| Visualisation & Numeric Summary |
Craft frequency & contingency tables, barplots, histograms. Read the data's “story” with means, medians, variances, quartiles, and shape. |
| Probability Theory |
Manipulate events (AND, OR, negation, implication). Apply conditional probability, independence, the Law of Total Expectation, and Bayes’ Rule—the backbone of expert systems and machine learning. |
| Random Variables |
Describe distributions via PMF, PDF, and CDF; compute expectation & variance; work fluently with Binomial, Poisson, Geometric, Uniform, Exponential, and Normal laws; appreciate the power of the Poisson & Central Limit Theorems. |
| Sampling & Confidence Intervals |
Use random samples to infer population parameters and wrap your estimates in accuracy bounds. |
| Hypothesis Testing |
Detect shifts in means, proportions, and variances with Z, t, F, and two-sample tests. Know the assumptions—and how to handle paired data. |
| Bivariate Analysis |
Quantify relationships with covariance, correlation, and simple linear regression; interpret residuals and R². |
| Multivariate Data & Multiple Regression |
Diagnose model fit with plots and ANOVA tables, judge coefficient significance, avoid overfitting, and streamline models by trimming predictors. |
