Workspace for applied problems of probability theory & mathematical statistics & modelling class

A general understanding of Statistics Basics, Different tests with Different Python Libraries

Digital Signal Processing projects 2019.1

Time series analysis on NYC births data.

This repository is for me to practice Time Series Forecasting.

Spatial Statistical analyses created using R and RStudio for an "Advanced Statistics for Urban Applications" at Temple University

Lattice Data with R (City of Cape Town)

Explore Python implementations of predictive modeling techniques like F-test, t-test, ANOVA, linear square estimation, autocorrelation, and least squares in this practical-driven GitHub repository

Standards Time Series and Regression Package, a library of Fortran subroutines for statistical data analysis developed by the Statistical Engineering Division of the National Institute of Standards and Technology

Evaluating white noise for class. This was from 1/15/2020

Extracts the dominant frequency sequence from an OGG audio file and exports it as a text file.

Walkthrough of the YIN algorithm for pitch detection from musical signals.

Metaheuristics for finding good Low Autocorrelation Binary Sequences.

C code to run individual-based model in Duthie and Falcy (2013)

This project builds a time series model that forecasts a 3-year industrial production of electic and gas utilities in US.

Code computing (Generalized) Kendall's tau and its jackknife variance under serial dependence.

The purpose of my application was to solve a problem many businesses (small businesses in particular) face. They do not know how much to produce, where to price, how much to spend on advertising and many other questions. Eden’s purpose was to answer these questions for them easily and with no technical acumen required by the user. Eden would mod…

A library to compute the 3D and angular correlation function using MPI

Gives analytic formulas to calculate autocovariance matrix and autocorrelation matrix for averaged Wiener process with equal-distance time points. Is supplemented with Python numpy code to verify those formulas with a Monte Carlo simulation.