Debunking Handbook, The Posted on 27 November 2011 by John Cook The Debunking Handbook, a guide to debunking misinformation, is now freely available to download. Although there is a great deal of psychological research on misinformation, there's no summary of the literature that offers practical guidelines on the most effective ways of reducing the influence of myths. The Debunking Handbook boils the research down into a short, simple summary, intended as a guide for communicators in all areas (not just climate) who encounter misinformation. The Handbook explores the surprising fact that debunking myths can sometimes reinforce the myth in peoples' minds. It also looks at a key element to successful debunking: providing an alternative explanation. The Authors: John Cook is the Climate Change Communication Fellow for the Global Change Institute at the University of Queensland. Professor Lewandowsky is an Australian Professorial Fellow and a cognitive scientist at the University of Western Australia.

What is volatility? Some facts and some speculation. Definition Volatility is the annualized standard deviation of returns — it is often expressed in percent. A volatility of 20 means that there is about a one-third probability that an asset’s price a year from now will have fallen or risen by more than 20% from its present value. In R the computation, given a series of daily prices, looks like: sqrt(252) * sd(diff(log(priceSeriesDaily))) * 100 Usually — as here – log returns are used (though it is unlikely to make much difference). Historical estimation What frequency of returns should be used when estimating volatility? There is folklore that it is better to use monthly data than daily data because daily data is more noisy. However, this is finance, so things aren’t that easy. Another complication is if there are assets from around the globe. Through time Volatility would be more boring if finance were like other fields where standard deviations never change. But why? Across assets Implied volatility Not risk

Graph of the Week Forecasting within limits Forecasting within limits It is common to want forecasts to be positive, or to require them to be within some specified range . Both of these situations are relatively easy to handle using transformations. Positive forecasts To impose a positivity constraint, simply work on the log scale. . Forecasts constrained to an interval To see how to handle data constrained to an interval, imagine that the egg prices were constrained to lie within and . to the whole real line: where is on the original scale and is the transformed data. The prediction intervals from these transformations have the same coverage probability as on the transformed scale, because quantiles are preserved under monotonically increasing transformations. Related Posts:

FLCT: Funny Little Calculus Text - Robert W. Ghrist Classics in Psychology An internet resource developed byChristopher D. Green , ISSN 1492-3173 (Return to Classics index) Last updated . 19th- & 20th-Century Psychology Can't find what you want? Ancient Thought Plato. Aristotle. Aristotle. For additional works by the Presocratics, Plato, Aristotle, Hippocrates, Euclid, Lucretius, Epictetus, Galen, Plotinus, and Augustine, see the Links to Documents at Other Sites page. Medieval & Renaissance Thought For works by Aquinas, Roger Bacon, Pico, and Machiavelli see the Links to Documents at Other Sites page. Modern Philosophical Thought Berkeley, George. (1732). Bowen, Francis. (1860). McCosh, James. (1874). Herbart, J. Fiske, John. (1902). Royce, Josiah. (1902). Stumpf, Carl. (1930). Titchener, E. Creighton, J. For additional works by Descartes, Hobbes, Pascal, Locke, Leibniz, Spinoza, , Voltaire, Hume, Smith, Malthus, Kant, Hegel, Marx, Mill, Brentano, Mach, Peirce, James, Dewey, Husserl, Russell, Mead, and Merleau-Ponty see the Links to Documents at Other Sites page.

Time Series Analysis | R Statistics.Net Any metric that is measured over time is a time series. It is of high importance because of industrial relevance especially w.r.t forecasting (demand, sales, supply etc). It can be broken down to its components so as to systematically forecast it. What is a Time Series ? Any metric that is measured over regular time intervals makes a Time Series. How To Create A Time Series In R ? Upon importing your data into R, use ts() function as follows. ts (inputData, frequency = 4, start = c(1959, 2)) # frequency 4 => Quarterly Data ts (1:10, frequency = 12, start = 1990) # freq 12 => Monthly data. Understanding Your Time Series Each datapoint (Yt) in a Time Series can be expressed as either a sum or a product of 3 components, namely, Seasonality(St), Trend(Tt) and Error(et) (a.k.a White Noise). For Additive Time Series, Yt = St + Tt + et For Multiplicative Time Series, Yt = St * Tt * et A multiplicative time series can be converted to additive by taking a log of the time series.

RStudio Blog Interpreting noise When watching the TV news, or reading newspaper commentary, I am frequently amazed at the attempts people make to interpret random noise. For example, the latest tiny fluctuation in the share price of a major company is attributed to the CEO being ill. When the exchange rate goes up, the TV finance commentator confidently announces that it is a reaction to Chinese building contracts. No one ever says “The unemployment rate has dropped by 0.1% for no apparent reason.” What is going on here is that the commentators are assuming we live in a noise-free world. They imagine that everything is explicable, you just have to find the explanation. The finance news Every night on the nightly TV news bulletins, a supposed expert will go through the changes in share prices, stock prices indexes, currency rates, and economic indicators, from the past 24 hours. in magnitude, where Sadly, that’s unlikely to happen. Seasonally adjusted data where and . .

Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook "Thank you and thank you again for providing a complete, well-structured, and easy-to-understand online resource. Every other website or snobbish research paper has not deigned to explain things in words consisting of less than four syllables. I was tossed to and fro like a man holding on to a frail plank that he calls his determination until I came across your electronic textbook...You have cleared the air for me. You have enlightened. You have illuminated. — Mr. "As a professional medical statistician of some 40 years standing, I can unreservedly recommend this textbook as a resource for self-education, teaching and on-the-fly illustration of specific statistical methodology in one-to-one statistical consulting. — Mr. "Excellent book. — Dr. "Just wanted to congratulate whoever wrote the 'Experimental Design' page. — James A. Read More Testimonials >> StatSoft has freely provided the Electronic Statistics Textbook as a public service since 1995. Proper citation:

historic documents in computer science Fortran Fortran Automated Coding System For the IBM 704 the very first Fortran manual, by John Backus, et al., Oct. 1956 Al Kossow has in his manual collection also an IBM 704 manual, if you want to have a look at the machine that this original Fortran language was made for. Also the next IBM manuals are from the collection at his web site, where you can find a number of Fortran manuals, for many machines of various manufacturers. The FORTRAN II General Information Manual and IBM 7090/7094 Programming Systems: FORTRAN II Programming are two IBM manuals of 1963 describing the FORTRAN II language. IBM 7090/7094 Programming Systems: FORTRAN IV Language, 1963, and IBM System 360 and System 370 FORTRAN IV Language, 1974. FORTRAN IV was next to the ANSI standard of 1966 for long time the reference language for Fortran as used by legions of scientists and engineers. Algol60 I think, this is the original Peter Naur edition of the Algol 60 report.

R Video tutorial for Spatial Statistics: Introductory Time-Series analysis of US Environmental Protection Agency (EPA) pollution data Download EPA air pollution data The US Environmental Protection Agency (EPA) provides tons of free data about air pollution and other weather measurements through their website. An overview of their offer is available here: The data are provided in hourly, daily and annual averages for the following parameters: Ozone, SO2, CO,NO2, Pm 2.5 FRM/FEM Mass, Pm2.5 non FRM/FEM Mass, PM10, Wind, Temperature, Barometric Pressure, RH and Dewpoint, HAPs (Hazardous Air Pollutants), VOCs (Volatile Organic Compounds) and Lead. All the files are accessible from this page: The web links to download the zip files are very similar to each other, they have an initial starting URL: and then the name of the file has the following format: type_property_year.zip The type can be: hourly, daily or annual. data <- download.EPA(year=2013,property="ozone",type="daily")

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