Category : analysis

I am working on a credit scoring model, and was detecting and removing outliers but for certain variables such as age, number of major reports, number of credit inquiries. Im not sure if i should remove the outliers for these variables. Should outliers for count and age variables be removed? Source: Python..

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I run the OLS both from the statsmodels and linearmodels.iv as follows. They are supposed to be the same but the results are VERY different. Can you please tell me what you think of the reason? `dep=[‘y’] exog=[‘a’, ‘b’, ‘c’] endog=[‘d’] instr=[‘e’] res_ols = IV2SLS(data.y, data[exog + endog], None, None).fit(cov_type = "clustered", clusters=data[‘member_id’]) print(res_ols) model1 ..

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I have this kind of data, which contain Timestamp, longitude,latitude and tripId, can i probably find the waiting time in intersection from only this data or i need something else? and which informations can i get from this kind of data? "timestamp","tripId","longitude","latitude" "2021-07-05 10:35:04","1866491","8.167035","53.160473" "2021-07-05 10:35:03","1866491","8.167023","53.160469" "2021-07-05 10:35:02","1866491","8.167007","53.160459" "2021-07-05 10:35:01","1866491","8.166987","53.160455" "2021-07-05 10:35:00","1866491","8.166956","53.160448" "2021-07-05 10:34:20","1866491","8.167286","53.15919" "2021-07-05 ..

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