数据集对比分析专家

已就位。为保证分析高质量与可复现性，请按以下要点提供信息或数据，我将基于您的具体任务进行精准分析与可视化，并给出可执行结论。

请提供
- 业务目标与问题范围：要回答的核心问题/决策场景；关键指标（定义、口径、单位、期望方向）。
- 数据概览：数据源与时间范围；数据粒度（如用户-日、订单-明细）；主键；行数级别；字段清单（字段名、类型、单位、含义、可能取值）。
- 数据样例：建议提供前50行或抽样（可脱敏）；或提供可下载链接/结构化描述（CSV/Parquet/SQL）。
- 约束与期望输出：时限、隐私/合规要求、可用算力；希望的交付（图表类型、模型、报告/Notebook/SQL）。
- 评估标准：成功判据（如相对提升%、统计显著性、误差阈值、业务KPI）。

分析方法与流程（我将按需裁剪执行）
1) 数据准备
- 完整性与一致性：主键唯一性检查；重复与异常记录清理；单位与时区统一；时间对齐与去重。
- 缺失值与异常值：缺失机制诊断（MCAR/MAR/MNAR）；IQR/稳健z-score/时间序列异常检测；合理填补或删失策略。
- 编码与类型：类别高基数处理（合并长尾/目标编码）；数值缩放；日期派生特征（周内/节假日/滞后特征）。

2) 探索性分析与可视化
- 分布与集中趋势：均值/中位数/分位数/变异系数；置信区间。
- 分组对比与异质性：箱线图/小提琴图/条形图+误差线；Aggr与分层可视化。
- 相关性与共线性：Pearson/Spearman/Kendall；VIF；偏相关；热力图。
- 时间序列特性：分解（趋势、季节性、残差）；ADF平稳性检验；滞后/自相关ACF/PACF。

3) 统计检验与因果/实验
- 对比分析：正态/方差齐性检验（Shapiro/Levene）；t检验或Mann-Whitney；多重比较控制（Benjamini-Hochberg）；效应量（Cohen’s d/Cliff’s delta）。
- A/B测试：样本量/功效分析；分层与CUPED；顺序检测与错误率控制；干扰/样本污染排查。
- 观测因果：回归调整、倾向评分匹配/加权；差分中的差分（含平行趋势检验）；断点回归；合成控制（如政策/渠道上线评估）。

4) 预测/分类/时间序列建模
- 回归/分类：基线到梯度提升/正则化线性；避免数据泄漏；K折或留出验证；不平衡处理（权重/重采样）；指标：RMSE/MAE/R2、ROC-AUC/PR-AUC、校准度。
- 时间序列：SARIMAX/ETS/TBATS或基于滞后特征的树模型；节假日/促销外生变量；滚动回测（expanding/rolling origin）；指标：sMAPE、MASE、RMSE。
- 生存/流失：Kaplan-Meier、Cox模型（检验PH假设）；指标：中位生存期、HR、Brier score。

5) 结果验证与解释
- 稳健性：敏感性分析、替代口径、子样本分析、留一法/交叉验证。
- 可解释性：特征重要性（Permutation/SHAP），偏依赖与局部解释；残差诊断。
- 业务落地：可执行建议、风险与假设边界、预估影响区间。

可交付
- 可视化图表：分布/对比/时间趋势/相关性与诊断图（使用颜色盲安全方案，附注释与区间）。
- 可复现工件：分析报告（含方法、假设、限制）；Python/R Notebook；必要SQL；指标字典。
- 决策摘要：关键发现、效应量与不确定性、行动清单与预期影响。

数据模板示例（可直接粘贴）
- 目标：提升转化率（CVR=order_users/visit_users），期望+5%。
- 粒度：用户-日；主键：user_id+date；时间：2024-01-01~2024-06-30。
- 字段：
  user_id(str), date(YYYY-MM-DD), channel(cat), device(cat), visits(int), clicks(int), orders(int), revenue(float, CNY), is_new_user(bin), campaign(cat)
- 备注：节假日口径含法定+调休；iOS收入单位为分需换算为元。
- 输出：分渠道CVR差异分析+显著性；拟合包含渠道/设备/新老用户的Logistic回归；可视化与结论。

请提供上述信息或上传样例数据，我将据此启动分析并给出结构化结论与可视化建议。

Acknowledged. I will respond as a data analysis specialist with precise, context-aware methods across preprocessing, statistical analysis, visualization, and interpretation. To proceed efficiently and maintain accuracy, please share your task or dataset and the following details:

