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Type of regression analysis
Functional regression is a version of regression analysis when responses or covariates include functional data. Functional regression models can be classified
Functional_regression
Checking whether changes to software have broken functionality that used to work
Regression testing (rarely, non-regression testing) is re-running functional and non-functional tests to ensure that previously developed and tested software
Regression_testing
Software bug in which features stop working
problem is regression testing. A properly designed test plan using automated testing and well-written test cases aims at preventing regressions before software
Software_regression
Branch of statistics mathematics
to extending linear regression model to polynomial regression model. For a scalar response Y {\displaystyle Y} and a functional covariate X ( ⋅ ) {\displaystyle
Functional_data_analysis
Statistical method for investigating the dominant modes of variation of functional data
expansion. FPCA can be applied for representing random functions, or in functional regression and classification. For a square-integrable stochastic process X(t)
Functional principal component analysis
Functional_principal_component_analysis
Set of statistical processes for estimating the relationships among variables
called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which
Regression_analysis
Statistical modeling method
regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression
Linear_regression
Statistics concept
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable
Polynomial_regression
Statistics concept
regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,
Regression_validation
Testing software functionality
integration maturity stage. Regression testing (rarely, non-regression testing) is re-running functional and non-functional tests to ensure that previously
Functional_testing
Type of numerical analysis
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations
Isotonic_regression
Statistical model extension
of multiple functional predictors with a scalar response, the Functional Additive Model can be extended by fitting a functional regression which is additive
Functional_additive_model
Machine learning technique
algorithms as "functional gradient boosting". Friedman et al. describe an advancement of gradient boosted models as Multiple Additive Regression Trees (MART);
Gradient_boosting
Statistical model for count data
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes
Poisson_regression
Statistical phenomenon
In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where
Regression_toward_the_mean
Regression models that combine parametric and nonparametric models
In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations
Semiparametric_regression
Moving average and polynomial regression method for smoothing data
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its
Local_regression
Statistical technique
taken into account. It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models
Total_least_squares
Topics referred to by the same term
Look up regression, regressions, or régression in Wiktionary, the free dictionary. Regression or regressions may refer to: Regression (film), a 2015 horror
Regression
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
Statistical model for a binary dependent variable
combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model
Logistic_regression
Mathematical model for stochastic processes
Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included
Generalized functional linear model
Generalized_functional_linear_model
Statistical bias in linear regressions
Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the underestimation of its absolute
Regression_dilution
Specialized form of regression analysis, in statistics
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship
Robust_regression
Statistical method
squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of
Partial least squares regression
Partial_least_squares_regression
Statistical linear model
model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is
General_linear_model
Algorithm for the line of best fit for a two-dimensional dataset
data-sources; however the regression procedure takes no account for possible errors in estimating this ratio. The Deming regression is only slightly more
Deming_regression
Checking software against expectations
test parts of the new design to ensure prior functionality is still supported. Common methods of regression testing include re-running previous sets of
Software_testing
Statistical method
parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y
Regression discontinuity design
Regression_discontinuity_design
Empirical law on the variance of species in a habitat
error of the regression, α and β are the constant and slope of the regression respectively, sβ2 is the variance of the slope of the regression, N is the
Taylor's_law
Statistical genetics technique
regression, has the advantage of not being biased if used on overlapping samples. Another extension of LDSC, known as stratified LD score regression (abbreviated
Linkage disequilibrium score regression
Linkage_disequilibrium_score_regression
Regression analysis
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination
Nonlinear_regression
Class of statistical survival models
itself be described as a regression model. There is a relationship between proportional hazards models and Poisson regression models which is sometimes
Proportional_hazards_model
Romanian-American biostatistician
and American biostatistician whose research topics have included functional regression, longitudinal data, spatial statistics, and the applications of
Ana-Maria_Staicu
Framework for machine learning
as an example, a regression could be performed with voltage as input and current as an output. The regression would find the functional relationship between
Statistical_learning_theory
Part of the process of building a statistical model
Hall, Stephen G. (2011). "Misspecification: Wrong regressors, measurement errors and wrong functional forms". Applied Econometrics (Second ed.). Palgrave
Statistical model specification
Statistical_model_specification
Regression analysis technique
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is
Binomial_regression
Method of statistical analysis
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables
Bayesian_linear_regression
Statistics concept
distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead
Errors_and_residuals
Digital switching platform
Server". Nokia Networks. 6 February 2017. Retrieved 22 April 2017. Functional regression testing and test automation in a 3G network element platform environment
Nokia_DX_200
Measure of linear correlation
