Yuly Koshevnik Fundamentals Of Statistical Thinking: Tools And Applications [portable] -

Finding or practice problems related to this specific curriculum

: Earned a Ph.D. in Mathematical Statistics from Moscow State University .

To truly learn, apply each concept to a : Finding or practice problems related to this specific

Fundamentals of Statistical Thinking: Tools and Applications

"Statistical thinking," as defined in this context, is the ability to see the world through the lens of variability and uncertainty. It is the discipline of moving away from deterministic answers ("X causes Y") to probabilistic reasoning ("X increases the likelihood of Y, given Z"). It is the discipline of moving away from

This guide assumes the book emphasizes conceptual understanding, real‑data reasoning, and practical tool use (e.g., R, Python, or spreadsheet‑based).

| Mistake | Koshevnik’s corrective | |---------|------------------------| | Using mean without checking outliers | Always use median + IQR for skewed data | | Interpreting correlation as causation | Draw causal diagrams (DAGs) | | p‑hacking (multiple tests) | Apply Bonferroni / FDR correction | | Overfitting regression models | Use adjusted R² or cross‑validation | | Ignoring assumption checks | Test normality, equal variance before t‑test/ANOVA | Applied task : Predict house prices from square

This mirrors Koshevnik’s emphasis on .

Applied task : Predict house prices from square footage and age; check residuals.

– Ch 1–3: Descriptive stats + probability Week 3 – Ch 4–5: Inference (CI, hypothesis tests) Week 4 – Ch 6–7: Regression & Bayesian intro Week 5 – Ch 8–9: Categorical data + DOE Week 6 – Ch 10–11 + capstone project