Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
R - Multinomial Logisitic Regression Example
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2018 This video replaces a previous live in-class video. You will learn about how to analyze a multinomial logistic regression. We start by exploring power using G*Power, then the simple data screening required for log regre
From playlist Learn and Use G*Power
R - Multinomial Log Regression Example
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2016 This video covers how to run and interpret a multinomial logistic regression using mlogit - from assessing ratio of cases, additivity, to understanding the reshaping of data and interpreting the output. Note: This vid
From playlist Learn and Use G*Power
Logistic Regression - Is it Linear Regression?
Is it Linear? Why the sigmoid? Let's talk about it. Breaking Linear Regression video: https://www.youtube.com/watch?v=Bu1WCOQpBnM RESOURCES [1] Great Lecture notes to start understanding Logistic Regression: https://pages.stat.wisc.edu/~st849-1/lectures/GLMH.pdf [2] More slightly detaile
From playlist Logistic Regression
Linear regression ANOVA ANCOVA Logistic Regression
In this video tutorial you will learn about the fundamentals of linear modeling: linear regression, analysis of variance, analysis of covariance, and logistic regression. I work through the results of these tests on the white board, so no code and no complicated equations. Linear regressi
From playlist Statistics
Lecture 4 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to
From playlist Lecture Collection | Machine Learning
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://go.umd.edu/jbg-inst-808 (Including homeworks and reading.) Music: https://soundcloud.com/alvin-grissom-ii/review-and-rest
From playlist Deep Learning for Information Scientists
Overview of logistic regression, a statistical classification technique.
From playlist Machine Learning
What is Multicollinearity? Extensive video + simulation!
See all my videos at http://www.zstatistics.com/videos/ 0:00 Introduction 2:16 Intuition 4:13 How does it affect our regression output? 6:55 Detection method I: Correlations 8:37 Detection method II: Variance Inflation Factors (VIFs) 11:50 Remedies 15:13 Justin's Simulation (COOL!) 22:17
From playlist Regression series (10 videos)
Stanford Webinar - How to Analyze Research Data: Kristin Sainani
In this webinar, Associate Professor Kristin Sainani walks you through the steps of a complete data analysis, using real data on mental health in athletes. She provides practical, hands-on tips for how to approach each step of the analysis and how to improve rigor and reproducibility of yo
From playlist Statistics and Data Science
R - Binary Logistic Regression Lecture & Example
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2016 This video covers how to run and interpret a binary logistic regression using glm - from assessing ratio of cases, additivity, to understanding the output. Lecture materials and assignments available at statisticsofdo
From playlist Learn and Use G*Power
Stanford CS229: Machine Learning | Summer 2019 | Lecture 7 - GDA, Naive Bayes & Laplace Smoothing
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqcX9P Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Stanford CS229: Machine Learning | Summer 2019 | Lecture 23 - Course Recap and Wrap Up
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3B6WitS Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
R & Python - Logistic Regression
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. Next in our series is logistic regression - treated more as a statistical techni
From playlist Human Language (ANLY 540)
Applied Machine Learning 2019 - Lecture 07 - Linear Models for Classifications, SVMs
Logistic Regression, linear SVMs, the kernel trick One-vs-Rest and One-vs-One multi-class strategies. Class website with slides and more materials: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
R & Python - Classification Part 2
Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my Natural Language Processing course. This video explores the basic concepts of classification with a focus on text data using word2vec, bag of words, tfidf as feature extraction a
From playlist Natural Language Processing
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.