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Can you do regression analysis with non normal data?
Non-normality in the predictors MAY create a nonlinear relationship between them and the y, but that is a separate issue. You have a lot of skew which will likely produce heterogeneity of variance which is the bigger problem.
What to do when data has a non-normal distribution?
If the data appear to have non-normally distributed random errors, but do have a constant standard deviation, you can always fit models to several sets of transformed data and then check to see which transformation appears to produce the most normally distributed residuals.
How is utility based regression used in data mining?
Utility-based regression (UBR) is one of the rare techniques that work efficiently with imbalanced or non-uniform weights overall data. This technique is originally used in the data mining field. The basic idea of the UBR is to add a cost penalty for the wrong estimate, which varies based on the relevance function.
Is the y-variable normally distributed in regression?
I suppose that is the origin of people thinking that the y-variable should be normally distributed as an assumption for regression, but the y-data distribution in regression is not conditional, and that is not true. It is desirable for estimated residuals to be normally distributed, though even that is not a very strict requirement.
Which is the best description of nonlinear regression?
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form,
What does linear regression with only categorical dependent variables look like?
A linear regression with only categorical dependent variables is called ANOVA. This website explains pretty clearly what the binary variables look like. Essentially, you will have a new variable for each level of your factor (e.g. White Flour). The new variable will take the value 1 if that recipe used white flour and 0 if it did not.
When do we assume that data are independent?
Regression and correlation assume that observations are independent. If one of the measurement variables is time, or if the two variables are measured at different times, the data are often non-independent.