Sensor for Home Assistant that gets reset at midnight
Go implementation of variance's method for one-pass variance computation with D. H. D. West improved methods which features merging of several multiple sets of statistics and adding weighted values.
A small library that does the statistics for your numbers.
Data analysis and filtering using time series for embedded devices (IoT). All in a single C++ library, Data Tome. Focus on the developer's experience and performance. It is the successor to the MovingAveragePlus library.
This example computes three core statistics from an array (a), namely the average (AV), standard deviation (SD), and coefficient of variation (CV). Results are derived step by step and returned in array (r). Implemented in Python, MATLAB, and JavaScript.
CNN autoencoder is trained on the MNIST numbers dataset for image reconstruction. Anomaly detection is carried out by calculating the Z-score. The framework used is Keras.
📊 This repository contains a Standard Deviation Calculator implemented in C++. It provides an efficient algorithm for calculating the statistical standard deviation of a dataset, making it a valuable tool for students, researchers, and analysts seeking a reliable method for data analysis.
Free WordPress Plugin: Given a discrete data set, the calculator calculates the mean, variance, and standard deviation of a sample or a population and shows all the intermediate steps of calculations. www.calculator.io/standard-deviation/
Series data statistics processing functions
Fast and simple statistics on the command line.
An online machine-learning library for creating robust statistical models of streaming data
Python package containing functions implemented for descriptive and inferential statistics.
In this project we will work with a real Candy Crush data set and use this data to estimate level difficulty.
Machine learning functions written in goLang:
A small plugin to provide sensible standard deviation displays.
I will include two ways of t tests that compare conversion rate and click through rate of two groups
This project uses statistical analysis to detect fraudulent credit card transactions by examining patterns and anomalies in a dataset of 10,000 transactions, calculating averages, medians, frequencies, and identifying outliers to distinguish between legitimate and fraudulent activities.
Protecting portfolios using Correlation Diversification
Calculate the standard deviation of a strided array using Welford's algorithm.
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