Search results
Results from the WOW.Com Content Network
Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file
This statistics -related article is a stub. You can help Wikipedia by expanding it.
This statistics -related article is a stub. You can help Wikipedia by expanding it.
The errors of omission vs. errors of commission effect, [30] in which, all other things being equal, people prefer to make errors by inaction (Stay) as opposed to action (Switch). Experimental evidence confirms that these are plausible explanations that do not depend on probability intuition.
Graphs of probability P of not observing independent events each of probability p after n Bernoulli trials vs np for various p.Three examples are shown: Blue curve: Throwing a 6-sided die 6 times gives a 33.5% chance that 6 (or any other given number) never turns up; it can be observed that as n increases, the probability of a 1/n-chance event never appearing after n tries rapidly converges to 0.
The law of truly large numbers (a statistical adage), attributed to Persi Diaconis and Frederick Mosteller, states that with a large enough number of independent samples, any highly implausible (i.e. unlikely in any single sample, but with constant probability strictly greater than 0 in any sample) result is likely to be observed. [1]
The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
Given a set of i.i.d. realizations of random variables , then the empirical distribution function is ^ ():= = (<), with the indicator function and the (normalized) weights . ...