Descriptive Statistics
This section covers how to summarize and describe the main features of a dataset.
Topics Covered
| # | Post | Key Concepts |
|---|---|---|
| 01 | What Is Statistics | Descriptive vs inferential, anchor dataset introduction |
| 02 | Types of Statistics | Parametric vs nonparametric, levels of measurement |
| 03 | Population vs Sample | Parameters, statistics, sampling variability, standard error |
| 04 | Measure of Central Tendency | Mean, median, mode, geometric/harmonic mean |
| 05 | Measure of Dispersion | Range, IQR, variance, SD, CV, MAD |
| 06 | Why Sample Variance Uses n−1 | Bessel's correction, degrees of freedom |
| 07 | Standard Deviation | Empirical rule, z-scores, CV, when to use SD vs IQR |
| 08 | Variables | Variable types, measurement scales, independent/dependent |
| 09 | What Are Random Variables | Discrete/continuous, PMF/PDF, CDF |
| 10 | Histograms | Bin width, frequency, ogive, KDE, shape interpretation |
| 11 | Percentiles and Quartiles | Interpolation, IQR, five-number summary, box plots, Tukey fences |
| 12 | Covariance and Correlation | Pearson r, Spearman ρ, Kendall's τ, edge cases |
| 13 | Skewness and Kurtosis | Moments, Jarque-Bera test, normality workflow |
Prerequisites
- Basic math knowledge
- Python with NumPy and SciPy
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