스킬 statsmodels
📊
OLS, GLM, ARIMA 및 이산 선택 모델을 사용하여 엄격한 통계 분석을 수행합니다. 전체 진단, 계수 테이블 및 잔차 분석과 함께 출판 준비가 완료된 결과를 얻으세요.
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테스트해 보기
"statsmodels" 사용 중입니다. 강건한 표준 오차와 함께 OLS 모델을 피팅하고 주요 진단을 보여줍니다
예상 결과:
- OLS 회귀 결과:
- R-squared: 0.452
- 계수 (X1): 2.31 (p < 0.001)
- 강건한 HC3 SE: 0.42
- 이분상성 테스트 (Breusch-Pagan): p = 0.23 (기각되지 않음)
- 정규성 테스트 (Jarque-Bera): p = 0.41 (기각되지 않음)
보안 감사
안전v4 • 1/17/2026
Documentation-only skill containing markdown files with Python code examples. Static scanner flagged 434 alerts but all are false positives. The skill contains no executable code - only documentation for the statsmodels statistical library. Scanner misinterpreted markdown backticks as shell commands, statistical terms (HC2, HC3) as C2 indicators, and common patterns as cryptographic algorithms.
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⚡ 스크립트 포함 (8)
⚙️ 외부 명령어 (353)
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🌐 네트워크 접근 (5)
감사자: claude 감사 이력 보기 →
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사양 준수
만들 수 있는 것
회귀 분석
강건한 표준 오차와 진단 테스트를 사용하여 선형 및 일반화 선형 모델 피팅
계량경제 모델링
적절한 추론, 가설 테스트 및 모델 비교를 사용하여 인과 효과 추정
시계열 예측
신뢰 구간 및 잔차 진단과 함께 예측을 위한 ARIMA 모델 구축
이 프롬프트를 사용해 보세요
기본 회귀
강건한 표준 오차를 사용하여 statsmodels로 OLS 회귀 모델 피팅합니다. 계수 요약을 보여주고 이분상성을 테스트하세요.
로지스틱 회귀
이진 결과에 대한 로지스틱 회귀 모델을 구축합니다. 승산비, 한계 효과 및 분류 지표를 계산하세요.
시계열
ARIMA로 이 시계열 데이터를 분석합니다. 정상성을 확인하고, 모델 차수를 식별하고, 모델을 피팅하며, 12단계 예측과 예측 구간을 생성합니다.
모델 비교
AIC, BIC 및 우도비 검정을 사용하여 여러 모델 사양을 비교합니다. 정당화와 함께 최선의 모델을 추천합니다.
모범 사례
- 상수를 의도적으로 제외하지 않는 한 항상 sm.add_constant()로 상수를 추가하세요
- 피팅 후 모델 가정(이분상성, 정규성, 자기상관)을 테스트하세요
- 가정 위반이 감지되면 강건한 표준 오차(HC0-HC3)를 사용하세요
- 추론을 위해 점 추정값과 함께 신뢰 구간을 보고하세요
피하기
- 상수 추가를 잊으면 절편 추정이 편향됩니다
- 이분상성을 무시하면 추론이 무효화됩니다
- 이진 결과에 OLS를 사용하면 확률이 잘못 계산됩니다
- 해석 전에 영향력 있는 관찰을 확인하지 않습니다
자주 묻는 질문
OLS와 GLM의 차이점은 무엇입니까?
OLS는 일정한 분산을 가진 정규 분포 오차를 가정합니다. GLM은 서로 다른 분산과 연결 함수를 사용하여 비정상적인 결과로 이를 확장합니다.
로짓과 프로빗 중 어떻게 선택합니까?
둘 다 이진 결과에 유사하게 작동합니다. 로지스틱은 꼬리가 더 두텁습니다. 해석이更容易(승산비)하려면 로짓을, 이론적 이유가 있으면 프로빗을 사용하세요.
ARIMA에 어떤 차수를 사용해야 합니까?
ACF/PACF 플롯을 사용하여 p(AR 차수)와 q(MA 차수)를 식별합니다. ADF 테스트를 사용한 정상성 검정으로 d(차분)를 결정합니다.
강건한 표준 오차를 언제 사용해야 합니까?
이분상성이 있거나 데이터에 clustering이 있어 상관관계가 유도될 때 강건한 SE를 사용하세요.
중첩된 모델을 어떻게 비교합니까?
우도비 검정을 사용하세요. 중첩되지 않은 모델의 경우 AIC 또는 BIC 값을 비교합니다. 더 낮은 AIC/BIC가 더 나은 적합도를 나타냅니다.
피팅 후 어떤 진단 테스트를 실행해야 합니까?
이분상성(Breusch-Pagan), 정규성(Jarque-Bera), 자기상관(Durbin-Watson)을 테스트하고 영향력 있는 관찰(Cook's 거리)을 확인하세요.
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K-Dense-AI라이선스
BSD-3-Clause license
리포지토리
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/statsmodels참조
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