Shiny App for comparison of samples
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Updated
Apr 29, 2024 - R
Shiny App for comparison of samples
Case Studies and Projects in Machine Learning/EDA/DL
A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
🏠 This repository contains data analysis scripts for the 2022 American Community Survey (ACS) focusing on individuals aged 25 and over in North Carolina, based on 75,340 observations. This repository offers valuable insights into demographic and economic patterns across North Carolina's urban areas.
PlotTwist - a web app for plotting and annotating time-series data
Calculate confidence intervals for correlation coefficients
about statistical techniques for Data Science
Package provides python implementation of statistical inference engine
MSc Dissertation: Estimating Uncertainty in Machine Learning Models for Drug Discovery
A MATLAB package for multivariate permutation testing and effect size measurement
Utility functions for statistical population samples.
Moderation, mediation, and moderated mediation in structural equation modeling and multiple regression
Q11) Suppose we want to estimate the average weight of an adult male in Mexico. We draw a random sample of 2,000 men from a population of 3,000,000 men and weigh them. We find that the average person in our sample weighs 200 pounds, and the standard deviation of the sample is 30 pounds. Calculate 94%,98%,96% confidence interval?
More Meaningful Measurements: Effect Sizes and Confidence Intervals for Proportions using Python
An R package to compute a confidence interval for the sample variance. Don't assume normality.
python module, showcasing computation (as part of a learning process) of some common statistical methods including mininum sample size, confidence interval estimation methods for mean or proportion, hypothesis testing mehods and regression models witth metrics and test suites
A comprehensive module used to calculate the high bound, low bound, and center of a Wilson score interval.
Use bootstrap resampling to estimate the sampling distribution of a statistic
My notes on statistics
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