Binomial Standard Error Matlab
Contents |
Search All Support Resources Support Documentation MathWorks Search MathWorks.com MathWorks Documentation Support Documentation Toggle navigation Trial binomial distribution standard error Software Product Updates Documentation Home Statistics and Machine Learning Toolbox standard error matlab regression Examples Functions and Other Reference Release Notes PDF Documentation Probability Distributions Discrete Distributions Binomial Distribution binomial sampling error Statistics and Machine Learning Toolbox Functions binostat On this page Syntax Description Examples More About See Also This is machine translation Translated by Mouse over
Binomial Standard Deviation
text to see original. Click the button below to return to the English verison of the page. Back to English × Translate This Page Select Language Bulgarian Catalan Chinese Simplified Chinese Traditional Czech Danish Dutch English Estonian Finnish French German Greek Haitian Creole Hindi Hmong Daw Hungarian Indonesian Italian Japanese binomial confidence interval Korean Latvian Lithuanian Malay Maltese Norwegian Polish Portuguese Romanian Russian Slovak Slovenian Spanish Swedish Thai Turkish Ukrainian Vietnamese Welsh MathWorks Machine Translation The automated translation of this page is provided by a general purpose third party translator tool. MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. Translate binostatBinomial mean and variancecollapse all in page Syntax[M,V] = binostat(N,P)
Description[M,V] = binostat(N,P) returns the mean of and variance for the binomial distribution with parameters specified by the number of trials, N, and probability of success for each trial, P. N and P can be vectors, matrices, or multidimensional arrays that have the same size, which is also the size of M and V. A scalar input for N or P is expanded to a constant array with the same dimensions as the other input.The mean of the binomial
Search All Support Resources Support Documentation MathWorks binomial variance Search MathWorks.com MathWorks Documentation Support Documentation Toggle navigation
Binomial T Test
Trial Software Product Updates Documentation Home Statistics and Machine Learning Toolbox Examples
Binomial Central Limit Theorem
Functions and Other Reference Release Notes PDF Documentation Probability Distributions Discrete Distributions Binomial Distribution Statistics and Machine Learning Toolbox Probability Distributions http://www.mathworks.com/help/stats/binostat.html Discrete Distributions Multinomial Distribution Statistics and Machine Learning Toolbox Probability Distributions Discrete Distributions Negative Binomial Distribution Statistics and Machine Learning Toolbox Probability Distributions Discrete Distributions Poisson Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Beta Distribution Statistics and Machine http://www.mathworks.com/help/stats/std.html Learning Toolbox Probability Distributions Continuous Distributions Birnbaum-Saunders Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Burr Type XII Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Exponential Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Extreme Value Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Gamma Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Generalized Extreme Value Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Generalized Pareto Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Half-Normal Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Inverse Gaussian Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Kernel Distribution Statistics and Machine Learning Toolbox Probability Distributions
Search All Support Resources Support Documentation MathWorks Search MathWorks.com MathWorks Documentation Support Documentation Toggle navigation Trial Software Product Updates Documentation Home Statistics and Machine Learning https://www.mathworks.com/help/stats/binofit.html Toolbox Examples Functions and Other Reference Release Notes PDF Documentation Probability Distributions Discrete Distributions Binomial Distribution Statistics and Machine Learning Toolbox Functions binofit On this page Syntax Description Examples More About References See Also This is machine translation Translated by Mouse over text to see original. Click the button below to return to the English verison of the standard error page. Back to English × Translate This Page Select Language Bulgarian Catalan Chinese Simplified Chinese Traditional Czech Danish Dutch English Estonian Finnish French German Greek Haitian Creole Hindi Hmong Daw Hungarian Indonesian Italian Japanese Korean Latvian Lithuanian Malay Maltese Norwegian Polish Portuguese Romanian Russian Slovak Slovenian Spanish Swedish Thai Turkish Ukrainian Vietnamese Welsh MathWorks Machine Translation The automated translation standard error matlab of this page is provided by a general purpose third party translator tool. MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. Translate binofitBinomial parameter estimatescollapse all in page Syntaxphat = binofit(x,n)
[phat,pci] = binofit(x,n)[phat,pci] = binofit(x,n,alpha)
Descriptionphat = binofit(x,n) returns a maximum likelihood estimate of the probability of success in a given binomial trial based on the number of successes, x, observed in n independent trials. If x = (x(1), x(2), ... x(k)) is a vector, binofit returns a vector of the same size as x whose ith entry is the parameter estimate for x(i). All k estimates are independent of each other. If n = (n(1), n(2), ..., n(k)) is a vector of the same size as x, the binomial fit, binofit, returns a vector whose ith entry is the parameter estimate based on the number of successes x(i) in n(i) independent trials. A scalar value for x or n is expanded to the same size as the other input.