'statsmodels' 검색 결과

Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.

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• Fit regression models and interpret coefficients
• Compare models for binary outcomes or counts
• Forecast time series with prediction intervals

Apply statsmodels in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statsmodels or its strengths fit the task.

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Fit regression and time series models in Python, then examine coefficients, uncertainty, and predictions. Check model assumptions, investigate unusual observations, and compare alternatives using detailed statistical summaries and diagnostic tests.

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• Interpret regression coefficients and confidence intervals
• Model binary outcomes or event counts
• Forecast time series with uncertainty intervals

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

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• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Analyze research data with guidance on choosing statistical tests, checking their assumptions, and measuring effect sizes, meaning the magnitude of differences or relationships. It also supports sample size planning, Bayesian analysis, and APA-style reports.

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• Comparing groups in experimental or survey data
• Choosing tests and checking their assumptions
• Planning the required study sample size

予測モデリング（教師あり機械学習 / 予測モデル / 精度評価 / 交差検証 / リーク、決定木 / ランダムフォレスト / GBDT / LightGBM / XGBoost / CatBoost、モデル解釈 / 特徴量重要度 / SHAP / PDP / ICE、時系列 / 季節性 / ARIMA / SARIMA / Prophet / VAR / 変化点 / 時系列予測）を実行したら必ずセットで出す図と値のルーター。train_test_split, cross_val_score, GridSearchCV, Pipeline, TimeSeriesSplit, DecisionTree, RandomForest, lgb., xgb., CatBoost, dtreeviz, shap., permutation_importance, PartialDependenceDisplay, statsmodels.tsa, SARIMAX, STL, adfuller, plot_acf, Prophet, ruptures がコードに現れたとき、または ユーザーが「予測モデルを作って」「精度を出して」「どの変数が効いているか」「来月を予測して」「季節性を見て」と 言ったときに使う。診断・リーク・ベースライン・残差に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、 対応する references/<手法>.md を読んでから実行する。回帰係数の推論・検定は statistical-inference-diagnostics、 効果推定は causal-inference-diagnostics を使う。

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因果推論・効果検証（A/B テスト / ABテスト / オンライン実験 / リフト / SRM / CUPED、観察データの処置効果 / ATE / ATT / CATE / 傾向スコア / propensity score / IPW / 二重頑健 / DML / causal forest、準実験 / 差の差 / DiD / イベントスタディ / 操作変数 / IV / 2SLS / 回帰不連続 / RDD / 合成コントロール）を実行したら必ずセットで出す図と値のルーター。 proportions_ztest, chisquare, statsmodels.stats.power, dowhy, econml, causalml, doubleml, propensity, LinearDML, IV2SLS, rdrobust, rddensity, pysyncon, treat*post がコードに現れたとき、またはユーザーが「ABテストの結果を 見て」「施策の効果を推定して」「差の差で」「傾向スコアで揃えて」「閾値前後で比較して」と言ったときに使う。SRM・ バランス・並行トレンド・first-stage に言及がなくても適用する。SKILL.md のルーティング表で設計を特定し、対応する references/<設計>.md を読んでから実行する。実験でない群間比較の検定は statistical-inference-diagnostics、 予測モデルの解釈（SHAP 等）は predictive-modeling-diagnostics を使う。

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Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

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Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.

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Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

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• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.

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• Estimating participant or sample counts before data collection
• Finding the smallest effect detectable with a fixed sample
• Preparing sample size justifications for grants or protocols

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

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• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.

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• Check whether price and return series are statistically stable
• Test long-term relationships between two assets
• Diagnose regression problems in factor research

Analyze how assets move together and test cointegration, a long-term equilibrium relationship between prices. Find related assets, group similar holdings, examine changing correlations, and estimate hedge ratios and reversion times for pairs-trading research.

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• Find assets that move with a target holding
• Group similar assets to review diversification
• Test long-term price relationships for pairs trading

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

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• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Plan quantitative research by comparing experiments and surveys against research goals and resource limits. Get design recommendations, sample size calculations, participant recruitment strategies, and experimental materials, with an assessment of factors that could distort results.

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• Compare experimental and survey designs
• Estimate the number of participants needed
• Plan participant sampling and recruitment

Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.

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• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures

Guide economic time series analysis from initial plots to forecasting. Use Python, R, and Stata examples to check whether statistical properties remain stable over time, choose models, diagnose errors, and examine relationships between multiple series.

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• Checking trends and stationarity in economic data
• Building ARIMA forecasts and evaluating accuracy
• Examining relationships between multiple time series

Turn regression results and summary statistics from Stata, R, or Python into LaTeX tables for academic papers. Format model comparisons, decimal places, statistical significance markers, and notes, with guidance for wide tables and inclusion in a paper.

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• Comparing several regression models in one table
• Formatting summary statistics for a paper
• Combining results into a table with multiple panels

Plan, run, and interpret linear regression with guidance on model choices, diagnostic checks, and reporting. Python, R, and Stata examples help examine coefficients, assess assumptions, and choose standard errors that account for different error patterns.

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• Specify a regression model for an empirical study
• Check residuals and correlations among explanatory variables
• Choose robust or clustered standard errors

Run linear or logistic regression on CSV or Excel tables to examine relationships between variables. Get coefficients, measures of model fit, significance tests, and checks for overlapping predictors, with explanations in plain language.

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• Examining relationships between sales and numeric factors
• Analyzing factors associated with customer churn
• Checking coefficient significance and model fit

Estimate study sample sizes and statistical power, the chance of detecting a specified effect. Calculate detectable effects for a fixed sample, compare assumptions with power curves, and document adjustments for dropout, multiple comparisons, or clustered observations.

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• Estimate participant or sample counts before data collection
• Find the smallest effect detectable with a fixed sample
• Compare sample requirements across plausible effect sizes

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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