goodness-of-fit

wittawatj/kernel-gof

NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.

leifeld/btergm

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

ricardozacarias/ironhack-labs

The collection of exercises I did during Ironhack's Data Science bootcamp.

dazamora/IDFtool

The Rainfall Intensity Data Series can be Fitted to Eight Different PDFs and the Intensity-Duration-Frequency Curves are Computed

egarpor/goffda

Goodness-of-fit tests for functional data analysis. Software companion for "A goodness-of-fit test for the functional linear model with functional response"

oliviergimenez/R2ucare

R package to perform goodness-of-fit tests for capture-recapture models (and various manipulations)

TommasoBelluzzo/BenfordLaw

A framework for Benford's Law conformity assessment.

XiaoruiZhu/SurrogateRsq

R-squared measure for categorical data goodness-of-fit analysis using the surrogate R-squared

aportelli/LatAnalyze

a C++11 library for lattice QCD data analysis

stdlib-js/stats-kstest

One-sample Kolmogorov-Smirnov goodness-of-fit test.

Avinash793/regression-analysis-examples

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

Eden-Kramer-Lab/time_rescale

Tools for evaluating the goodness of fit of a point process model via the time rescaling theorem

egarpor/rp.flm.test

Software companion for "Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes"

stdlib-js/stats-chi2gof

Perform a chi-square goodness-of-fit test.

vaitybharati/P24.-Supervised-ML---Simple-Linear-Regression---Newspaper-data

Supervised-ML---Simple-Linear-Regression---Newspaper-data. EDA and Visualization, Correlation Analysis, Model Building, Model Testing, Model predictions.

AppliedStat/R

egarpor/rotasym

Tests for rotational symmetry on the hypersphere. Software companion for "On optimal tests for rotational symmetry against new classes of hyperspherical distributions"

KaidenFrizu/GachaPull

Arknights Headhunting Distribution Analysis.

lacerbi/gofit

Compute absolute goodness of fit via entropy estimation

PiotrTymoszuk/coxExtensions

Extended diagnostic and visualization tools for Cox proportional hazard models in R

UdeshikaDissa/Logistic-Regression_Analysis-of-Categorical-Data

Predicting the Likelihood of Diabetes Using Common Signs and Symptoms - About one-third of patients with diabetes do not know that they have diabetes according to the findings published by many diabetes institutes around the world. Detecting and treating diabetes patients at early stages is critical in order to keep them healthy and to ensure their quality of life is not compromised. Early detection will also help to mitigate the risk of serious complications like heart disease & stroke, blindness, limb amputations, and kidney failures as a result of diabetes. The data set consists of signs and symptoms of 516 newly diabetic or would be diabetic patients, who presented at Sylhet Diabetes Hospital in Sylhet, Bangladesh. The data had been collected using the direct questionnaires method at the hospital under the supervisor of Doctors. The Source for the data set is the UCI Machine Learning Repository at, https://archive.ics.uci.edu/ml/datasets/Early+stage+diabetes+risk+prediction+dataset. The data set has 16 descriptive features and one target feature. This study intends to build a logistic regression model to predict the likelihood of having diabetes using common signs and symptoms presented by patients. A successful model will enable early detection of diabetes through signs and symptoms shown by possible patients. This study consists of two phases: 1) Phase I - preprocess and explore the data set in order to make it ready to consume for model development. 2) Phase II - build a logistic regression model to predict the likelihood of having diabetes based on signs and symptoms. The Phase I part has already been completed under previous work/submission and this report intends to cover the work carried out for Phase II. All the activities have been performed in the R package and the report has been compiled using R-Markdown.

vaitybharati/P25.-Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data

Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data. EDA and data visualization, Correlation Analysis, Model Building, Model Testing, Model Prediction.

18Dominik/riskManagement

Computational statistics project in R on "A Simulative Comparison of Goodness-of-Fit Tests (GOFTs) from an Operational Risk Perspective with Focus on Loss Severity Distributions"

abhishek1377/Customer-LifetTime-Value-Prediction

CLV prediction using Regression Analysis of customer invoice data for an online retail store

Arjun-08/STATISTICS-UNIT.04

This analysis is part of our comprehensive statistics course project. This chapter encompasses critical topics such as goodness of fit., test for independence, and contingency tables with Yates correction. These concepts are essential for examining data relationships and validating statistical models.

cbsteh/Goodness-of-fit-model-indexes-for-Excel

Several goodness-of-fit (GoF) model indexes for Excel

hardhik-99/Thompsom_Sampling_GoF

Thompson Sampling equipped with Goodness of Fit test based active change-point detection in Non-Stationary Bandit environment

Kovenda/R-Shiny-App-to-predict-Factors-Affecting-Survival-of-CHD

R Shiny App to determine the factors that are most influential in patients’ survival of CHD. I created a Logistic Regression model in R using RStudio to predict the survival of CHD patients. Retrieved the data from the PHIS database using SQL & built tableau dashboards. The model predicted the survival of CHD with an AUC of over .90 and indicated that a higher EF % is a major factor.

mahajanaman/Human-Resource-Analytics-using-R

Companies face the problem that their human resources on whom the company have invested time and money to train them, leave the company voluntarily. It is important for the management to know the variables responsible for employees quitting jobs and also have a prediction that which employees will be quitting their jobs in future. The goal of this project is to design different models for predicting if an employee will stay or leave the company within the next year and analyze the accuracy of the models.

maximum-maximum/ContingencyTableAnalysis

Implementation of various models for contingency table analysis

MoinDalvs/Learn_hypothesis_testing_for_Data_science

Hypothesis for Data Science

Ohm-Rajpal/FinalProject

Stats HW chat bot that solves chi-square problems involving goodness of fit, homogeneity, or independence

sanjushasuresh/CHI-SQUARE-DISTRIBUTION

Tests based on Chi-Square distribution using R.

suuyawu/Score-based-Hypothesis-Testing-for-Unnormalized-Models

Score-based Hypothesis Testing for Unnormalized Models

IaraKrasnoff/Excel_Multiple_Linear_Regression_-M.L.R-_Iara_Krasnoff

This Excel Workbook repository explores the following skills: scatterplots, trend-line equation, linear regression, multiple regression equation, interpret the s standard error of estimate se and R2, and estimated fit of the equation.