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Mathematical statistics is the application of probability theory and other mathematical concepts to statistics, as opposed to techniques for collecting statistical data. [1] Specific mathematical techniques that are commonly used in statistics include mathematical analysis , linear algebra , stochastic analysis , differential equations , and ...
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
Mathematical statistics is the application of mathematics to statistics. Mathematical techniques used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory. [1] [7] All statistical analyses make use of at least some mathematics, and mathematical statistics ...
Romberg's method (numerical analysis) Runge–Kutta method (numerical analysis) Sainte-Laguë method (voting systems) Schulze method (voting systems) Sequential Monte Carlo method; Simplex method; Spectral method (numerical analysis) Variational methods (mathematical analysis, differential equations) Welch's method
The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications of statistics. [1] [2] The theory covers approaches to statistical-decision problems and to statistical inference, and the actions and deductions that satisfy the basic principles stated for these different approaches.
The Foundations of Statistics are the mathematical and philosophical bases for statistical methods. These bases are the theoretical frameworks that ground and justify methods of statistical inference , estimation , hypothesis testing , uncertainty quantification , and the interpretation of statistical conclusions.