Statistics Every Programmer Needs

They transform raw data into actionable insights, enabling us to navigate uncertainty, optimize outcomes, and make informed choices in high-stakes scenarios. [1] [2]

Probability distributions, such as normal, binomial, and Poisson, model the likelihood of different outcomes, forming the foundation for statistical inference and risk assessment. [1] [2]

Forecasts account for the time-based structure of data, predicting future values based on past observations, while predictions often ignore temporal order. [1]

ARIMA combines autoregressive, differencing, and moving average components to capture trends, seasonality, and autocorrelation in sequential data. [1]

Exponential smoothing models are preferred for short-term forecasts where recent data is more indicative of the future, offering simplicity and adaptability for data with trends or seasonality. [1] [2]

Decision trees split data based on feature importance for interpretability, while random forests aggregate multiple trees to enhance accuracy and reduce overfitting. [1] [2]

Logistic regression models the probability of categorical outcomes, making it a foundational tool for binary and multi-class classification. [1] [2]

Linear programming formulates objective functions and constraints to find the optimal allocation of limited resources, such as maximizing project value within a fixed budget. [1] [2]

Monte Carlo simulations incorporate randomness to model uncertainty, providing a probabilistic view of outcomes and supporting better decision-making under uncertainty. [1] [2]

Markov chains model transitions between states, such as customer loyalty and churn, enabling forecasts of long-term market share dynamics. [1] [2]

Benford’s Law predicts the distribution of leading digits in naturally occurring numbers, so deviations from this pattern can indicate potential fraud or data manipulation. [1] [2]

Statistical risk analysis, such as using variance and z-scores, helps estimate the probability of on-time project completion and manage uncertainty in schedules. [1] [2]