Flexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.

A Python tool to forecast Google Analytics data using several popular time series models.

It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.

Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.

Statistical concepts, from statistical inference to Bayes probability and different distribution types

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

This is a group project for MTH416A: Regression Analysis at IIT Kanpur

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

Time series analysis project: forecasting M3 competition series.

Forecast the Airlines Passengers and CocaCola Prices data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.

Final project progress will be posted here.

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

Compute a one-parameter Box-Cox transformation.

Time series analysis project: forecasting brazilian inflation.

Space-Time Statistical Quality Control of Extreme Precipitation Observation

Translate Measurements, Z-Scores and Centiles with the RIF format

Notes on statistical learning. Currently contains probability based models, parametric and non-parametric statistical tests.

total raw governmental industry employment data from January 1 1939 to October 30 2019. Time Series analysis to forecast employment from October 2019-October 2020.

ExcelR_Assignment---Forecasting---Assignment---18