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

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.

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.

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

Classical Linear Regression

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

Statistical Methods for Heart Disease & Indicators Using R Programming

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.

Diagnostic tools for regression modeling.

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

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.

Functions and data for Darlington's and Hayes' "Regression Analysis and Linear Models" book used in Utah State University's EDUC 7610 course

Applied multiple linear regression and regression diagnostics on NSSO consumption data and IPL performance data to analyze prediction accuracy, model assumptions, and the relationship between player performance and compensation.

Modelldiagnostik der linearen Regression mit R

An R Companion for the Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, and Li (2005).

NumXL SDK