Required inputs
- Objective and key questions: What decisions will this analysis inform? Any hypotheses to test?
- Unit of analysis and granularity: Row meaning (e.g., transaction, user-day), time coverage, sampling frame.
- Target variable(s) and key features: Definitions, data dictionary, expected relationships.
- Data sources and joins: Primary keys, foreign keys, expected constraints, known quality issues.
- Experiment/causal design (if applicable): Randomization, assignment, exposure, inclusion/exclusion criteria.
- Metrics/KPIs: Business metrics to optimize or monitor; success thresholds.
- Context constraints: Regulatory, operational, domain-specific rules.
- Preferred environment: Python/R/SQL; ability to share sample data (schema + 10–50 rows).

Standard analysis workflow
1. Problem framing
   - Define hypotheses, metrics, unit of analysis, cohorts/segments, and time windows.
   - Identify confounders and potential sources of bias.

2. Data audit and preprocessing
   - Validate schema, types, uniqueness, referential integrity, and time coverage.
   - Handle missingness (MCAR/MAR/MNAR) using appropriate strategies: listwise deletion (if MCAR and small), simple imputation (median/mode), multiple imputation (MICE), or model-based imputations; document rationale.
   - Detect and treat outliers using robust statistics (IQR/median absolute deviation) or domain rules; consider winsorization and robust models.
   - Address duplicates, inconsistent labels, and timezone issues; standardize units and encodings.
   - Prevent target leakage by ensuring features are available at prediction time.

3. Exploratory data analysis (EDA)
   - Univariate: distributions, tails, missingness maps.
   - Bivariate/multivariate: correlations (Pearson/Spearman), mutual information, pivoted group summaries, pairwise plots.
   - Temporal: trend/seasonality, stationarity diagnostics (ADF/KPSS), lag features, cohort retention curves.

4. Statistical inference
   - Variable-type and design-appropriate tests:
     - Two-group numeric: t-test (if normal, equal variances) or Welch’s t-test; Mann–Whitney U if non-normal.
     - Multi-group numeric: ANOVA or Kruskal–Wallis; post-hoc Tukey/Holm.
     - Categorical associations: chi-square or Fisher’s exact (small counts).
     - Correlation: Pearson (linear, normal), Spearman (monotonic), partial correlations controlling for confounders.
     - Regression: linear/logistic/Poisson/negative binomial; regularization (L1/L2); robust SEs; check assumptions (linearity, homoscedasticity, multicollinearity, residual diagnostics).
   - Multiple testing control via FDR (Benjamini–Hochberg) or Bonferroni where appropriate.
   - Report effect sizes and confidence intervals, not just p-values.

5. Modeling (if predictive or forecasting)
   - Baselines: simple mean/propensity/logistic baseline; naive or seasonal naive for time series.
   - Feature engineering: domain-driven features, interactions, lags/rolling stats; avoid leakage.
   - Models:
     - Classification: logistic regression, tree-based (RF/GBM), calibrated probabilities (Platt/Isotonic).
     - Regression: linear/regularized, tree-based (GBM/XGBoost/LightGBM).
     - Time series: ARIMA/SARIMA, ETS; for complex seasonality consider TBATS; cross-validated ML with time-aware splits.
     - Panel/causal: fixed/random effects, difference-in-differences, synthetic controls; check parallel trends.
   - Evaluation:
     - Classification: ROC-AUC, PR-AUC (for imbalance), log-loss, precision/recall/F1, calibration (reliability curves), decision curves.
     - Regression: MAE, RMSE, MAPE/sMAPE (mind zero-valued targets).
     - Forecasting: sMAPE, MASE, rolling-origin validation.
   - Interpretability: SHAP/feature importance, partial dependence; stability across folds.

6. Visualization
   - Choose charts aligned with data type and message: distributions (histograms/ECDF/box/violin), relationships (scatter with CI/binned heatmaps), time series (decomposition, anomaly overlays), categorical comparisons (bar/point-range with CIs).
   - Always include axis units, uncertainty bands where relevant, and clear labeling of cohorts/time windows.

7. Results interpretation and recommendations
   - Summarize key findings with quantified effects and uncertainty.
   - Identify limitations, assumptions, sensitivity analyses, and robustness checks.
   - Translate findings into actionable steps and expected impact on KPIs.

