Exercise : Simulate dice rolls in R/Python to see the LLN in action.
Introduces readers to probability models and sampling distributions. These concepts are critical for understanding the inherent variation in any data set and the rules of chance that govern them. Exercise : Simulate dice rolls in R/Python to
| 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 | Exercise : Simulate dice rolls in R/Python to
Covers population vs. sample concepts and data presentation. Exercise : Simulate dice rolls in R/Python to
Dr. Koshevnik’s background in mathematical statistics from Moscow University and his experience as a senior statistical analyst in the corporate world ensure the material is both theoretically sound and practically applicable. Key Specifications