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The sample mean (or "empirical mean") and the sample covariance are statistics computed from a sample of data on one or more random variables. The sample mean is the average value (or mean value) of a sample of numbers taken from a larger population of numbers, where "population" indicates not number of people but the entirety of relevant data, whether collected or not. A sample of 40 companies' sales from the Fortune 500 might be used for convenience instead of looking at the population, all 500 companies' sales. The sample mean is used as an estimator for the population mean, the average value in the entire population, where the estimate is more likely to be close to the population mean if the sample is large and representative. The reliability of the sample mean is estimated using the s

Attributes	Values
rdf:type	Thing football league season
rdfs:label	Sample mean and covariance (en)
rdfs:comment	The sample mean (or "empirical mean") and the sample covariance are statistics computed from a sample of data on one or more random variables. The sample mean is the average value (or mean value) of a sample of numbers taken from a larger population of numbers, where "population" indicates not number of people but the entirety of relevant data, whether collected or not. A sample of 40 companies' sales from the Fortune 500 might be used for convenience instead of looking at the population, all 500 companies' sales. The sample mean is used as an estimator for the population mean, the average value in the entire population, where the estimate is more likely to be close to the population mean if the sample is large and representative. The reliability of the sample mean is estimated using the s (en)
rdfs:seeAlso	Variance
sameAs	Sample mean and covariance
dbp:wikiPageUsesTemplate	dbt:Authority_control dbt:Further dbt:Main dbt:Math dbt:More_citations_needed dbt:Multiple_issues dbt:Reflist dbt:See_also dbt:Short_description dbt:Technical
Subject	Covariance and correlation Summary statistics U-statistics Matrices Estimation methods
gold:hypernym	Statistics
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Sample_mean_and_covariance?oldid=1063504049&ns=0
Wikipage page ID	10005756 (xsd:integer)
page length (characters) of wiki page	13590 (xsd:nonNegativeInteger)
Wikipage revision ID	1063504049 (xsd:integer)
has abstract	The sample mean (or "empirical mean") and the sample covariance are statistics computed from a sample of data on one or more random variables. The sample mean is the average value (or mean value) of a sample of numbers taken from a larger population of numbers, where "population" indicates not number of people but the entirety of relevant data, whether collected or not. A sample of 40 companies' sales from the Fortune 500 might be used for convenience instead of looking at the population, all 500 companies' sales. The sample mean is used as an estimator for the population mean, the average value in the entire population, where the estimate is more likely to be close to the population mean if the sample is large and representative. The reliability of the sample mean is estimated using the standard error, which in turn is calculated using the variance of the sample. If the sample is random, the standard error falls with the size of the sample and the sample mean's distribution approaches the normal distribution as the sample size increases. The term "sample mean" can also be used to refer to a vector of average values when the statistician is looking at the values of several variables in the sample, e.g. the sales, profits, and employees of a sample of Fortune 500 companies. In this case, there is not just a sample variance for each variable but a sample variance-covariance matrix (or simply covariance matrix) showing also the relationship between each pair of variables. This would be a 3×3 matrix when 3 variables are being considered. The sample covariance is useful in judging the reliability of the sample means as estimators and is also useful as an estimate of the population covariance matrix. Due to their ease of calculation and other desirable characteristics, the sample mean and sample covariance are widely used in statistics to represent the location and dispersion of the distribution of values in the sample, and to estimate the values for the population. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Sample_mean_and_covariance
is Wikipage redirect of	Sample covariance Sample covariance matrix Sample mean and sample covariance Sample mean Weighted covariance matrix Weighted sample Empirical mean
is Link from a Wikipage to another Wikipage of	List of statistics articles Normal distribution Arithmetic mean Average Behrens–Fisher problem Bernoulli distribution Berry–Esseen theorem Beta distribution Bias of an estimator Bootstrapping (statistics) Monte Carlo method Centering matrix Central limit theorem Central tendency Geometric distribution Gibbs sampling Glossary of engineering: A–L Glossary of probability and statistics Kurtosis Degrees of freedom (statistics) Hotelling's T-squared distribution Confidence interval Consistent estimator Covariance and correlation Covariance matrix Efficiency (statistics) High-dimensional statistics Michael Wolf (statistician) Minimum-variance unbiased estimator Neyman construction Notation in probability and statistics Empirical measure Ensemble Kalman filter Errors and residuals Independent component analysis Expected value F-test of equality of variances Feature learning Fieller's theorem Grouped data Grubbs's test L-estimator Estimating equations Estimation of covariance matrices Estimation theory Estimator McKay's approximation for the coefficient of variation Law of large numbers Likelihood function Markov chain central limit theorem Maximum likelihood estimation Mean Multivariate normal distribution Empirical mean

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