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Learn how to choose the number of observations or replicates in a statistical sample based on various factors, such as confidence level, margin of error, and variability. Find formulas and examples for estimating proportions, means, and variances.
Learn the definition, formula, and applications of standard error, a measure of the dispersion of sample means around the population mean. Find out how to estimate ...
Ordinary least squares (OLS) is a method of estimating parameters in a linear regression model by minimizing the sum of squared residuals. Learn the formula, properties, assumptions, and applications of OLS in statistics and econometrics.
where n is the sample size and N is the population size and s xy is the ... The sample estimate was 71,866.333 baptisms per year over this period giving a ratio of ...
A plot of the Kaplan–Meier estimator is a series of declining horizontal steps which, with a large enough sample size, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations ("clicks") is assumed to be constant.
An estimator is a rule for calculating an estimate of a given quantity based on observed data. Learn about the different types, properties and applications of estimators in statistics and decision theory.
Learn how to estimate parameters of a probability distribution using the method of maximum likelihood estimation (MLE), which maximizes the likelihood function of the observed data. Find out the principles, properties, and applications of MLE, as well as its relation to Bayesian and frequentist inference.
A consistent estimator is a rule for computing estimates of a parameter that converges to the true value as the sample size grows. Learn the definition, examples, and methods of proving consistency for different types of estimators.