Tag Archives: Statistics

Confidence Intervals in Python

Suppose you are interested in finding out the mean weight of all Sumo wrestlers in Japan. Or the average gas consuption of Korean made automobiles… Why…? No idea, but that sort of statistics might be of interest, for someone, at … Continue reading

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Central Limit Theorem illustrated

A fundamental theorem of statistics is the Central Limit Theorem, which basically states that the averages (means) of a large number of samples drawn from any population converges towards a normal distribution. The graph above demonstrates this theorem: the blue … Continue reading

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Wind,Temperature,Air Pressure statistics

The above graphs show statistics from SMHI, the swedish meteorological and hydrographical institute, on air pressure, temperature and wind speed.  For each categrory, (pressure,temp,wind speed) there are two graphs: the first a frequency chart, the second a Q-Q-chart, showing the … Continue reading

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Wind Speed Distribution – is it Normal…?

As a sailor, I’ve become obsessed by wind. Trying to understand its behavior has kept me busy for decades, and still, I can’t claim I fully understand wind behavior. Anyways, one of the things I’ve wondered is whether wind is … Continue reading

Posted in Data Analytics, development, Nautical Information Systems, Probability, Python, software, Statistics | Tagged , , , , , , , | 3 Comments

MH370 and Bayesian reasoning: example of SAR optimization

In two previous posts, [1,2] I’ve covered the fundamentals of Bayesian probability theory.  The second post looked into how the air distaster investigation team might have proceeded, once they received Inmarsat’s satellite data, to assess the likelihood of the flight path … Continue reading

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Bayesian reasoning and SAR for flight MH370

I covered the basics of Bayesian reasoning in a previous post. So, let’s apply Bayesian reasoning to the search & rescue operation of flight MH370. As we now know, Inmarsat’s identification of the most likely path taken by MH370, by … Continue reading

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Bayesian reasoning made simple by Golden Retrievers and other canines

    Just finished reading Brian Clegg’s excellent ‘Dice World – science and life in a random universe‘. Highly recommended to anyone who’s interested in the inherent unpredictability of our world, and even more highly recommended to all those empty … Continue reading

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Surrounded by average people… what’s the likely IQ of your neighbour…?

IQ, the intelligence quotient, is defined as a normal distribution with mean of 100, and a standard deviation of 15. According to James Flynn, people with IQ 70 or less, are mentally retarded, while those with IQ 130 or more, … Continue reading

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Probability Distributions for Dummies – Drunken walks and blindfolded archers

After having spent a few evenings exploring Python’s various math & science modules, specifically those dealing with probability, simulation and statistics, perhaps it’s time for a brief summary. In my previous recent posts, I’ve gone thru concepts such as mild … Continue reading

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How accurate and precise is your aim ? Skilled and unskilled archers

In a previous post, The Blindfolded Archer, I described the difference between wild and mild randomness, the former exemplified by the blindfolded archer and the Cauchy probability distribution, and characterised by the “long tail”, that is, precence of extreme events, … Continue reading

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