Analysis of variance

Variance (4 of 4: Proof of two formulas)

More resources available at www.misterwootube.com

From playlist Random Variables

More Standard Deviation and Variance

Further explanations and examples of standard deviation and variance

From playlist Unit 1: Descriptive Statistics

Derivations.2.Derivation of Variance

This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources

From playlist Optional - Derivations

How to find the number of standard deviations that it takes to represent all the data

👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard deviation of a set of data is a measure of spread/variation

From playlist Variance and Standard Deviation

Learning how to find the variance and standard deviation from a set of data

👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard deviation of a set of data is a measure of spread/variation

From playlist Variance and Standard Deviation

Derivation.3.Variance as an Expectation

This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources

From playlist Optional - Derivations

How to find the variance and standard deviation from a set of data

👉 Learn how to find the variance and standard deviation of a set of data. The variance of a set of data is a measure of spread/variation which measures how far a set of numbers is spread out from their average value. The standard deviation of a set of data is a measure of spread/variation

From playlist Variance and Standard Deviation

Extra Math lecture 2: The derivation of the variance formula

Forelæsning med Per B. Brockhoff. Kapitler:

From playlist DTU: Introduction to Statistics | CosmoLearning.org

QAICourse6 5ANOVA

From playlist Applied Data Analysis and Statistical Inference

Lecture 08 - Bias-Variance Tradeoff

Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities. The learning curves. Lecture 8 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/ma

From playlist Machine Learning Course - CS 156

StatGeoChem session 5 Factor Analysis

PCA and Factor analysis with applications in Geosciences

From playlist Statistical Geochemistry

08b Machine Learning: Principal Component Analysis

Lecture of principal component analysis for dimensionality reduction and general inference, learning about the structures in our subsurface data. Follow along with the demonstration workflow in Python's scikit-learn package: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/

From playlist Machine Learning

Deep Learning Lecture 6.3 - PCA part 2

Principal Component Analysis - PCA Algorithm - Properties of PCA - Equivalence between maximum projection variance and minimal reconstruction error - Applications to images

From playlist Deep Learning Lecture

Structural Equation Modeling of Latent Growth Curves with AMOS

This video demonstrates Latent Growth Curve Modeling with AMOS. Useful links: Video 1: https://www.youtube.com/watch?v=ATdrC8KNp3I Video 2: https://www.youtube.com/watch?v=8UJkO8o7jZs Paper: https://www.tandfonline.com/doi/full/10.1080/01443410.2014.950946 To support the channel, I would

From playlist Structural Equation Modeling

Latent Growth Curve Modeling | Part 2 | Structural Equation Modeling

In the second installment of this video series, I will discuss the essential concepts in Growth Curve Modeling within the Structural Equation Modeling framework.

From playlist Growth Curve Models

R & Python - Exploratory Factor Analysis

Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class - this video set covers the updated version with both R and Python. Expanding on cluster analysis, this video examines how to put together concepts

From playlist Human Language (ANLY 540)

Independent Samples t-test in Excel | effect size calculator

In this video, I show how to do independent samples t-test analysis in Excel. Since excel does not provide the option to test the homogeneity of variance and effect size, I introduce an easy-to-use calculator for these. Effect size calculator: https://www.socscistatistics.com/effectsize/d

From playlist Independent Samples t-Test

04-2 Sensitivity Analysis Global

Sobol' and regionalized sensitivity analysis

From playlist QUSS GS 260

Foundations of ANOVA – Variance Between and Within (12-2)

When measuring groups with ANOVA, there are two sources of variance: between and within. Variance between groups is due to actual treatment effect plus differences due to chance (or error). True variance between indicates differences between groups. Variance within the groups is due only t

From playlist WK12 One-Way ANOVA - Online Statistics for the Flipped Classroom

QAICourse6 14Unbiased

From playlist Applied Data Analysis and Statistical Inference