B.Linear regression is an estimation of linear relationship between two or more variables. A linear equation is fitted into the observed data. This line consists of one dependent variable and one or more than one independent variables. In models, wherein there is only one independent variable it is regarded as simple linear regression and when there are more than one independent variables it is regarded as multiple linear regression.
According to the concept of regression fallacy, the results obtained or the relationship established is just due to the corrective actions .and no abnormalities. The concept of fallacy ignores the natural fluctuations that may also occur in variables over time.
A model is a good fit when it is able to fir the sample data provided. Both Standard Error of Regression and R squared statistic explains the goodness of fit of the model. However, there is a slight difference between these two statistics. SER explains the exact distance between the regression line and data points. Whereas, the R squared value explains the relative change caused in the dependent variable due to the collective change in the independent variable.
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