Ordinary least square (OLS) regression analysis carried out in this project. The selected dependent variables are some public health indicators like anxiety, diabetes. We tried to find the independent variables which are responsible for this health hazard.

time series analysis in R use cases

This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).

Used libraries and functions as follows:

Estimation of the Hubble constant using Gaussian process regression and viable alternatives

Usual linear regression or XGBoost? Combo! Or how I was investigating the impact of intellectual capital on NASDAQ-100 capitalization during 2 years.

This R package allows calibration parameter estimation for inexact computer models with heteroscedastic errors proposed by Sung, Barber, and Walker (2022) in SIAM/ASA Journal on Uncertainty Quantification.

A real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc

Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model

SmoothTrend is a comprehensive time series analysis tool that utilizes Holt-Winters, Holt, and Simple Exponential Smoothing methods, as well as ARIMA modeling, to perform advanced trend analysis, stationarity testing, residual analysis, and forecasting.

Recipes for common linear regression operations: model comparisons, heteroskedasticity, collinearity, goodness of fit

This instruction aims to reproduce the results in the paper “Calibration of inexact computer models with heteroscedastic errors” proposed by Sung, Barber, and Walker (2022).

Skript zur Videoreihe Regressionsdiagnostik in R

Evaluate the Buy Online Pick-up in Store (BOPS) strategy with a real-world dataset

Generalized Additive Forecasting Mortality

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t

tool to verify model test specifications and presence of heteroscedasticity in the forest inventory plot

This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.

Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Levera…