Sørensen–Dice coefficient

digital signal processing FIR filter implimentation in microcontroller

PyAbsorp is a python module that has the main focus to help estimate the Sound Absorption Coefficient using analytical models.

This example calculates the Pearson correlation coefficient (r), by measuring the linear relationship between two arrays. The use of means and then the accumulators, shows how covariance and variance combine into (r), with results from -1 to +1. Implemented in Python, MATLAB, and JavaScript.

Quadratic Equation Solver. We can help you solve an equation of the form "ax2 + bx + c = 0" Just enter the values of a, b and c below: a. x2 +. b. x +. c. = 0.

avsdbdg_3v3 is a Bandgap Reference circuit, which is used to generate a constant voltage reference in analog domain which is independent of temperature and supply voltage variations.

Sparkta: Interactive charts from Stata. One command produces a self-contained HTML file with charts, filters, and summary statistics — no R, no Python, no server. Supports multiple chart types including CI charts that match Stata output exactly. Opens in any browser, works offline.

Import og generate reports and analytics from a Microsoft Forms Excel export.

The Cramer, Determinant, Coefficient, Multiplication for the matrix.

Compute a moving sample Pearson product-moment correlation distance incrementally.

Typical subject topics are covered, in 167 notebooks. Streamlined Mathematica shortcuts take precedence over traditional precursor methods. A number of problems are dealt with in an inept, tentative, inefficient, or erroneous manner.

Return an integer corresponding to the significand of a single-precision floating-point number.

A utility library for comparing strings via the Dice Coefficient algorithm

Return an integer corresponding to the significand of a half-precision floating-point number.

Automate some statistics metods in Python.

Webapp that uses AngularDART framework to calculate Water quality in open-channel systems.

This is the last project in Level 1 of roadmap, and it was not classification this time, but Linear Regression. This project enabled me to understand the residuals, and check if there were any outliers by the graph. I even learnt the different types of encoding like one-hot, ordinal encoding. I even compared with other models like random forest

An item/document distribution suggestion based on user load calculation.

In this simple project I calculate coefficient clustering of a network data.

Executed Regression modelling, hypothesis testing and statistical analysis to predict factors affecting credit card balances in a firm. Tools & technologies: ANOVA, p-value, R square