NegativeBinomialResults.predict()
  • References/Python/Statsmodels/Regression with Discrete Dependent Variable

statsmodels.discrete.discrete_model.NegativeBinomialResults.predict

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GLM.score_test()
  • References/Python/Statsmodels/Generalized Linear Models

statsmodels.genmod.generalized_linear_model.GLM.score_test

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sandbox.regression.gmm.IVGMM()
  • References/Python/Statsmodels/Generalized Method of Moments

statsmodels.sandbox.regression.gmm.IVGMM class statsmodels.sandbox

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TLinearModel.predict()
  • References/Python/Statsmodels/Other Models

statsmodels.miscmodels.tmodel.TLinearModel.predict

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stats.power.NormalIndPower()
  • References/Python/Statsmodels/Statistics

statsmodels.stats.power.NormalIndPower class statsmodels.stats.power.

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genmod.generalized_linear_model.GLMResults()
  • References/Python/Statsmodels/Generalized Linear Models

statsmodels.genmod.generalized_linear_model.GLMResults

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RLM.loglike()
  • References/Python/Statsmodels/Robust Linear Models

statsmodels.robust.robust_linear_model.RLM.loglike

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sandbox.stats.runs.runstest_1samp()
  • References/Python/Statsmodels/Statistics

statsmodels.sandbox.stats.runs.runstest_1samp statsmodels

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static RLMResults.bse()
  • References/Python/Statsmodels/Robust Linear Models

statsmodels.robust.robust_linear_model.RLMResults.bse

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Summary.add_array()
  • References/Python/Statsmodels/Input-Output

statsmodels.iolib.summary2.Summary.add_array Summary.add_array(array

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