CorrelationIn statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.
Classical conditioningClassical conditioning (also respondent conditioning and Pavlovian conditioning) is a behavioral procedure in which a biologically potent physiological stimulus (e.g. food) is paired with a neutral stimulus (e.g. the sound of a musical triangle). The term classical conditioning refers to the process of an automatic, conditioned response that is paired with a specific stimulus. The Russian physiologist Ivan Pavlov studied classical conditioning with detailed experiments with dogs, and published the experimental results in 1897.
Pearson correlation coefficientIn statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.
Correlation does not imply causationThe phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc ('with this, therefore because of this').
Extinction (psychology)Extinction is a behavioral phenomenon observed in both operantly conditioned and classically conditioned behavior, which manifests itself by fading of non-reinforced conditioned response over time. When operant behavior that has been previously reinforced no longer produces reinforcing consequences the behavior gradually stops occurring. In classical conditioning, when a conditioned stimulus is presented alone, so that it no longer predicts the coming of the unconditioned stimulus, conditioned responding gradually stops.
Stimulus (psychology)In psychology, a stimulus is any object or event that elicits a sensory or behavioral response in an organism. In this context, a distinction is made between the distal stimulus (the external, perceived object) and the proximal stimulus (the stimulation of sensory organs). In perceptual psychology, a stimulus is an energy change (e.g., light or sound) which is registered by the senses (e.g., vision, hearing, taste, etc.) and constitutes the basis for perception. In behavioral psychology (i.e.
Spurious relationshipIn statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable", "confounding factor", or "lurking variable"). An example of a spurious relationship can be found in the time-series literature, where a spurious regression is a one that provides misleading statistical evidence of a linear relationship between independent non-stationary variables.
Correlation coefficientA correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible agreement and 0 the strongest possible disagreement.
Coefficient of multiple correlationIn statistics, the coefficient of multiple correlation is a measure of how well a given variable can be predicted using a linear function of a set of other variables. It is the correlation between the variable's values and the best predictions that can be computed linearly from the predictive variables. The coefficient of multiple correlation takes values between 0 and 1.
Partial correlationIn probability theory and statistics, partial correlation measures the degree of association between two random variables, with the effect of a set of controlling random variables removed. When determining the numerical relationship between two variables of interest, using their correlation coefficient will give misleading results if there is another confounding variable that is numerically related to both variables of interest. This misleading information can be avoided by controlling for the confounding variable, which is done by computing the partial correlation coefficient.
