Excel for Statistics 8c--Independent-samples t-tests
This video explains how to conduct independent-samples t-tests in Excel.
From playlist RStats Videos
A02 Independence of the solution set
The independence of a linear system. How to make sure that a set of solutions are not constant multiples of each other.
From playlist A Second Course in Differential Equations
Jamovi: Independent samples T test
From playlist Jamovi
Levene’s Test of Homogeneity of Variance in SPSS (11-3)
One important assumption about the Independent-Samples t Test is that the variances in the sample groups are approximately equal. We assume that the samples have “homogeneity of variance.” Levene’s Test for Equality of Variances is a test of whether the variances of the two samples/groups
From playlist WK11 Independent Sample t Tests and Paired t Tests - Online Statistics for the Flipped Classroom
Lecture 9 - Approx/Estimation Error & ERM | Stanford CS229: Machine Learning (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptwgyN Anand Avati PhD Candidate and CS229 Head TA To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.h
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course
From playlist Machine Learning Course - CS 156
Chi-Squared Test of Independence with SPSS
This demonstration shows you how to conduct a Chi-Squared Test of Independence (Test of Association) with SPSS. This demonstration corresponds to Introduction to Statistics, Think & Do, by Scott Stevens (www.StevensStats.com).
From playlist SPSS Demonstrations
Lecture 02 - Is Learning Feasible?
Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes
From playlist Machine Learning Course - CS 156
Lecture 05 - Training Versus Testing
Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 5 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://
From playlist Machine Learning Course - CS 156
Overview of the F-Test. What it is and how it works with general steps and assumptions.
From playlist F Test
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 15 - Batch Reinforcement Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 11 - Fast Reinforcement Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
Lecture 06 - Theory of Generalization
Theory of Generalization - How an infinite model can learn from a finite sample. The most important theoretical result in machine learning. Lecture 6 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://i
From playlist Machine Learning Course - CS 156
07 Independent Samples t-Tests in SPSS – SPSS for Beginners
2021 NEW SERIES for SPSS 27: https://youtu.be/PN-H8GikRQ0 Another way of measuring the difference between two samples is to compare two unrelated groups or participants or samples. In this design, you measure two groups one time; in contrast, the previous paired test measured the same samp
From playlist Introduction to SPSS Statistics 27
A Combinatorial Proof of the Chernoff-Hoeffding Bound...- Valentine Kabanets
Valentine Kabanets Simon Fraser University; Institute for Advanced Study March 30, 2010 We give a simple combinatorial proof of the Chernoff-Hoeffding concentration bound for sums of independent Boolean random variables. Unlike the standard proofs, our proof does not rely on the method of
From playlist Mathematics
A Gentle Introduction to the Independent Samples t Test (11-2)
The independent samples t test compares one sample mean to another sample mean. It is widely used in statistics and will help us understand other statistical tests that we will learn later, such as ANOVA. The Independent Samples t Test, sometimes called “t for two,” is a parametric procedu
From playlist WK11 Independent Sample t Tests and Paired t Tests - Online Statistics for the Flipped Classroom
Gabriela Ciolek - Sharp Bernstein and Hoeffding type inequalities for regenerative Markov chains
The purpose of this talk is to present Bernstein and Hoeffding type functional inequalities for regenerative Markov chains. Furthermore, we generalize these results and show exponential bounds for suprema of empirical processes over a class of functions F which size is controlled by its un
From playlist Les probabilités de demain 2017
The VC Dimension - A measure of what it takes a model to learn. Relationship to the number of parameters and degrees of freedom. Lecture 7 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple
From playlist Machine Learning Course - CS 156
We discuss Hossenfelder's recent papers on Quantum Mechanics frameworks, see https://arxiv.org/search/?searchtype=author&query=Hossenfelder Here's my notes while reading the paper, links are found at the beginning of it https://gist.github.com/Nikolaj-K/37faa8a7a7afb5fa376ee09ebba0a545
From playlist Physics