Energy statistics refers to collecting, compiling, analyzing and disseminating data on commodities such as coal, crude oil, natural gas, electricity, or renewable energy sources (biomass, geothermal, wind or solar energy), when they are used for the energy they contain. Energy is the capability of some substances, resulting from their physico-chemical properties, to do work or produce heat. Some energy commodities, called fuels, release their energy content as heat when they burn. This heat could be used to run an internal or external combustion engine. The need to have statistics on energy commodities became obvious during the 1973 oil crisis that brought tenfold increase in petroleum prices. Before the crisis, to have accurate data on global energy supply and demand was not deemed critical. Another concern of energy statistics today is a huge gap in energy use between developed and developing countries. As the gap narrows (see picture), the pressure on energy supply increases tremendously. The data on energy and electricity come from three principal sources: * Energy industry * Other industries ("self-producers") * Consumers The flows of and trade in energy commodities are measured both in physical units (e.g., metric tons), and, when energy balances are calculated, in energy units (e.g., terajoules or tons of oil equivalent). What makes energy statistics specific and different from other fields of economic statistics is the fact that energy commodities undergo greater number of transformations (flows) than other commodities. In these transformations energy is conserved, as defined by and within the limitations of the first and second laws of thermodynamics. (Wikipedia).
Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
https://www.patreon.com/ProfessorLeonard Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
From playlist Statistics (Full Length Videos)
Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
Teach Astronomy - Types of Energy
http://www.teachastronomy.com/ There are several broad types of energy. Energy is measured in units of calories in the English system or joules in the international system of metric units. One broad category of energy is kinetic energy, or the energy of motion. A second broad category of
From playlist 04. Chemistry and Physics
Statistics: Collecting Data Exercises
This video covers sample, population, qualitative data, quantitative data, sampling methods, sampling bias, experimental and observational studies, and the types of experiments. http://mathispower4u.com
From playlist Introduction to Statistics
Parametric vs Nonparametric Spectrum Estimation
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduces parametric (model-based) and nonparametric (Fourier-based) approaches to estimation of the power spectrum.
From playlist Estimation and Detection Theory
This lecturelet will introduce you to the series on statistical analyses of time-frequency data. For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
From playlist OLD ANTS #8) Statistics
measuring the energy of the very small
An electron volt is a unit of energy, but how is it derived and determined? In this video I quickly review how energy is increased in fields, starting with a familiar gravitational field situation and then discuss an electron in an electric field. Quickly discussing the mathematics involv
From playlist Electricity and Magnetism
Fellow Short Talks: Dr Quentin Berthet, Cambridge University
Quentin Berthet is a Lecturer in the Statslab, in the DPMMS at Cambridge, and a fellow of St John’s College, since 2015. He is a former student of the Ecole Polytechnique, received a Ph.D. from Princeton University in 2014, and was a CMI postdoctoral fellow at Caltech. RESEARCH Dr Berthe
From playlist Short Talks
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
Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
https://www.patreon.com/ProfessorLeonard Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
From playlist Statistics (Full Length Videos)
Neuroscience as source separation
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #1) Introductions
Protein Collapse and Folding by Govardhan Reddy
Indian Statistical Physics Community Meeting 2016 URL: https://www.icts.res.in/discussion_meeting/details/31/ DATES Friday 12 Feb, 2016 - Sunday 14 Feb, 2016 VENUE Ramanujan Lecture Hall, ICTS Bangalore This is an annual discussion meeting of the Indian statistical physics community wh
From playlist Indian Statistical Physics Community Meeting 2016
Formal verification and learning of complex systems - Professor Alessandro Abate
For slides, future Logic events and more, please visit: https://logic-data-science.github.io/?page=logic_learning Two known shortcomings of standard techniques in formal verification are the limited capability to provide system-level assertions, and the scalability to large-scale, complex
From playlist Logic and learning workshop
Statistical Rethinking Fall 2017 - week10 lecture19
Week 10, lecture 19 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapters 14 and 15. Slides are available here: https://speakerdeck.com/rmcelreath/statistical-rethinking-fall-2017-lecture-19 Additional in
From playlist Statistical Rethinking Fall 2017
Multiscaling in Randomly Forced Hydrodynamical Equations by Rahul Pandit
Program Turbulence: Problems at the Interface of Mathematics and Physics (ONLINE) ORGANIZERS: Uriel Frisch (Observatoire de la Côte d'Azur and CNRS, France), Konstantin Khanin (University of Toronto, Canada) and Rahul Pandit (Indian Institute of Science, Bengaluru) DATE: 07 December 202
From playlist Turbulence: Problems at The Interface of Mathematics and Physics (Online)
Mean field asymptotics in high-dimensional statistics – A. Montanari – ICM2018
Probability and Statistics Invited Lecture 12.16 Mean field asymptotics in high-dimensional statistics: From exact results to efficient algorithms Andrea Montanari Abstract: Modern data analysis challenges require building complex statistical models with massive numbers of parameters. It
From playlist Probability and Statistics
India-CMS Collaboration by Sanjay Swain (1)
DISCUSSION MEETING PARTICLE PHYSICS: PHENOMENA, PUZZLES, PROMISES ORGANIZERS: Amol Dighe, Rick S Gupta, Sreerup Raychaudhuri and Tuhin S Roy, Department of Theoretical Physics, TIFR, India DATE: 21 November 2022 to 23 November 2022 VENUE: Ramanujan Lecture Hall and Online While the LH
From playlist PARTICLE PHYSICS: PHENOMENA, PUZZLES, PROMISES - 2022
Statistics Lecture 5.4: Finding Mean and Standard Deviation of a Binomial Probability Distribution
https://www.patreon.com/ProfessorLeonard Statistics Lecture 5.4: Finding the Mean and Standard Deviation of a Binomial Probability Distribution
From playlist Statistics (Full Length Videos)