What Are Error Intervals? GCSE Maths Revision
What are error Intervals and how do we find them - that's the mission in this episode of GCSE Maths minis! Error Intervals appear on both foundation and higher tier GCSE maths and IGCSE maths exam papers, so this is excellent revision for everyone! DOWNLOAD THE QUESTIONS HERE: https://d
From playlist Error Intervals & Bounds GCSE Maths Revision
Comparison of systematic and random error. Types of systematic error, including offset error and scale factor error/
From playlist Experimental Design
How to calculate margin of error and standard deviation
In this tutorial I show the relationship standard deviation and margin of error. I calculate margin of error and confidence intervals with different standard deviations. Playlist on Confidence Intervals http://www.youtube.com/course?list=EC36B51DB57E3A3E8E Like us on: http://www.facebook
From playlist Confidence Intervals
From playlist a. Numbers and Measurement
Statistics: Ch 7 Sample Variability (11 of 14) What is "The Standard Error of the Mean"?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 What is “the standard error of the mean”? It is the standard deviation (of the sampling distribution) of the sample means. Previous
From playlist STATISTICS CH 7 SAMPLE VARIABILILTY
How To Find The Z Score, Confidence Interval, and Margin of Error for a Population Mean
This statistics video tutorial explains how to find the z-score that will be used to find the confidence interval and margin of error for a population mean. This video contains 2 example problems in which you're asked to find a 90% and 95% confidence interval given the population standard
From playlist Statistics
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 12 - classifiers
Professor Sanjay Lall Electrical Engineering To follow along with the course schedule and syllabus, visit: http://ee104.stanford.edu To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/ 0:00 Introduction 0:11 Categorical outputs 12:07 Applic
From playlist Stanford EE104: Introduction to Machine Learning Full Course
DRAM Errors in the Wild: A Large-Scale Field Study
(October 21, 2009) Bianca Schroeder of the University of Toronto Computer Science Department gives an in depth discussion on how common dynamic random access memory errors are, their statistical properties, and how they are affected by external and chip-specific factors. Stanford Univer
From playlist Engineering
A Genome-Wide View of Transcription Fidelity
A Genome-Wide View of Transcription Fidelity. Filmed May 11, 2021 Abstract: Replication, transcription, and translation are the most fundamental molecular processes shared across the tree of life. While DNA mutation rates have been characterized for dozens of species, the rate at which RN
From playlist NCGAS: Genomics Research webinar series
Local Correctability of Expander Codes - Brett Hemenway
Brett Hemenway University of Pennsylvania April 14, 2014 An error-correcting code is called locally decodable if there exists a decoding algorithm that can recover any symbol of the message with high probability by reading only a small number of symbols of the corrupted codeword. There is
From playlist Mathematics
Statistical Rethinking 2022 Lecture 17 - Measurement Error
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: Music: https://www.youtube.com/watch?v=xXHH6bBAjDQ Palms: https://www.youtube.com/watch?v=We2KHqtqDos Pancake: https://www.youtube.com/watch?v=44ORuxym4fo Pause: https://www.youtube.com/watch?v=p
From playlist Statistical Rethinking 2022
Statistical Rethinking Fall 2017 - week10 lecture18 (fix)
Week 10, lecture 18 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapters 14. Slides are available here: https://speakerdeck.com/rmcelreath/statistical-rethinking-fall-2017-lecture-18 Additional informati
From playlist Statistical Rethinking Fall 2017
Statistical Rethinking 2023 - 17 - Measurement & Misclassification
Course: https://github.com/rmcelreath/stat_rethinking_2023 Music: https://www.youtube.com/watch?v=eTTcNPDAWYo Palm leaves: https://www.youtube.com/watch?v=We2KHqtqDos Outline 00:00 Introduction 10:00 Measurement error 15:55 Modeling measurement 26:00 Pause 26:52 Coding measurement 39:20
From playlist Statistical Rethinking 2023
Lecture 6/16 : Optimization: How to make the learning go faster
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 6A Overview of mini-batch gradient descent 6B A bag of tricks for mini-batch gradient descent 6C The momentum method 6D A separate, adaptive learning rate for each connection 6E rmsprop: Divide the gradient by a runni
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
StatQuest: Random Forests in R
Random Forests are an easy to understand and easy to use machine learning technique that is surprisingly powerful. Here I show you, step by step, how to use them in R. NOTE: There is an error at 13:26. I meant to call "as.dist()" instead of "dist()". The code that I used in this video ca
From playlist Statistics and Machine Learning in R
Overfitting 3: confidence interval for error
[http://bit.ly/overfit] The error on the test set is an approximation of the true future error. How close is it? We show how to compute a confidence interval [a,b] such that the error of our classifier in the future is between a and b (with high probability, and under the assumption that f
From playlist Overfitting
Stanford Seminar - Flash Reliability in Production: The Expected and the Unexpected
"Flash Reliability in Production: The Expected and the Unexpected" - Bianca Schroeder of University of Toronto About the talk: As solid state drives based on flash technology are becoming a staple for persistent data storage in data centers, it is important to understand their reliabilit
From playlist Engineering