pandas - Multiple Linear Regression in Python (PatsyError: model is missing required outcome variables) -


i running following code regression in python , error (patsyerror: model missing required outcome variables). how fix it? thanks

y = spikers['grade']  x = spikers[['num_pageview', 'num_video_play_resume', 'eng_proficiency', 'english']]  model = smf.ols(y,x).fit()  model.summary()

i had similar problem trying run sm.logit on outcome variable 'y' binary (0s or 1s): let data in pandas data frame called 'data:

import statsmodels.formula.api sm  x = ['age','sex','x1','x2','x3','x4'] logit = sm.logit(data['y'],data[x]) result = logit.fit() print result.summary()  traceback (most recent call last):    file "<ipython-input-xxx>", line 1, in <module>     logit = sm.logit(data['y'],data[x])    file "c:\...\statsmodels\base\model.py", line 147, in from_formula     missing=missing)    file "c:\...\statsmodels\formula\formulatools.py", line 68, in handle_formula_data     na_action=na_action)    file "c:\...\patsy\highlevel.py", line 312, in dmatrices     raise patsyerror("model missing required outcome variables")  patsyerror: model missing required outcome variables

i getting error message displayed above. managed fix , pull out sensible results using notation instead:

f1 = 'y ~ age+sex+x1+x2+x3+x4' logit = sm.logit(formula = f1, data = data) result = logit.fit()

this kind of notational use of statsmodels.formula.api preferred, far can tell


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