Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry. It is usually done as products leave the factory, or in some cases even within the factory. Most often a producer supplies a consumer with several items and a decision to accept or reject the items is made by determining the number of defective items in a sample from the lot. The lot is accepted if the number of defects falls below where the acceptance number or otherwise the lot is rejected. In general, acceptance sampling is employed when one or several of the following hold: * testing is destructive; * the cost of 100% inspection is very high; and * 100% inspection takes too long. A wide variety of acceptance sampling plans is available. For example, multiple sampling plans use more than two samples to reach a conclusion. A shorter examination period and smaller sample sizes are features of this type of plan. Although the samples are taken at random, the sampling procedure is still reliable. (Wikipedia).

What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.

From playlist Sampling

What is convenience sampling? Advantages and disadvantages of grab sampling. How to analyze data from convenience sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creato

From playlist Sampling

What is purposive (deliberate) sampling? Types of purposive sampling, advantages and disadvantages. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sam

From playlist Sampling

Systematic Sampling (Introduction to Systematic Sampling & worked examples)

More resources available at www.misterwootube.com

From playlist Data Analysis

Sampling (4 of 5: Introductory Examples of Stratified Random Sampling)

More resources available at www.misterwootube.com

From playlist Data Analysis

SIMPLE Random Sampling Methods (12-3)

We want a representative sample. The best way to get a representative sample is to use a random sample. The best way to get a random sample is to use random sampling techniques. We can also use non-random sampling techniques. But…selecting a random sample does not guarantee it will be a re

From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)

Other Sample Types and Biased Samples

Voluntary response and convenience sampling, and biased samples

From playlist Unit 4: Sampling and Experimental Design

CONVENIENCE Samples: Most Popular Non-random Sample (12-5)

Convenience sampling (a.k.a., opportunity sampling) uses a population that is easy to study and convenient to the researcher, such as a Professor who surveys his Introduction to Psychology class. It can be extremely quick, easy, and cost-effective, but also lacks power, generalizability, a

From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)

Statistics: Introduction (12 of 13) Sampling: Definitions and Terms

Visit http://ilectureonline.com for more math and science lectures! We will review a sampling of definitions and terms of statistics: census, sampling frame, sampling plan, judgment sample, probability samples, random samples, systematic sample, stratified sample, and cluster sample. To

From playlist STATISTICS CH 1 INTRODUCTION

Accept-Reject Sampling : Data Science Concepts

How to sample from a distribution WITHOUT the CDF or even the full PDF! Inverse Transform Sampling Video: https://www.youtube.com/watch?v=9ixzzPQWuAY

From playlist Data Science Concepts

Coding MCMC : Data Science Code

Coding Accept-Reject, Metropolis, and talking about the tradeoffs! Accept-Reject Sampling Video : https://www.youtube.com/watch?v=OXDqjdVVePY MCMC Video : https://www.youtube.com/watch?v=yApmR-c_hKU Metropolis-Hastings Video : https://www.youtube.com/watch?v=yCv2N7wGDCw Link to Code :

From playlist Bayesian Statistics

Metropolis - Hastings : Data Science Concepts

The *most famous* MCMC method: Metropolis - Hastings. Made simple. Intro MCMC Video: https://www.youtube.com/watch?v=yApmR-c_hKU

From playlist Bayesian Statistics

Phiala Shanahan: "Machine learning for lattice field theory"

Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Machine learning for lattice field theory" Phiala Shanahan, Massachusetts Institute of Technology (MIT) Abstract: I will discuss opportuni

From playlist Machine Learning for Physics and the Physics of Learning 2019

Markov processes and applications-5 by Hugo Touchette

PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online

From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021

Markov Chain Monte Carlo (MCMC) : Data Science Concepts

Markov Chains + Monte Carlo = Really Awesome Sampling Method. Markov Chains Video : https://www.youtube.com/watch?v=prZMpThbU3E Monte Carlo Video : https://www.youtube.com/watch?v=EaR3C4e600k Markov Chain Stationary Distribution Video : https://www.youtube.com/watch?v=4sXiCxZDrTU

From playlist Bayesian Statistics

Lec 17 | MIT 3.320 Atomistic Computer Modeling of Materials

Monte Carlo Simulations: Application to Lattice Models, Sampling Errors, Metastability View the complete course at: http://ocw.mit.edu/3-320S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 3.320 Atomistic Computer Modeling of Materials

Andrea Sportiello: The challenge of linear-time Boltzmann sampling

Let Xn be an ensemble of combinatorial structures of size N, equipped with a measure. Consider the algorithmic problem of exactly sampling from this measure. When this ensemble has a ‘combinatorial specification, the celebrated Boltzmann sampling algorithm allows to solve this problem with

From playlist Services numériques pour les mathématiques

Jascha Sohl-Dickstein: "Generalizing Hamiltonian Monte Carlo with Neural Networks"

Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Generalizing Hamiltonian Monte Carlo with Neural Networks" Jascha Sohl-Dickstein, Google Abstract: We present a general-purpose method to

From playlist Machine Learning for Physics and the Physics of Learning 2019

Research Methods 1: Sampling Techniques

In this video, I discuss several types of sampling: random sampling, stratified random sampling, cluster sampling, systematic sampling, and convenience sampling. The figures presented are adopted/adapted from: https://www.pngkey.com/detail/u2y3q8q8e6o0u2t4_population-and-sample-graphic-de

From playlist Research Methods

Christian P. Robert: Bayesian computational methods

Abstract: This is a short introduction to the many directions of current research in Bayesian computational statistics, from accelerating MCMC algorithms, to using partly deterministic Markov processes like the bouncy particle and the zigzag samplers, to approximating the target or the pro

From playlist Probability and Statistics