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Theoretical and technical comments on regression and anti-regression
Abstract:
Following a discussion of the 'classical' formulations on regression and fixation, the view is put forward that the distinction between various types of regression is on the whole superfluous. However, it is important to distinguish between 'latent' regression in the patient's 'present unconscious', the contents of which are defended against and kept from consciousness on the one hand, and 'manifest' regression on the other, in which current versions of developmentally earlier wishes and fantasies emerge overtly in acceptable form in thought and behaviour. The anti-regressive function can be regarded as being operative from early in development, and functions to a greater or lesser extent throughout our daily lives. As a concept it reflects the operation of all the defence mechanisms and manoeuvres which operate to protect consciousness and conscious awareness of behaviour. Because it reflects a general tendency of the mind to defend consciousness from unwelcome content arising in the present unconscious, it is consequently a major source of resistance in analysis. Analytic progress takes place when the patient gains sufficient security in an analysis-syntonic way to allow a progressively increasing capacity for controlled relaxation of the anti-regressive function to take place, and for previously unacceptable wishful phantasies to be tolerated and accepted by the patient. This relaxation of the anti-regressive function is allied to Ernst Kris's description of 'regression in the service of the ego'.
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Regression Toward the Mean
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...