regression-diagnostics

Diagnostics for HierArchical Regession Models

A statistical framework that serves as a common interface to a large range of models

An introductory statistics course for social scientists, using Stata

2SLS Regression with Diagnostics in R

Comprehensive Stata do-file for testing and correcting violations of classical OLS assumptions: model specification, heteroskedasticity, multicollinearity, spatial autocorrelation, endogeneity, residual normality, and outlier/influence detection.

An R package aimed at making specification curve analysis easy, fast, and pretty. It improves upon existing offerings with additional features and tidyverse integration. Users can easily visualize and evaluate how their models behave under different specifications with a high degree of customization.

Detailed implementation of various regression analysis models and concepts on real dataset.

Influence diagnostics for ridge regression based on the Kullback-Leibler divergence

Classical Linear Regression

Statistical Methods for Heart Disease & Indicators Using R Programming

Monte Carlo experiments testing OLS robustness to sample size, outliers, multicollinearity, and nonlinearity, with method comparisons and a real-data case study.

Análisis estadístico y econométrico de los factores que influyen en el precio de automóviles mediante regresión lineal y diagnósticos del modelo en R.

Diagnostic tools for regression modeling. Julia-equivalent for diagnoser (https://github.com/robertschnitman/diagnoser).

Diagnostic tools for regression modeling.

OLS model selection and diagnostics on the Boston housing data: best subsets under four criteria, LOOCV, VIF, assumption checks.

R script performing ANOVA (analysis of variance) with residuals-vs-fitted and normal-QQ assumption checks on Mariana Trench factors (car). Lemenkova (2019), An Empirical Study of R Applications for Data Analysis in Marine Geology, MASTEB 8(1):1-9.

Multivariate linear modeling of global life expectancy (WHO dataset) with variable selection, diagnostics and LASSO regularization in R.

This script analyses the relationship between the Human Development Index (HDI), population, and non-religious groups in various countries. Plots visualise relationships between HDI, population, and non-religious groups and using scatterplots and a linear regression model to predict.

This was a course project for the course " Multivariate Data Analysis ".

This repository contains a price prediction model for King County, Washington. The model results and analysis would prove invaluable to investors and stakeholders in the King County, WA housing market.