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Why is it important to have independent and dependent variables?
Dependent and independent variables are important because they drive the research process. Dependent and independent variables are also important because they determine the cause and effects in research.
Why do we transform variables into logs?
When our original continuous data do not follow the bell curve, we can log transform this data to make it as “normal” as possible so that the statistical analysis results from this data become more valid . In other words, the log transformation reduces or removes the skewness of our original data.
Do we need to scale the target variable?
Yes, you do need to scale the target variable. I will quote this reference: A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable.
What happens when dependent variables are log transformed?
Our QQ plot also shows our residual normality improved. As you probably guessed, our interpretation of the coefficients has changed again. When both independent and dependent variables are log transformed, the coefficient represents the % change in y for a 1% change in x.
How to interpret log transformations in a linear model?
OK, you ran a regression/fit a linear model and some of your variables are log-transformed. Only the dependent/response variable is log-transformed. Exponentiate the coefficient, subtract one from this number, and multiply by 100. This gives the percent increase (or decrease) in the response for every one-unit increase in the independent variable.
When is it appropriate to use the log of an independent variable?
In linear regression, when is it appropriate to use the log of an independent variable instead of the actual values? Am I looking for a better behaved distribution for the independent variable in question, or to reduce the effect of outliers, or something else?
What happens to mpg with a log transformation?
As you probably guessed, our interpretation of the coefficients has changed again. When both independent and dependent variables are log transformed, the coefficient represents the % change in y for a 1% change in x. In our model, this means mpg decreases by .55% when displacement changes by 1%.