Sobre El Autor
David J. Hand is emeritus professor of mathematics and senior research investigator at Imperial College London. His many books include Statistics: A Very Short Introduction.
Obras de David J. Hand
The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day (2014) 284 copias
Multivariate Analysis of Variance and Repeated Measures: A Practical Approach for Behavioural Scientists (Chapman &… (1987) 2 copias
Pattern detection and discovery : ESF Exploratory Workshop, London, UK, September 16-19, 2002 : proceedings (2002) 1 copia
Discrimination and Classification (Wiley Series in Probability and Statistics - Applied Probability and Statistics… (1981) 1 copia
Il tradimento dei numeri: I dark data e l'arte di nascondere la verità (Italian Edition) (2019) 1 copia
Advances in intelligent data analysis : Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9-11,… (2003) 1 copia
ROC Curves for Continuous Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) 1 copia
Obras relacionadas
Etiquetado
Conocimiento común
- Otros nombres
- HAND, David J.
- Fecha de nacimiento
- 1950
- Género
- male
- Ocupaciones
- statistician
- Biografía breve
- rofessor David Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, where he formerly held the Chair in Statistics. He is also Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and an Honorary Fellow of the Institute of Actuaries, and has served (twice) as President of the Royal Statistical Society. He is a non-executive director of the UK Statistics Authority, and is Chair of the Board of the UK Administrative Data Research Network. He has published 300 scientific papers and 28 books, including Principles of Data Mining, Information Generation, Measurement Theory and Practice, The Improbability Principle, and The Wellbeing of Nations. In 2002 he was awarded the Guy Medal of the Royal Statistical Society, and in 2012 he and his research group won the Credit Collections and Risk Award for Contributions to the Credit Industry. He was awarded the George Box Medal in 2016. In 2013 he was made OBE for services to research and innovation.
http://www.imperial.ac.uk/people/d.j....
Miembros
Reseñas
Helped me understand even better than before, how not everything happens for a reason. Or, how the reasons, when there are reasons for an event, may be so varied and contingent, that we can never expect to know all of them.
Langdradig boek. Alleen hoofdstuk "Life, the Universe and everything" over evolutie theorie en het ontstaan van het heelal, of meer specifiek onze aarde, zijn interessant.
Wierd typo: the author asserts that journeys that would have taken days w/out a car, take but minutes w/ the car. He meant "hours", and the editor should have caught it. Also, "slip over" was maybe intended to be "fall over" or just "slip", unless that is an Anglicism unknown to me.
1. Surrounded by Statistics:
This is really just an introduction to the introduction.
2. Simple Descriptions
"ordinal", "ratio", "absolute" scale. I do not know that I have ever cared.
Summary numbers: the mean, the median, and the mode. There is the range, which is a measure of dispersion. Combined with the mean and the median, it should tell you something. It would be nice to write a program that would generate graphs of the distribution of values given the number of values, the range, the mean, and the median. It would be fun to under specify, too, so that, given only the mean, you could show the variety. There is also the standard deviation, and you could play the same trick with that. We can subdivide the data using quantiles, like the median, only with smaller sections.
3. Collecting Good Data
"observational" vs. "experimental" studies. Sampling the data: "sampling frame", "stratified random sampling" and "clustered random sampling". If we just want to find the average of some value over a total population we can sample and, the author argues, it is the absolute size of the sample rather than the relative size that matters.
4. Probability
"subjective", "frequentist", and "classical". I guess I would call classical "formalist". The cumulative probability distribution graph is drawn as continuous. But I looked on Wikipedia and that is not a necessary property. It is, however, a cadlag function. That makes sense, and I like it better. The probability density function doesn't convey information by checking one particular point, but rather by finding the area under the curve between two points. Wikipedia has a nice graphic showing the relationship of the median, mean, and mode to the probability density function. There is also a probability mass function, useful for representing probabilities of continuous random variables. Comparison of distributions: Bernoulli, binomial extends Bernoulli over multiple trials. The Poisson distribution, according to this author, is useful if the number of trials is unlimited, but I thought that was true of the binomial as well. For continuous variables, we can have the "uniform", "exponential", or "normal" distributions.… (más)
1. Surrounded by Statistics:
This is really just an introduction to the introduction.
2. Simple Descriptions
"ordinal", "ratio", "absolute" scale. I do not know that I have ever cared.
Summary numbers: the mean, the median, and the mode. There is the range, which is a measure of dispersion. Combined with the mean and the median, it should tell you something. It would be nice to write a program that would generate graphs of the distribution of values given the number of values, the range, the mean, and the median. It would be fun to under specify, too, so that, given only the mean, you could show the variety. There is also the standard deviation, and you could play the same trick with that. We can subdivide the data using quantiles, like the median, only with smaller sections.
3. Collecting Good Data
"observational" vs. "experimental" studies. Sampling the data: "sampling frame", "stratified random sampling" and "clustered random sampling". If we just want to find the average of some value over a total population we can sample and, the author argues, it is the absolute size of the sample rather than the relative size that matters.
4. Probability
"subjective", "frequentist", and "classical". I guess I would call classical "formalist". The cumulative probability distribution graph is drawn as continuous. But I looked on Wikipedia and that is not a necessary property. It is, however, a cadlag function. That makes sense, and I like it better. The probability density function doesn't convey information by checking one particular point, but rather by finding the area under the curve between two points. Wikipedia has a nice graphic showing the relationship of the median, mean, and mode to the probability density function. There is also a probability mass function, useful for representing probabilities of continuous random variables. Comparison of distributions: Bernoulli, binomial extends Bernoulli over multiple trials. The Poisson distribution, according to this author, is useful if the number of trials is unlimited, but I thought that was true of the binomial as well. For continuous variables, we can have the "uniform", "exponential", or "normal" distributions.… (más)
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Zo eenvoudig is het nu ook weer niet en dat past overigens prima bij dit boek. Het leest makkelijk weg maar is tegelijkertijd taaie stof. Steeds als ik een hoofdstuk uit had vroeg ik me af wat ik er nu eigenlijk van onthouden had en wat ik er mee kan in de praktijk. Dat laatste lees je in de laatste paragraaf van de bespreking op mijn boekenblog https://www.rizoomes.nl/boekenblog/… (más)