the business make strategic decisions.
Analyzing biological networks using statistical testing to uncover significant differences in protein distributions based on functional relationships.
Yeast TMT data - 3 different carbon sources (from Gygi lab) analyzed with PAW pipeline and MaxQuant
This repository contains all about the proposed solutions to the assignments that I was required to complete as part of the Quantium Data Analytics Virtual Experience Program. 📊📈📉👨💻
🐟 Statistical analysis of fish dimensions and weights implemented into linear regression (Ordinary Least Squares) predictive model
This repository will include Python | Jupyter-Notebook statistical testing | tests and analysis. Highly useful for in depth data analysis & model development.
This repository is a fork of a repository originally created by Lucas Descause. It is the codebase used for my Master's dissertation "Reinforcement Learning with Function Approximation in Continuing Tasks: Discounted Return or Average Reward?" which was also an extension of Luca's work.
MATLAB functions for Beta distribution test
Analysis of factors affecting learning performance based on a data set from Learning Management System software. Conducted with R language.
Significant Network Interval Mining
GUI app for doing sliding window Z-score transformations of quantitative proteomics data
Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases
Multivariate analysis (MVA) of high dimensional heterogeneous data
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