Logistic Regression
/ˈlɒdʒ.ɪ.stɪk rɪˈɡrɛʃ.ən/
noun … “predicting probabilities with a curve, not a line.”
Lasso Regression
/ˈlæs.oʊ rɪˈɡrɛʃ.ən/
noun … “OLS with selective pruning.”
Ridge Regression
/rɪdʒ rɪˈɡrɛʃ.ən/
noun … “OLS with a leash on wild coefficients.”
Ordinary Least Squares
/ˈɔːr.dən.er.i liːst skwɛərz/
noun … “fitting a line to tame the scatter.”
Ordinary Least Squares (OLS) is a fundamental method in statistics and regression analysis used to estimate the parameters of a linear model by minimizing the sum of squared differences between observed outcomes and predicted values. It provides the best linear unbiased estimates under classical assumptions, allowing analysts to quantify relationships between predictor variables and a response variable while assessing the strength and direction of these relationships.
Fourier Transform
/ˈfʊr.i.ɛr ˌtrænsˈfɔːrm/
noun … “the secret language of frequencies.”
SARIMA
/sɛˈriː.mə/
noun … “ARIMA with a seasonal compass.”
ARIMA
/ɑːrˈɪ.mə/
noun … “the Swiss army knife of time-series forecasting.”
Stationarity
/ˌsteɪ.ʃəˈnɛr.ɪ.ti/
noun … “when time stops twisting the rules of a system.”
Autocorrelation
/ˌɔː.toʊ.kəˈreɪ.ʃən/
noun … “how the past whispers to the present.”
Autocorrelation is a statistical measure that quantifies the correlation of a signal, dataset, or time series with a delayed copy of itself over varying lag intervals. It captures the degree to which current values are linearly dependent on past values, revealing repeating patterns, trends, or temporal dependencies. Autocorrelation is widely used in time-series analysis, signal processing, econometrics, and machine learning to detect seasonality, persistence, and memory effects in data.
Stochastic Process
/stoʊˈkæs.tɪk ˈproʊ.ses/
noun … “a story told by randomness over time.”