sample correlation造句

• (See Correlation # Sample correlation for the formula for r ).
• See sample correlation coefficient for additional details.
• The sample correlation coefficient is written:
• A corresponding result exists for sample correlations, in which the sample correlation is reduced to zero.
• A corresponding result exists for sample correlations, in which the sample correlation is reduced to zero.
• Where is the sample correlation coefficient between and; and and are the sample standard deviation of and.
• In this case, you can " create " a statistical test based on the sample correlation.
• Thus, the sample correlation coefficient between the observed and fitted response values in the regression can be written ( calculation is under expectation, assumes Gaussian statistics)
• Examples of variance-stabilizing transformations are the Fisher transformation for the sample correlation coefficient, the square root transformation or Anscombe transform for type-II error.
• In other words, if linear regression is the appropriate model for a set of data points whose sample correlation coefficient is not perfect, then there is regression toward the mean.
• And the sample "'partial "'correlation is then given by the usual formula for sample correlation, but between these new " derived " values.
• These empirical sample correlation matrices are the most straightforward and most often used estimators for the correlation matrices, but other estimators also exist, including regularised or shrinkage estimators, which may have better properties.
• The Pearson product-moment correlation coefficient is " not " a statistical test by itself-it is a measure of the sample correlation between a set of simultaneous measurements of two random variables.
• Under certain assumptions about the distribution of those random variables, it may also be a good estimate of the population correlation, and you can calculate the distribution of the population correlation given that you have a sample with a particular sample correlation.
• If you frame your null hypothesis precisely, so that it says " the population correlation is between " r " 1 and " r " 2 " or " the population correlation is at least " r " 1 ", then you can calculate the probability that the null hypothesis is true, given the observed sample correlation.