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.

End-to-end marketing and business analysis projects utilizing machine learning and statistical analysis techniques using the R programming language.

about statistical techniques for Data Science

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

My Python learning experience 📚🖥📳📴💻🖱✏

Residual analysis in Linear regression is based on examination of graphical plots which are as follows :: 1. Residual plot against independent variable (x). 2. Residual plot against independent variable()y. 3. Standardize or studentized residual plot 4. Normal probability plot

Perform a STEP by STEP multiple mean comparison analysis on R

Projet pour une banque présente dans plusieurs pays, l'objectif est de cibler les prospects les plus susceptibles d'avoir, plus tard dans leur vie, de hauts revenus.

This problem concludes which factor is significantly effecting the CAT Score out of College type,program type,and interaction factor type for sample data. Here factorial Experiment design and Two Way Anova is used.

For this Project, I first applied an analysis of variance (ANOVA) model to the Pymaceutical dataset and then did a post-hoc analysis of the results by using Tukey Honest Significant Difference (HSD) to determine which drug treatments in the dataset significantly reduce tumor volume and metastasis. I then wrote a summary of my findings.

A simple ANOVA calculator with arbitrary number of alternatives and measurements.

A few statistical methods appropriate for applications in the biological and social sciences.

Tutorials for BSE classes.

MA4094*-應用統計

Project with Seokjun for Stat 204. On athletes, sex, & anger management.

Regression models for predicting customer acquisition costs (CAC) and the effectiveness of univariate and lasso feature selection techniques in improving the accuracy.

projet d'analyse exploratoire et de visualisation

Regression | Analysis | Modelling | Bayesian Search CV | Data Leakage | Overfit/Underfit | End-to-End Project