About: Sample mean and covariance

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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

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rdf:type	Thing football league season
rdfs:label	표본 평균 (ko) Sample mean and covariance (en) Média e covariância amostrais (pt)
rdfs:comment	통계학에서 표본 평균(標本平均, sample mean) 또는 '표본 평균과 표본 분산'(sample mean and sample covariance)은 표본의 평균과 이에 대한 분산 값이다. (ko) 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) A média amostral ou média empírica e a covariância amostral são cálculos de estatística feitos a partir de uma coleta de dados em uma ou mais variáveis aleatórias. A média amostral é um vetor onde cada um dos elementos é a média da amostra de uma das variáveis aleatórias, ou seja, cada um dos elementos é a média aritmética dos valores observados de uma das variáveis. A matriz da covariância de amostra é quadrada, cujo i, j elemento é a covariância da amostra (uma estimativa da covariância da população ) entre os conjuntos de valores observados de duas das variáveis e cujo i, i elemento é a variância da amostra de valores observados de uma das variáveis . Se apenas uma variável teve valores observados , em seguida, a média da amostra é um único número (a média aritmética dos valores o (pt)
rdfs:seeAlso	Sample variance
dcterms:subject	Covariance and correlation Matrices Summary statistics U-statistics Estimation methods
Wikipage page ID	10005756 (xsd:integer)
Wikipage revision ID	1063504049 (xsd:integer)
Link from a Wikipage to another Wikipage	Probability distribution Quantile Sample median Bessel's correction Bias of an estimator Variance Vector (mathematics and physics) Interquartile range Correlation Covariance Covariance and correlation Matrix (mathematics) Mean Estimation of covariance matrices Estimator Pseudoreplication Multivariate random variable Statistical population Matrices Central limit theorem Summary statistics Winsorising Location Normal distribution Normalizing constant Fortune 500 Statistical dispersion Gaussian distribution Random variable Realization (probability) Covariance matrix U-statistics Estimation methods Trimmed estimator Trimmed mean Weighted mean Winsorized mean Average Positive semi-definite matrix Independence (probability theory) Random variables Maximum likelihood Sample (statistics) Standard error Outliers Robust statistics Statistic Scatter matrix Unbiased estimation of standard deviation Random vector Unbiased estimator
sameAs	Sample mean and covariance Sample mean and covariance Sample mean and covariance Sample mean and covariance 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
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) 통계학에서 표본 평균(標本平均, sample mean) 또는 '표본 평균과 표본 분산'(sample mean and sample covariance)은 표본의 평균과 이에 대한 분산 값이다. (ko) A média amostral ou média empírica e a covariância amostral são cálculos de estatística feitos a partir de uma coleta de dados em uma ou mais variáveis aleatórias. A média amostral é um vetor onde cada um dos elementos é a média da amostra de uma das variáveis aleatórias, ou seja, cada um dos elementos é a média aritmética dos valores observados de uma das variáveis. A matriz da covariância de amostra é quadrada, cujo i, j elemento é a covariância da amostra (uma estimativa da covariância da população ) entre os conjuntos de valores observados de duas das variáveis e cujo i, i elemento é a variância da amostra de valores observados de uma das variáveis . Se apenas uma variável teve valores observados , em seguida, a média da amostra é um único número (a média aritmética dos valores observados para a variável) e a matriz da covariância de amostra é simplesmente um único valor (a variância da amostra de valores observados de que variável). (pt)
gold:hypernym	Statistics
prov:wasDerivedFrom	wikipedia-en:Sample_mean_and_covariance?oldid=1063504049&ns=0
page length (characters) of wiki page	13590 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Sample_mean_and_covariance
is Link from a Wikipage to another Wikipage of	Price equation Sample mean and sample covariance Decile Estimation of covariance matrices Glossary of probability and statistics Arithmetic mean Simple linear regression Sample mean Empirical mean List of statistics articles Resampled efficient frontier

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