Mult-Loci nonlinear/Linear Model based on new algorithm For GWAS , Epistasis and GS（ISR）

Value to Business :: Using this Regression model, the decision-makers will able to understand the properties of various products and stores which play an important and key role in optimizing the Marketing efforts and results in increased sales.

Data Analysis of the impact of socio-economic background on driving behavior among US teens.

Regressão Linear: Testando Relações e Prevendo Resultados

This case study uses exploratory data analysis (EDA) and regression to predict alcohol levels in wine by modeling several linear regression models with varying parameters.

A simple Multiple Regression model to predict the quality of air(AQI) using a dataset that contains Air pollutants' values of different cities of India.

Machine learning Basics. Just Started learning and uploading small chunk of codes and small projects .

sebuah aplikasi untuk memprediksi harga kost di kota Malang | tingkat akurasai : 79%

An automotive manufacturer's newest prototype has been suffering from production troubles. I offered to review the production data to identify insights that may help. Within that, several statistical analyses were performed in R, including a multiple linear regression analysis to determine which variables in the dataset can be used to most accur…

This explains the code for multiple regression model for a sample data saved as dummy2.xlsx. It explains the variability of model i.e. dependency of salary on Experience of Employee and their Gender. It also clarifies whether the average salary for the female employees are lesser than male employees or not.And if yes than by how much.

We know how to build a model with one X (feature variable) and Y (response variable). But what if we have three feature variables, or may be 10 or 100? Building a separate model for each of them, combining them, and then understanding them will be a very difficult and next to impossible task. By using multiple linear regression, we can build mod…

This is a course project done in 6th sem in course 'HS421: Econometric Data Science' under Prof. Sunil Paul at IIT Goa.

Statistical Analysis for Student Exam Scores Using SAS Studio

A classwork example in which R / R Studio is used to review production data for insights to help a manufacturing team

This report will attempt to predict the daily ozone level, our response, with a multiple regression Bayesian framework. A smoothing spline will be implemented to help make predictions and identify patterns in the data set.

The repository is about 50 Startups Prediction Project and Toyota Corolla Price prediction Project.

This project was undertaken as the culmination of our statistical learning course. Its primary objective was to utilize data from the 1990 U.S. Census to predict median house values, employing multiple regression models and advanced statistical analysis to attain precise predictions and gain valuable insights.

Supervised-ML---Multiple-Linear-Regression---Toyota-Cars. 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. Leverage value, Improving the Model, Model - Re-buil…

Predict Rental Prices from the street easy platform using Multiple Linear Regression with scikit-learm

In this repository, we delve into the world of regression analysis. I have provided straightforward examples and in-depth explanations of both simple and multiple regression, helping you gain a solid grasp of these essential statistical techniques.😎✅