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

Performed linear regression and residuals analysis on college tuition fees and admission rate in R.

In this notebook, I've loaded historical Dollar-Yen exchange rate futures data. I've applied time series analysis and modeling to determine whether there is any predictable behavior.

Prediction of Delivery Time of newspapers using Sorting Time

Linear Regression to identify the important physicochemical properties of the substrate that influence the aerial biomass production in the Cape Fear Estuary.

Linear regression is also a type of machine-learning algorithm more specifically a supervised machine-learning algorithm that learns from the labeled datasets and maps the data points to the most optimized linear functions.

Prediction-model-for-predicting-Price-of-Cars

simple short stata program to residualize some variable on some other variables

This data visualization package utilizes a unique and informal comparative data analysis and regression presentation.

Given features of car, training the model to predict the car price.

To increase efficiency of a cotton mill. I set up an ANOVA 3 factor analysis model in R to determine best spindle & position that produces the longest roving. The only significant difference in roving length was observed when position was 3 and spindle was 1 or 2. (ANOVA Model in R)

Business Case : The Waist Circumference - Adipose Tissue

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

Working through all the exercises for An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

Construção de um modelo de machine learning para prever com precisão a demanda de estoque com base nos dados históricos de venda do grupo Bimbo.

Prediction of mid-career salary, considering data from students of different universities.

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metric.

In this notebook we would be learning how to check that whether there is intercacion between two dependent variables or not. After that we would consider or add that interaction variable into our regression model and will monitor the changes in the parametrs.

Laboratory works on Numerical Methods course

Diagnostic tools for regression modeling.