9 мар. 2022 г. · Linear regression does not assume normal distribution for the dependent variable, but conditional normality. See the linked thread. – Tim.

30 июн. 2014 г. · The assumption about the conditional distribution is equivalent to an assumption of normal errors in the model.

9 нояб. 2013 г. · You don't need to assume Normal distributions to do regression. Least squares regression is the BLUE estimator (Best Linear, Unbiased Estimator) regardless of ...

12 окт. 2021 г. · I want to know whether I can do simple linear regression, with one X variable and one Y variable if the data itself is non-linear (both X and Y) to make ...

26 авг. 2020 г. · In simple linear regression with only one variable in the model, this implies that the independent variable must also be normally distributed.

9 мар. 2023 г. · For the dependent (y) variable, normality can matter, but that is for the error term or conditional distribution, not of the distribution of all ...

24 мая 2014 г. · This is quite easy. You just need to decide what distribution you want your errors to have, and use the corresponding random generation function.

1 мая 2017 г. · I have a continuous dependent variable that is non-normally distributed, skewed to the right (long right tail). I have fit two OLS regression models to this ...

25 апр. 2018 г. · The dependent variable Yi does NOT need to be normally distributed, but it typically assumes a distribution from an exponential family (e.g. ...

4 янв. 2017 г. · If you want to compare the performance of a ratio model with zero-inflated count or hurdle models then a tobit model approach isn't a bad idea.