EDA utilizes visual frameworks to detect anomalies, patterns, and underlying structures.
: Viewing all work as a series of interconnected processes where understanding one part helps explain the behavior of the whole.
Models the functional relationship between independent drivers and dependent outcomes. 3. Practical Frameworks for Application It’s a mindset: Tools like histograms, hypothesis tests,
Guard against selecting data subsets that merely validate prior beliefs.
┌────────────────────────────────────────────────────────┐ │ PPDAC Cycle (Statistical Workflow) │ └───────────────────────────┬────────────────────────────┘ ▼ [Problem] ──> [Plan] ──> [Data] ──> [Analysis] ──> [Conclusion] 🔁 The PPDAC Cycle 📊 Exploratory Data Analysis (EDA)
[Raw Data] ──> [Descriptive Summaries] ──> [Visual Explorations] ──> [Inferential Models] 🔢 Descriptive Statistics
The guide also covered more advanced topics, such as: It’s a mindset: Tools like histograms
Statistical thinking is not just about formulas. It’s a mindset: Tools like histograms, hypothesis tests, confidence intervals, and regression turn raw data into actionable insights.
Skewness measures profile asymmetry. Kurtosis measures tail thickness and outlier frequency. 📊 Exploratory Data Analysis (EDA)