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The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
The PEG ratio's validity is particularly questionable when used to compare companies expecting high growth with those expecting low-growth, or to compare companies with high P/E with those with a low P/E. It is more apt to be considered when comparing so-called growth companies (those growing earnings significantly faster than the market).
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of a parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...
The cyclically adjusted price-to-earnings ratio, commonly known as CAPE, [1] Shiller P/E, or P/E 10 ratio, [2] is a stock valuation measure usually applied to the US S&P 500 equity market. It is defined as price divided by the average of ten years of earnings ( moving average ), adjusted for inflation. [ 3 ]
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population and statisticians attempt to collect ...
[16]: 45 [17] For example, these methods can be used to make the sample more similar to some target "controls" (i.e., population of interest), a process also called "standardization". [ 9 ] : 187 In such cases, these adjustments help with providing unbiased estimators (often with the cost of increased variance, as seen in the following sections).
The sample covariance matrix has in the denominator rather than due to a variant of Bessel's correction: In short, the sample covariance relies on the difference between each observation and the sample mean, but the sample mean is slightly correlated with each observation since it is defined in terms of all observations.
The basic idea of importance sampling is to sample the states from a different distribution to lower the variance of the estimation of E[X;P], or when sampling from P is difficult. This is accomplished by first choosing a random variable L ≥ 0 {\displaystyle L\geq 0} such that E [ L ; P ] = 1 and that P - almost everywhere L ( ω ) ≠ 0 ...