See all my videos here: http://www.zstatistics.com/videos/ See the whole regression series here: https://www.youtube.com/playlist?list=PLTNMv857s9WUI1Nz4SssXDKAELESXz-bi 0:00 Introduction 1:41 Techincal definition 5:25 Why care? 8:12 Detection method 1 - Resiudal plots 8:42 Detection me
From playlist Regression series (10 videos)
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
Comparison of systematic and random error. Types of systematic error, including offset error and scale factor error/
From playlist Experimental Design
Matched Pairs t Confidence Interval
I do an example of setting up a Matched Pairs Mean t-Confidence Interval. The sixth student's score was mis-copied and should be a 95 instead of a 92. Sorry for the small copy error. There is an annotation for the Normality check. Normality: With the difference being fairly symmetric &
From playlist AP Statistics
Teach Astronomy - Measurement Error
http://www.teachastronomy.com/ There is no such thing as a perfect measurement. In science there's no such thing as perfect data. All observations and measurements have errors attached. The word error is perhaps inappropriate. Rather we should call it uncertainty due to limitations in
From playlist 01. Fundamentals of Science and Astronomy
Brief overview of the standard error. What it represents and how you would find it with a formula.
From playlist Basic Statistics (Descriptive Statistics)
Bayesian Optimization in the Wild: Risk-Averse Decisions and Budget Constraints
A Google TechTalk, presented by Anastasia Makarova, 2022/08/23 Google BayesOpt Speaker Series - ABSTRACT: Black-box optimization tasks frequently arise in high-stakes applications such as material discovery or hyperparameter tuning of complex systems. In many of these applications, there i
From playlist Google BayesOpt Speaker Series 2021-2022
Intro to the ARCH (Auto Regressive Conditional Heteroskedasticity) model in time series analysis.
From playlist Time Series Analysis
Regression assumptions explained!
See all my videos at http://www.zstatistics.com/ See the whole regression series here: https://www.youtube.com/playlist?list=PLTNMv857s9WUI1Nz4SssXDKAELESXz-bi 0:00 Introduction 8:08 Linearity (correct functional form) 14:10 Constant error variance (homoskedasticity) 19:18 Independent e
From playlist Regression series (10 videos)
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the basics of linear regression including assumptions, hypothesis testing, how to understand overall models and coefficients, how to examine for outliers, and how to run categorical va
From playlist Graduate Statistics Flipped
Efficient Exploration in Bayesian Optimization – Optimism and Beyond by Andreas Krause
A Google TechTalk, presented by Andreas Krause, 2021/06/07 ABSTRACT: A central challenge in Bayesian Optimization and related tasks is the exploration—exploitation dilemma: Selecting inputs that are informative about the unknown function, while focusing exploration where we expect high ret
From playlist Google BayesOpt Speaker Series 2021-2022
Random and systematic error explained: from fizzics.org
In scientific experiments and measurement it is almost never possible to be absolutely accurate. We tend to make two types of error, these are either random or systematic. The video uses examples to explain the difference and the first steps you might take to reduce them. Notes to support
From playlist Units of measurement
How to calculate a pooled standard error for a difference in sample means (used in two sample t tests). Step by step example of working the formula. Check out my Statistics Handbook: https://www.statisticshowto.com/the-practically-cheating-statistics-handbook/ Thanks for your support!
From playlist t-test
Time Varying Volatility and GARCH in Risk Management
These classes are all based on the book Trading and Pricing Financial Derivatives, available on Amazon at this link. https://amzn.to/2WIoAL0 Check out our website http://www.onfinance.org/ Follow Patrick on twitter here: https://twitter.com/PatrickEBoyle In Todays video let's learn abo
From playlist Risk Management
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments
The Data Science Institute (DSI) hosted a virtual seminar by Robert Gramacy from Virginia Tech on March 15, 2021. Read more about the DSI seminar series at https://data-science.llnl.gov/latest/seminar-series. We investigate the merits of replication and provide methods that search for opti
From playlist DSI Virtual Seminar Series
R - Frequency and Response Latency with Linear Regression
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class. This video covers linear regression focusing on response latencies and how to deal with skewed data. Note: these videos are part of live online lec
From playlist Human Language (ANLY 540)
24. Generalized Linear Models (cont.)
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about Hessian, Fisher information, weighted least squares, and iteratively reweighed least squares. License: Creat
From playlist MIT 18.650 Statistics for Applications, Fall 2016
Teach Astronomy - Random and Systematic Errors
http://www.teachastronomy.com/ In science we deal with two fundamentally different types of errors. Random errors are usually associated with limitations in the measuring apparatus. A random error can displace a measurement either to the high or low side of the true value. Random errors
From playlist 01. Fundamentals of Science and Astronomy
R - Latent Growth Models Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom In this section, you will learn about latent growth models and how to analyze them in a similar fashion to multilevel models. You can learn more at: https://statisticsofdoom.com/page/structural-equation
From playlist Structural Equation Modeling 2020