Im not sure why you would want to? Linear regression is for continuous dependent variables, while logistic regression is for binary.
Dear Imrus Salehin
These articles might be helpful, have a look:
1. https://www.analyticsvidhya.com/blog/2020/12/beginners-take-how-logistic-regression-is-related-to-linear-regression/
2. https://www.analyticssteps.com/blogs/how-does-linear-and-logistic-regression-work-machine-learning
Kind Regards
Qamar Ul Islam
I'm sorry but logistic regression is a form of linear statistical models analysis. See Kutner et al introduction to linear statistical models available in the z-library google instructions for it's use. Since they are a subset of the class of linear statistical models you really are always doing what you want to do. Best wishes, David Booth
Technically, even though not recommended, you could use linear regression (LIN) for a binary Dependent Variable (DV), but you CANNOT use logistic regression (LOG) for a continuous DV. The main problem with LIN when the DV is binary is that you could well get a regression which gives predicted values outside of the (0,1) range of the DV!
When dealing with regression problems, linear regression is employed, and when dealing with classification problems, logistic regression is employed. So based on problem description , you can user either linear or logistic regression.
it is depended in your data if depended is conations that you use Linear regression while as your depend is binary , you have to use logistic regression. Both are not used the same.
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