Statistics — Sayfa 52
Explore statistical methods, their assumptions, interpretation and limitations.
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Yöntem ailesi
RANSAC Regression
RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeate...
RCOPE (Full Religious Coping)
The RCOPE, developed by Kenneth Pargament, Harold Koenig, and Lisa Perez in 2000, is the comprehensive instrument for measuring how people draw on religion to cope with stress, loss, and crisis. Where earlier measures asked only whether rel...
ROC analysis
ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision t...
Racial Resentment Scale
The Racial Resentment Scale, developed by Donald Kinder and Lynn Sanders in Divided by Color (1996), measures a modern, symbolic form of racial prejudice in which anti-Black affect is fused with the traditional American value of individuali...
Radiation Model
The Radiation Model, introduced by Simini et al. in 2012, is a parameter-free model for predicting human mobility and migration flows between geographic locations. Drawing an analogy from radiation physics, it predicts trip volumes based so...
Ramsey RESET Test
The Ramsey RESET test, proposed by James Ramsey in 1969, is a general test for functional-form misspecification in a linear regression — for omitted nonlinear relationships between the response and the regressors. It adds powers of the fitt...
Ramsey-Cass-Koopmans Model
The Ramsey-Cass-Koopmans model, developed initially by Frank Ramsey in 1928 and formalized by David Cass and Tjalling Koopmans in 1965, is the workhorse model of macroeconomic growth theory. It describes how rational consumers optimize cons...
Random Effects Model
The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel...
Random Effects Panel Model
The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed pa...
Random Survival Forest
Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank sp...
Randomization Inference
Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is rega...
Randomized Complete Block Design
The Randomized Complete Block Design (RCBD) is a parametric experimental design and hypothesis-testing framework that isolates and removes a known source of heterogeneity — called a block — before comparing treatment means. Introduced by Ro...
Randomized Controlled Trial
A randomized controlled trial (RCT) is the gold standard experimental design in clinical and health research, in which participants are randomly allocated to a treatment group or a control group so that the effect of an intervention can be...
Rasch Analysis of Disability Measures
Rasch analysis is a psychometric method, based on Georg Rasch's probabilistic measurement model, used to test and refine the disability, function, and participation scales that pervade disability and rehabilitation research. As set out for...
Rasch Model
The Rasch model, introduced by Georg Rasch in 1960, is the simplest member of the Item Response Theory (IRT) family. It assigns a single difficulty parameter to each test item and places both item difficulties and person abilities on the sa...
Real Business Cycle Model
The Real Business Cycle (RBC) model, developed by Finn Kydland and Edward Prescott in 1982, is a dynamic stochastic general equilibrium framework that explains macroeconomic fluctuations as rational responses to exogenous technological shoc...
Realized Volatility
Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility...
Recidivism Survival Analysis
Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods...
Recreation Demand Travel Cost Model
The recreation demand travel cost model values a recreation site — a national park, beach, lake, or heritage attraction — by exploiting the fact that visitors reveal how much the experience is worth to them through the cost they incur to ge...
Recreation Experience Preference Scales
The Recreation Experience Preference (REP) scales are a hierarchical battery of self-report measures, developed by B. L. Driver and colleagues over three decades, that quantify the desired psychological outcomes people seek from recreation....
Recurrent Event Model
A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data w...
Redistribution Preference Analysis
Redistribution preference analysis examines why individuals support or oppose government efforts to reduce inequality. The self-interest baseline comes from Meltzer and Richard's 1981 model, in which the demand for redistribution falls with...
Redundancy Analysis
Redundancy Analysis (RDA) is a multivariate technique developed by van den Wollenberg (1977) that combines multiple regression and principal component analysis. RDA finds linear combinations of predictor variables that best predict variatio...
Reference Price Modeling
Reference price models capture the behavioral reality that consumers judge a price not in absolute terms but relative to an internal benchmark — a reference price they have formed from past prices. When the observed price falls below the re...