- published: 10 Mar 2016
- views: 1333
QRadar Open Mic replay: 7.2.5 Features. Part 3 - Historical Correlation. Open Mic presentation: https://ibm.biz/BdXvRU Discusses Historical Correlation and how users can run events through a new CRE instance to generate offenses for historical data. This is a new feature added to the User Interface for users in QRadar 7.2.5.
This video provides an overview of the results of a correlation study that compared the Bradley Siderograph and the Advanced Astro Indicator against 60 securities for 2015. NOTE: At the end of the video I meant to say that I found the "Long Terms and No Opposite Weights" to be the most interesting Advanced Astro Indicator (i.e., I didn't mean to say "Long Terms and No Oppositions.").
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This video explains the basics of correlation, and shows how to find the correlation between two assets step by step. Join us in the discussion on InformedTrades: http://www.informedtrades.com/851244-how-find-correlation-between-two-assets-step-step.html Practice trading using a free Forex demo account: http://tracking.leadfinance.com/SH3S
Is the Historical Timeline presented to Us Correct? Are We really in the 21st Century? Old Maps and Illustrations give Us a very Different Picture. Isaac Newton Chronological Studies. http://chronologia.org/en/ http://www.ilya.it/chrono/enpages/weristwer.html#N
View full lesson: http://ed.ted.com/lessons/the-wars-that-inspired-game-of-thrones-alex-gendler Beginning around 1377, medieval England was shaken by a power struggle between two noble families, which spanned generations and involved a massive cast of characters, complex motives and shifting loyalties. Sound familiar? Alex Gendler illustrates how the historical conflict known as the Wars of the Roses served as the basis for much of the drama in Game of Thrones. Lesson by Alex Gendler, animation by Brett Underhill.
We discuss linear correlation between two variables, least squares linear regression and non-linear transformations.
Discusses how to download two companies' stock returns from Yahoo Finance, and calculate (a) the variance and standard deviation of each stock, and (b) the covariance and correlation of the returns of both stocks. Some good books on Excel and Finance: Financial Modeling - by Benninga: http://amzn.to/2tByGQ2 Principles of Finance with Excel - by Benninga: http://amzn.to/2uaCyo6
The definition, visualization and demonstration of a calculation of correlation in Excel. Including =AVERAGE function and =SUM function. For investment and financial modeling of stocks and portfolios. Of course you can use the =COVARIANCE.P function or =CORREL but to truly learn investment modeling knowing this calculation is vital. https://factorpad.com/fin/glossary/correlation.html Topics covered in our investment glossary: Excel tutorial, Python examples, portfolio theory, portfolio return, portfolio risk, correlation, regression, linear algebra, alpha signal, risk models, performance attribution. Glossary: https://factorpad.com/fin/glossary/index.html Innovators: https://factorpad.com/fin/innovators/index.html https://factorpad.com
How to generate correlation graphs for two or more sets of genomic regions
This is a bit messy, but I recently managed to find some of the files that I used for an article on using Eclipses in the Anglo-Saxon Chronicles to calibrate that historical document, and possibly other events, to the one absolute constant; the movement of time expressed in the sky. There are a lot of difficulties with this approach, and this is only a quick over view. It is not a proof or a rebuttal. When I saw the 'Missing/Faked History' videos I obviously thought of my own work I did on this article about 20 years ago. To investigate and establish the fundamental truth behind the solar eclipse entries, it would require a doctorate level of investigation. I will aim to do a follow up video on this as soon as I have more time and resource, but I had to just rush this one out!
This episode discusses the factors that explain why carbon dioxide and temeperature don't correlate perfectly. 0:00-1:27 -Global Temperature and CO2 graphical representation -Decade to Decade time scale variability 1:28-4:06 -Graphs of isolated scenarios for temperature change (Anthropogenic greenhouse gases, Anthropogenic Aerosols, Volcanic Aerosols, and Solar radiation) -Graphs of all influences combined 4:07-7:34 -Internal Variability (changes in ocean heat absorption) -Fictional Example of isolated Internal Variability and example with combined Greenhouse Gases
QRadar taxonomy simplify the process of creating searches and rules because you do not have to necessarily actually see the actual event to create the search or the rule. The Taxonomy has been created so well that it covers a great variety of events. A file with the links to all my recent videos can be found here: https://ibm.box.com/s/ich0yyiw54y0ek6s9a66xvtjku8e42rc
An introduction to Volatility and Correlation using components of the corresponding module found under Optimal MRM's market risk e-Learning service. The full presentation includes risk measurement exercises in Excel and guides subscribers as they practice the concepts and techniques presented in a hands-on manner. We invite you to attend a complimentary e-Learning demo module (http://www.optimalmrm.com/services/elearning-catalog/17-banks/22-basel/) to experience how Optimal MRM delivers a practical understanding of risk in a rich and interactive manner.
This video clip from the film "Global Warming or Global Governance" shows that a major premise of the film "Inconvenient Truth" is that Carbon Dioxide is the major greenhouse gas that drives the temperature of the Earth. In fact a high correlation does exist between CO2 levels and temperate, however examinations of ice core data proves that temperature levels have driven the levels of CO2, not the other way around. Historically CO2 levels have lagged temperature increases by as much as 800 years. For a written description of the information in this video clip, see: http://newsofinterest.tv/global_warming/video_summaries/gwgg.php
Today we’re going to talk about data relationships and what we can learn from them. We’ll focus on correlation, which is a measure of how two variables move together, and we’ll also introduce some useful statistical terms you’ve probably heard of like regression coefficient, correlation coefficient (r), and r^2. But first, we’ll need to introduce a useful way to represent bivariate continuous data - the scatter plot. The scatter plot has been called “the most useful invention in the history of statistical graphics” but that doesn’t necessarily mean it can tell us everything. Just because two data sets move together doesn’t necessarily mean one CAUSES the other. This gives us one of the most important tenets of statistics: correlation does not imply causation. Crash Course is on Patreon! ...
Predicting Stock Price movement statistically. Here we use historical data to predict the movement of stock price for next day. It is completely mathematically valid. The mathematical model of Brownian motion has several real-world applications. Stock market fluctuations are often cited, although Benoit Mandelbrot rejected its applicability to stock price movements in part because these are discontinuous. This is a momentum indicator used in technical analysis, which compares the stock's closing price to its price over the course of a particular time frame. During an upward trend in the market, a stock's share price will close near its high (highest price traded), and when in a downward-trending market, the security's price will close near the low (lowest price traded). This may determin...
Bad historical assumptions about why things happen - after all, ice cream consumption was blamed for causing polio once upon a time. Clip from the 2010 documentary "Freakonomics: The Movie". A dream team of directors explore the hidden side of everything.
This Great Companies, Inc. Fundamental Research graph supports the old adage; earnings are optional but cash is king, and L-3 generates strong operating cash flows (Dark Orange area marked with O) and importantly free cash flow (Orange Line marked F) that closely approximates earnings per share. So even though debt represents 44% of their total capital, they clearly generate more than adequate cash flows to handle it.