Julia package containing utilities intended for Time Series analysis.

Forecast airline passenger demand using time series models like AR, ARMA, and LSTM to improve operations, optimize scheduling, enhance resource allocation, and streamline supply chain management through accurate demand predictions

Compute a one-parameter Box-Cox transformation of 1+x.

Demonstrativo Detalhado de uma análise de Regressão Linear, e Não Linear Múltipla em planos de saúde.

Compute the inverse of a one-parameter Box-Cox transformation.

Prediction of road casualties and evaluate the impact of transformations in Time Series Modeling and Forecasting with ARIMA using the R programming language

A MLR algorithm that analyzes diabetes data in African Americans to predict a diabetes diagnosis

Time series analysis and forecasting

📘This repository provides a detailed exploration of Walmart's BlackFridaySales data using the Central Limit Theorem (CLT) coupled with Confidence Interval Analysis. Leveraging statistical techniques, we delve into the nuances of customer behavior, purchase patterns during one of the busiest shopping events of the year.

The purpose of this project is to develop a model for the Sale Price of a home in Ames, Iowa based on the other variables in the data set

📔 This repository of Delhivery's logistical endeavors, emphasizing the utilization of data processing, feature extraction, and hypothesis testing methodologies, A meticulous comparison analysis of ACTUAL vs OSRM time-distance metrics, we unveil intricate patterns, providing invaluable insights and metrics for decision-making with precision.

Compute the inverse of a one-parameter Box-Cox transformation for 1+x.

Compute a one-parameter Box-Cox transformation.

This repository contains the descriptive statistics notebook.

Fortran functions for variable transformation

Trying to solve heteroscedasticity using transformations, and differents models.

Multivariate least squares regression model that predicts cancer mortality rates for US counties

Power Transformer works best on linear model and The Power Transformer actually automates this decision making by introducing a parameter called lambda. It decides on a generalized power transform by finding the best value of lambda

This repository contains a concise statistical exploration of the Cereal Ratings dataset (originally compiled by Consumer Reports and widely mirrored on Kaggle/UC Irvine). The project narrows the focus to seven nutritional predictors and evaluates how each contributes to a cereal’s overall rating using a multiple linear regression analysis in R.