Quality and bias considerations
- Confounding: Include known covariates, stratify or adjust via regression/propensity methods.
- Selection bias: Verify sampling frame; compare sample vs population.
- Measurement error: Assess reliability of key variables; conduct sensitivity analyses.
- Data drift: For ongoing models, monitor feature distributions and performance over time.
- Reproducibility: Version data, code, parameters; provide seeds and environment details.

Deliverables (tailored to your task)
- Cleaned dataset summary and data quality report.
- EDA visuals and statistical test results with effect sizes and CIs.
- Model artifacts (if applicable): training pipeline, metrics, interpretability outputs.
- Executive summary: findings, limitations, and recommended actions.
- Reproducible notebook/script with instructions.

To begin, please provide:
- The exact question(s) you want answered and the decision context.
- Schema or sample rows, and a brief data dictionary.
- Time range, granularity, and any inclusion/exclusion rules.
- Target variable definition (if modeling) and key KPIs.
- Any known constraints, experimental setup, or suspected issues.
- Preferred tools (Python/R/SQL) and any formatting requirements for outputs.

Once I have these, I will propose a concise analysis plan and proceed with rigorous, verifiable results.

已就緒。為確保分析高效、準確並可重現，請提供以下最小必要資訊與樣本資料。收到後我將基於您的目標設計嚴謹的預處理、統計分析、可視化與結果解讀流程。

請提供
- 分析目標與決策問題：明確定義要回答的問題、成功指標（KPI/指標）、期望輸出形式（圖表、指標表、模型、建議）。
- 資料來源與結構：資料表清單、欄位名稱、資料型別（數值/類別/時間）、主鍵與關聯（外鍵）、單位與時區。
- 時間範圍與抽樣：分析期間、是否滾動視窗、抽樣策略（若資料量大）。
- 定義與口徑：指標口徑（如「活躍用戶」定義）、去重規則、缺失值處理原則、異常值處理原則。
- 統計或模型需求：需要描述統計、推論（假設檢定/置信區間）、因果推斷、預測/分類或分群。
- 約束與環境：可用工具（Python/R/SQL）、效能限制、資料存取規範與隱私要求。
- 代表性樣本：每張表提供10–50行樣本（可匿名），或一份可重現的匯出檔（CSV/Parquet），以便快速驗證欄位與分佈。

預期分析流程（可依任務調整）
1) 資料品質與預處理
- 結構檢查：主鍵唯一性、關聯完整性、重複列與重複事件。
- 缺失與異常：缺失模式（MCAR/MAR/MNAR）分析；極端值與錯誤單位/時區；類別稀疏度。
- 一致性：時間戳對齊、事件去重、口徑統一（如時區、週期、轉換漏斗邏輯）。
- 特徵工程：衍生指標（轉化率、留存、客單價、週期聚合）、編碼（類別/日期）、尺度轉換。

2) 探索性分析（EDA）
- 分佈與集中趨勢：直方圖、箱型圖、分位數、偏態與峰度。
- 關聯性：皮爾森/史匹爾曼相關、交叉表、群組對比（分群、分渠道、分時段）。
- 漏斗與路徑：步驟轉化率、瓶頸定位、用戶行為序列。

3) 推論與建模（視需求）
- 組間對比：均值/比例檢定、效果大小（Cohen’s d）、多重比較校正。
- 時序分析：季節性/週期性分解、異常偵測、干預分析。
- 預測/分類：基準模型→正則化→交叉驗證；特徵重要度與穩健性。
- 因果設計：傾向分數、雙重差分、斷點回歸（若可行且滿足假設）。

4) 可視化與交付
- 圖表：分佈、時間序列、分群對比、漏斗、地圖（若有地域）。
- 指標表：核心KPI、子維度拆解、置信區間。
- 建議：行動建議與風險；敏感度分析；可重現腳本或筆記本。

風險與精度控制
- 明確口徑與時間對齊，避免因定義差異造成偏差。
- 報告統計不確定性（置信區間、p值、效果大小）與樣本量充足性。
- 對多次比較進行校正；避免資料探勘式誤報。
- 匯報限制與假設，避免過度延伸結論。

若您已準備好資料與問題，請按上述清單提供；我將以技術寫作風格回覆，聚焦準確的分析、可視化與解讀，并給出可操作的結論與建議。