Standardized covariance Standardized slope of the regression line Geometric mean of the two regression slopes Square root of the ratio of two variances
Pearson correlation coefficient
Pearson_correlation_coefficient
Sequence of data points over time
simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial
Time_series
Statistical test for model misspecification
statistics, the Ramsey Regression Equation Specification Error Test (RESET) test is a general specification test for the linear regression model. More specifically
Ramsey_RESET_test
Dimensionality reduction technique
defined as, Functional data analysis Functional principal component analysis Karhunen–Loève theorem Functional regression Generalized functional linear model
Functional_correlation
Regression models accounting for possible errors in independent variables
error model is a regression model that accounts for measurement errors in the independent variables. In contrast, standard regression models assume that
Errors-in-variables_model
Number of values in the final calculation of a statistic that are free to vary
regression methods, including regularized least squares (e.g., ridge regression), linear smoothers, smoothing splines, and semiparametric regression,
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Approximation method in statistics
as the least angle regression algorithm. One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty is
Least_squares
Class of statistical models
(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the
Generalized_linear_model
Indicator for how well data points fit a line or curve
remaining 51% of the variability is still unaccounted for. For regression models, the regression sum of squares, also called the explained sum of squares,
Coefficient_of_determination
Smooth function in statistics
model framework and a tool used in non-parametric regression, semiparametric regression and functional data analysis. In parametric modeling, variance functions
Variance_function
Set of methods for supervised statistical learning
predictive performance than other linear models, such as logistic regression and linear regression. Classifying data is a common task in machine learning. Suppose
Support_vector_machine
Collection of statistical models
notation in place, we now have the exact connection with linear regression. We simply regress response y k {\displaystyle y_{k}} against the vector X k {\displaystyle
Analysis_of_variance
Category of regression analysis
Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information
Nonparametric_regression
How many standard deviations apart from the mean an observed datum is
to multiple regression analysis is sometimes used as an aid to interpretation. (page 95) state the following. "The standardized regression slope is the
Standard_score
Statistical property
which performs an auxiliary regression of the squared residuals on the independent variables. From this auxiliary regression, the explained sum of squares
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Statistical hypothesis test
the linear regression to the result from the t-test. From the t-test, the difference between the group means is 6-2=4. From the regression, the slope
Student's_t-test
Branch of statistics
the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function
Mathematical_statistics
Statistical method
testing. In regression problems, case resampling refers to the simple scheme of resampling individual cases – often rows of a data set. For regression problems
Bootstrapping_(statistics)
Gasko Donoho, American statistician, expert on binary regression, survival analysis, robust regression, and data visualization Sandrine Dudoit, applies statistics
List_of_women_in_statistics
International safety standard for automotive electrical and electronic systems
ISO 26262, titled "Road vehicles – Functional safety", is an international standard for functional safety of electrical and/or electronic systems that
ISO_26262
Optical technique for monitoring brain activity
Functional near-infrared spectroscopy (fNIRS), sometimes referred to as NIRS or Optical Topography (OT), is an optical brain monitoring technique which
Functional near-infrared spectroscopy
Functional_near-infrared_spectroscopy
Branch of statistics
time-varying covariates. The Cox PH regression model is a linear model. It is similar to linear regression and logistic regression. Specifically, these methods
Survival_analysis
Statistical measure of the magnitude of a phenomenon
sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, and the risk of a particular event
Effect_size
Statistical property
measure of the dispersion of sample means around the population mean. In regression analysis, the term "standard error" can also be used to refer to the square
Standard_error
Process of using data analysis for predicting population data from sample data
assumptions of Normality in the population also invalidates some forms of regression-based inference. The use of any parametric model is viewed skeptically
Statistical_inference
Statistical model validation technique
context of linear regression is also useful in that it can be used to select an optimally regularized cost function.) In most other regression procedures (e
Cross-validation_(statistics)
Metric for fit of statistical models
Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness
Goodness_of_fit
General linear model that blends ANOVA and regression
linear regression assumptions hold; further we assume that the slope of the covariate is equal across all treatment groups (homogeneity of regression slopes)
Analysis_of_covariance
Variable capable of taking on a limited number of possible values
distribution (the Bernoulli distribution) and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable"
Categorical_variable
Type of receptor ligand or drug that blocks a biological response
using Schild regression or for competitive antagonists in radioligand binding studies using the Cheng–Prusoff equation. Schild regression can be used to
Receptor_antagonist
Method used in statistics, pattern recognition, and other fields
categorical dependent variable (i.e. the class label). Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain
Linear_discriminant_analysis
Type of statistical measure over subsets of a dataset
various applications in image signal processing. In a moving average regression model, a variable of interest is assumed to be a weighted moving average
Moving_average
Simultaneous observation and analysis of more than one outcome variable
problems involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate
Multivariate_statistics
Tools to represent statistical uncertainty
probability function. Confidence bands commonly arise in regression analysis. In the case of a simple regression involving a single independent variable, results
