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Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.
Despite the fact that regression can be used for both causal inference and prediction, it turns out that there are some important differences in how the methodology is used, or should be used, in the two kinds of application.echnically, the more important criterion is the standard error of prediction, which depends both on the R 2 and the variance of y in the population. In any case, large sample sizes cannot compensate for models that are lacking in predictive power. 3. Multicollinearity.