R-squared value financial definition of R-squared value
correlation (redirected from R-squared value)
Also found in: Dictionary
The relationship between two variables during a period of time, especially one that shows a close match between the variables' movements. For example, all utility stocks tend to have a high degree of correlation because their share prices are influenced by the same forces. Conversely, gold stock price movements are not closely correlated with utility stock price movements because the two are influenced by very different factors. The concept of correlation is frequently used in portfolio analysis. See also serial correlation
In investment terms, correlation is the extent to which the values of different types of investments move in tandem with one another in response to changing economic and market conditions.
Correlation is measured on a scale of - 1 to +1. Investments with a correlation of + 0.5 or more tend to rise and fall in value at the same time. Investments with a negative correlation of - 0.5 to - 1 are more likely to gain or lose value in opposing cycles.
correlation a statistical term that describes the degree of association between two variables. When two variables tend to change together, then they are said to be correlated, and the extent to which they are correlated is measured by means of the CORRELATION COEFFICIENT.
correlationA former appraisal term, replaced by reconciliation.
References in periodicals archive
for the load-displacement curves from experiment and the simulations adopting different stress-strain curves.
The R-squared value
is not very high for the model (60%) due to characteristics of the sample however; regression model is highly significant (Probability .
A trendline is the most reliable when its R-squared value
is at or near 1.
a, b Values of Exponential Models Filters a b R-squared Value
V-pack Filter 1 79.
In each case, third order polynomial trend lines were used to fit the data, and the R-squared value
for the fit is indicated on the graphs.
Efficient frontiers for each of the six remaining dependent variables are developed by graphing the adjusted R-squared value
versus the number of variable terms for each remaining candidate model.
The regression models have been fitted by the best R-squared value