The term "Logistic regression" is referring to the graph of analysis in predictions. There are variables involved and explain probabilities that are a hypothesis of the dependent variable, which is the one being applied to a future prediction.
Ah, the stochastic error term and the residual are like happy little clouds in our painting. The stochastic error term represents the random variability in our data that we can't explain, while the residual is the difference between the observed value and the predicted value by our model. Both are important in understanding and improving our models, just like adding details to our beautiful landscape.
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There is no such term. The regression (or correlation) coefficient changes as the sample size increases - towards its "true" value. There is no measure of association that is independent of sample size.
LCM is an abbreviation for "least common multiple."
Regression analysis is based on the assumption that the dependent variable is distributed according some function of the independent variables together with independent identically distributed random errors. If the error terms were not stochastic then some of the properties of the regression analysis are not valid.
The term regression means to take back. To regress you take away something not in a physical form. For example in old age pensioners can often suffer from memory regression where they are mentally taken back to times past.
regression
Although not everyone follows this naming convention, multiple regression typically refers to regression models with a single dependent variable and two or more predictor variables. In multivariate regression, by contrast, there are multiple dependent variables, and any number of predictors. Using this naming convention, some people further distinguish "multivariate multiple regression," a term which makes explicit that there are two or more dependent variables as well as two or more independent variables.In short, multiple regression is by far the more familiar form, although logically and computationally the two forms are extremely similar.Multivariate regression is most useful for more special problems such as compound tests of coefficients. For example, you might want to know if SAT scores have the same predictive power for a student's grades in the second semester of college as they do in the first. One option would be to run two separate simple regressions and eyeball the results to see if the coefficients look similar. But if you want a formal probability test of whether the relationship differs, you could run it instead as a multivariate regression analysis. The coefficient estimates will be the same, but you will be able to directly test for their equality or other properties of interest.In practical terms, the way you produce a multivariate analysis using statistical software is always at least a little different from multiple regression. In some packages you can use the same commands for both but with different options; but in a number of packages you use completely different commands to obtain a multivariate analysis.A final note is that the term "multivariate regression" is sometimes confused with nonlinear regression; in other words, the regression flavors besides Ordinary Least Squares (OLS) linear regression. Those forms are more accurately called nonlinear or generalized linear models because there is nothing distinctively "multivariate" about them in the sense described above. Some of them have commonly used multivariate forms, too, but these are often called "multinomial" regressions in the case of models for categorical dependent variables.
A Stochastic error term is a term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs. It is, in effect, a symbol of the econometrician's ignorance or inability to model all the movements of the dependent variable.
correlation, or regression
The term "Logistic regression" is referring to the graph of analysis in predictions. There are variables involved and explain probabilities that are a hypothesis of the dependent variable, which is the one being applied to a future prediction.
regression
The term 'synergize' refers to the interaction of multiple elements in a system to product an effect that would be greater than the individual effects of the elements.
regression
Econometric models are also called regression models.
The opposite of regression is progression, although in mathematics, progression has a special meaning. Quote Mariam-Webster -Progression1 : a sequence of numbers in which each term is related to its predecessor by a uniform law