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statsmodels

by davila7

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
davila7
Category
Data Science
Views
15
GitHub repo

About this skill

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

How to use

  1. Zainstaluj bibliotekę statsmodels w swoim środowisku Python (np. pip install statsmodels). 2. Przygotuj dane: załaduj zmienną zależną (y) i zmienne niezależne (X) jako tablice NumPy lub DataFrame Pandas. Pamiętaj, aby dodać kolumnę stałej (intercept) za pomocą sm.add_constant(X), inaczej model nie będzie miał wyrazu wolnego. 3. Utwórz model OLS (zwykłą regresję liniową) poleceniem sm.OLS(y, X) i dopasuj go metodą fit(). 4. Przejrzyj wyniki: wydrukuj results.summary() aby zobaczyć pełny raport zawierający R-squared, współczynniki, p-wartości i testy diagnostyczne. 5. Wygeneruj prognozy dla nowych danych za pomocą results.get_prediction(X_new) — otrzymasz przedziały ufności i przedziały predykcji. 6. Przeprowadź diagnostykę modelu: sprawdź heteroskedastyczność (test Breuscha-Pagana), autokorelację i normalność reszt, aby zweryfikować założenia modelu.

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