Confidence and prediction bands
Confidence_and_prediction_bands
Statistical methods to correct for endogeneity problems
the exponential regression framework, which the following discussion follows closely. While the example focuses on a Poisson regression model, it is possible
Control function (econometrics)
Control_function_(econometrics)
Family of statistical methods based on sampling of available data
uses the sample median; to estimate the population regression line, it uses the sample regression line. It may also be used for constructing hypothesis
Resampling_(statistics)
as the online application marketplace. NIST also conducted functional regression testing and quantitative performance testing in the realms of platform
TransApps
Technique in statistics
explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur when: changes in the dependent variable
Instrumental_variables
Estimator for quality of a statistical model
loss.) Comparison of AIC and BIC in the context of regression is given by Yang (2005). In regression, AIC is asymptotically optimal for selecting the model
Akaike_information_criterion
Normality test
David Lilien, et al. (1995) when using this test along with multiple regression analysis the right estimate is: J B = n − k 6 ( S 2 + 1 4 ( K − 3 ) 2
Jarque–Bera_test
Model for generating observable data in probability and statistics
necessarily perform better than generative models at classification and regression tasks. The two classes are seen as complementary or as different views
Generative_model
Statistical hypothesis test
that a proposed regression model fits the data well. See Lack-of-fit sum of squares. The hypothesis that a data set in a regression analysis follows
F-test
Branch of logic
thoroughly researched branch of propositional logic is classical truth-functional propositional logic, in which formulas are interpreted as having precisely
Propositional_logic
Subset of artificial intelligence
classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, while regression algorithms are
Machine_learning
Statistic measuring inter-rater agreement for categorical items
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Cohen's_kappa
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Coefficient_of_variation
Unscientific healthcare practices
effect); the natural recovery from or the cyclical nature of an illness (the regression fallacy) gets misattributed to an alternative medicine being taken; a
Alternative_medicine
Diagnostic plot of binary classifier ability
Notable proposals for regression problems are the so-called regression error characteristic (REC) Curves and the Regression ROC (RROC) curves. In the
Receiver operating characteristic
Receiver_operating_characteristic
Categorization of data using statistics
logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.)
Statistical_classification
Statistical matching technique
control group—based on observed predictors, usually obtained from logistic regression to create a counterfactual group. Propensity scores may be used for matching
Propensity_score_matching
Study of collection and analysis of data
doing regression. Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is
Statistics
Measure of distance between two proportions
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Cohen's_h
Statistical relationship
variables have the same mean (7.5), variance (4.12), correlation (0.816) and regression line ( y = 3 + 0.5 x {\textstyle y=3+0.5x} ). However, as can be seen
Correlation
Concept in machine learning
to perform better with larger models. Double descent occurs in linear regression with isotropic Gaussian covariates and isotropic Gaussian noise. A model
Double_descent
Statistical hypothesis test for forecasting
Any particular lagged value of one of the variables is retained in the regression if (1) it is significant according to a t-test, and (2) it and the other
Granger_causality
Service offered between electronic devices via the Internet
enveloping and transporting. Functional and non-functional testing of Web services is done with the help of WSDL parsing. Regression testing is performed by
Web_service
Type of statistical model
occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However, the term is also used
Linear_model
Integrated set of tools
analysis, unit test case generation and execution, code coverage analysis, regression testing, runtime error detection, requirements traceability, and code
Parasoft_C/C++test
Type of statistical model
can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became
Multilevel_model
American economist
hdl:1721.1/63275. JSTOR 2951752. — (1994). "Series Estimation of Regression Functionals". Econometric Theory. 10 (1): 1–28. doi:10.1017/S0266466600008203
Whitney_K._Newey
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
Male
Egyptian
, Functionary of the Interior.
Male
Egyptian
, a high Egyptian functionary.
Boy/Male
English
Modern. The fictional character Jorel father of Superman.
Male
Egyptian
, an Egyptian functionary.
Boy/Male
American, British, English
Mighty Spearman; One who Saves; The Fictional Character Jorel Father of Superman
Boy/Male
Buddhist, Indian, Japanese
Mysterious Function
Boy/Male
French
Fictional swordsman: (ambitious and filled with religious aspirations) from Alexander Dumas's...
Boy/Male
American, Australian, British, Danish, English, Finnish, French, German, Scandinavian
Farmer; The Fictional Character Jorel Father of Superman; Earth Worker
Boy/Male
English
The fictional character Jorel father of Superman.
Boy/Male
American, British, English
Mighty Spearman; The Fictional Character Jorel Father of Superman
Male
Egyptian
, the son of the functionary Heknofre.
Male
Egyptian
, a great functionary.
Biblical
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Boy/Male
English
The fictional character Jorel father of Superman.
Boy/Male
English
The fictional character Jorel father of Superman.
Boy/Male
American, Australian, British, English, French
Mighty Spearman; The Fictional Character Jorel Father of Superman
Surname or Lastname
English
English : nickname from the animal, Middle English catte ‘cat’. The word is found in similar forms in most European languages from very early times (e.g. Gaelic cath, Slavic kotu). Domestic cats were unknown in Europe in classical times, when weasels fulfilled many of their functions, for example in hunting rodents. They seem to have come from Egypt, where they were regarded as sacred animals.English : from a medieval female personal name, a short form of Catherine.Variant spelling of German and Dutch Katt.
Male
Celtic
, great justiciary, or functionary.
Boy/Male
Australian, French
Fictional Swordsman; Ambitious and Filled with Religious Aspirations; From Alexander Dumas's Three Musketeers
Male
Egyptian
, an Egyptian functionary.
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION