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An overview of the techniques of modeling and analyzing multiple variables used in the regression an

Linear Regression is very sensitive to outliers. This is connected to the previous point where Lasso performs a type of feature selection.

Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functionswhich may be infinite-dimensional.

All of these regression regularization methods Lasso, Ridge and ElasticNet work well in case of high dimensionality and multicollinearity among the variables in the data set. In this regression technique, the best fit line is not a straight line.

Linear Regression Regression is a technique used to model and analyze the relationships between variables and often times how they contribute and are related to producing a particular outcome together.

The plot shows that the points generally follow the normal diagonal line with no strong deviations. However this can lead to illusions or false relationships, so caution is advisable. To do this, we can check scatter plots.

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The Multiple Linear Regression Analysis in SPSS