Falling Object with Air Resistance

Not formally a part of my physics book (which I am posting here chapter by chapter, completely for free as it is being written), but following the same trend, I decided to write up the solution to an interesting problem: that of deriving the solutions to the differential equations for an object falling in a gravitational field  through an atmosphere.  It is useful to know how to solve this problem, because the same method can be generally applied to any situation where there is both a variable force and a constant force that work together to cause an acceleration, or perhaps even eliminate acceleration, as in the case of terminal velocity, when the positive constant force and the negative variable force find their equilibrium. I will assume a relatively high mathematical literacy, but if you do have any questions, leave a comment and I’ll get back to you as soon as I can.

Here is the PDF.


Equations of Motion

I have been writing a theoretical physics book for college students. It is essentially a concise, yet minimalistic set of notes on each topic, designed for revision purposes. Here is the first chapter, Equations of Motion.

I will continue uploading chapters of this book for free here, on my blog, as I write them.

The Tuned Deck

Measure of Doubt

In Sweet Dreams: Philosophical Objections to a Science of Consciousness, Daniel Dennett recounts the story of “The Tuned Deck,” a mysterious magic trick that’s more — or less? — than it seems. Conjurer Ralph Hull performed it over and over throughout his life, challenging his fellow magicians to figure out how it was done. Here’s Dennett’s account of how Hull’s act went:

“Boys, I have a new trick to show you. It’s called ‘The Tuned Deck’. This deck of cards is magically tuned [Hull holds the deck to his ear and riffles the cards, listening carefully to the buzz of the cards]. By their finely tuned vibrations, I can hear and feel the location of any card. Pick a card, any card… [The deck is then fanned or otherwise offered for the audience, and a card is taken by a spectator, noted, and returned to the deck by one…

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Bayesian Statistics For Dummies


  • The following is an excerpt from an article by Kevin Boone. Bayesian statistics is so simple, yet fundamental a concept that I really believe everyone should have some basic understanding of it. Please, take your time and read carefully.


‘Bayesian statistics’ is a big deal at the moment. It has been put forward as a solution to a number of important problems in, among other disciplines, law and medicine. These problems are concerned with such matters as determining the likelihood that a particular suspect committed a murder if his fingerprints are found on the weapon, or the likelihood that a person who tests positive for HIV really has an HIV infection. These are, clearly, important matters. However, most people can’t see that there are any difficulties here at all. If the likelihood of two people having the same fingerprints is, say, one in 500,000, and fingerprints matching person X are found on the murder weapon, then surely it is a near-certainty that X is the murderer? And if I tell you that 99.5% of people with a confirmed HIV infection test positive for HIV, then surely a person who tests positive is 99.5% likely to have the virus? In fact, both of these conclusions of wrong. Dead wrong, if you’ll pardon the expression. The importance of Bayes’ theorem is that it will tell you the true likelihood of a person having an HIV infection if he tests positive, and the true likelihood of person X being the murderer if his fingerprints turn up on the weapon. Or, at least, it will do so if you can feed in the right data, which isn’t always easy.

In a celebrated court case (R v Adams [1998] 1 Cr App R 377, for any lawyers that are interested) Lord Bingham, one of the UK’s most senior judges, refused to allow the defence to present an argument to the jury based on Bayes’ theorem. He conceded that it was a methodologically sound approach, but that it would ‘confuse the jury’. In fact, Bayes’ theorem is extremely straightforward, and need not confuse anyone who can add, multiply, and divide. The result of this judge’s decision could well have been that an innocent person was convicted, because a likelihood based on a Bayesian calculation is not merely better than the intuitive result, it is the only right answer. Any different answer is simply wrong. Not wrong in the sense of ‘stealing is wrong’, but wrong in the sense that ‘2+2=5′ is wrong.

In this article I will explain how Bayes’ theorem works, from first principles. I assume of the reader no knowledge of mathematics beyond elementary arithemetic. At the end of the article I will attempt to show how Bayes’ theorem can be derived from the common-sense example I present, and to follow this does require a knowledge of basic algebra. But you don’t need to be able to follow the derivation to appreciate how Bayes’ method works. I will start by considering, as the Reverend Mr Bayes himself did, the best way to bet on a horse race.

See full article here.