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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
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
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
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
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
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
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 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
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
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
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
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
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
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
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 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
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
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
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
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
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
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
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
Romanian-American biostatistician
and American biostatistician whose research topics have included functional regression, longitudinal data, spatial statistics, and the applications of
Ana-Maria_Staicu
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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
Type of statistics
their applicability. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections between
Robust_statistics
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
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
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
Dimensionality reduction technique
defined as, Functional data analysis Functional principal component analysis Karhunen–Loève theorem Functional regression Generalized functional linear model
Functional_correlation
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
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
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 algorithm
predictive power of the regression model. In statistics, a nonlinear transformation of variables is commonly used in practice in regression problems. ACE is
Alternating conditional expectations
Alternating_conditional_expectations
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)
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
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
Design of tasks
publication on an optimal design for regression models in 1876. A pioneering optimal design for polynomial regression was suggested by Gergonne in 1815.
Design_of_experiments
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
Correlation of a signal with a time-shifted copy of itself, as a function of shift
whether or not the regressors include lags of the dependent variable, is the Breusch–Godfrey test. This involves an auxiliary regression, wherein the residuals
Autocorrelation
Concept in inferential statistics
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Statistical_significance
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
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
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
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
Fundamental theorem in probability theory and statistics
large-sample statistics to the normal distribution in controlled experiments. Regression analysis, and in particular ordinary least squares, specifies that a dependent
Central_limit_theorem
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
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
Probabilistic problem-solving algorithm
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Monte_Carlo_method
Parametric model in survival analysis
=\exp(-[\beta _{1}X_{1}+\cdots +\beta _{p}X_{p}])} . (Specifying the regression coefficients with a negative sign implies that high values of the covariates
Accelerated failure time model
Accelerated_failure_time_model
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 property of collections of time series data
as more regressors are included. If the variables are found to be cointegrated, a second-stage regression is conducted. This is a regression of Δ y t
Cointegration
Continuous probability distribution
standard linear regression is used for modeling continuous variables (e.g., income or population). Specifically, logistic regression models can be phrased
Logistic_distribution
Range to estimate an unknown parameter
under Excel Confidence interval calculators for R-Squares, Regression Coefficients, and Regression Intercepts Weisstein, Eric W. "Confidence Interval". MathWorld
Confidence_interval
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
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
Class of statistical tests
by regressing the data against the quantiles of a normal distribution with the same mean and variance as the sample. Lack of fit to the regression line
Normality_test
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
Measure of the asymmetry of random variables
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Skewness
Measure of statistical dispersion
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Interquartile_range
Measure of covariance of components of a random vector
{YX} }\operatorname {K} _{\mathbf {XX} }^{-1}} is known as the matrix of regression coefficients, while in linear algebra K Y | X {\displaystyle \operatorname
Covariance_matrix
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
Male
Egyptian
, the son of the functionary Heknofre.
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.
Boy/Male
Buddhist, Indian, Japanese
Mysterious Function
Boy/Male
American, Australian, British, Danish, English, Finnish, French, German, Scandinavian
Farmer; The Fictional Character Jorel Father of Superman; Earth Worker
Boy/Male
English
Modern. The fictional character Jorel father of Superman.
Male
Egyptian
, an Egyptian functionary.
Boy/Male
Australian, French
Fictional Swordsman; Ambitious and Filled with Religious Aspirations; From Alexander Dumas's Three Musketeers
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
Boy/Male
American, British, English
Mighty Spearman; One who Saves; The Fictional Character Jorel Father of Superman
Male
Egyptian
, a high Egyptian functionary.
Boy/Male
French
Fictional swordsman: (ambitious and filled with religious aspirations) from Alexander Dumas's...
Male
Egyptian
, Functionary of the Interior.
Male
Egyptian
, a great functionary.
Male
Celtic
, great justiciary, or functionary.
Male
Egyptian
, an Egyptian 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, British, English
Mighty Spearman; The Fictional Character Jorel Father of Superman
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
Boy/Male
English American Greek Shakespearean
From the cliff.
Biblical
servant of the Lord
Boy/Male
Hindu, Indian
Brave Guy
Boy/Male
Australian, Danish, Japanese, Swedish
Man's Defender; Shining Upon Man
Girl/Female
American, British, English, Greek, Latin
A State of Order or Agreement; Unity; Concord; Musically in Tune; A Tuneful Sound
Girl/Female
Tamil
Strange
Boy/Male
Tamil
Air circulating in the body
Boy/Male
Hindu
Treasure of light, Another name for the Sun
Boy/Male
Hindu, Indian
A Fortunate and Wealthy Person
Boy/Male
Tamil
Charioteer
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
FUNCTIONAL REGRESSION
a.
Pertaining to, or characterized by, fiction; fictitious; romantic.
a.
Capable of, or pertaining to, flection or inflection.
n.
The office, duties, or functions of a minister, servant, or agent; ecclesiastical, executive, or ambassadorial function or profession.
a.
Fractional.
n.
One charged with the performance of a function or office; as, a public functionary; secular functionaries.
v. i.
To execute or perform a function; to transact one's regular or appointed business.
n.
A derived function; a function obtained from a given function by a certain algebraic process.
n.
Paper fractional currency.
a.
Relating to friction; moved by friction; produced by friction; as, frictional electricity.
v. t.
To supply with an organ or organs having a special function or functions.
v. i.
Alt. of Functionate
pl.
of Functionary
adv.
In a functional manner; as regards normal or appropriate activity.
n.
A quantity so connected with another quantity, that if any alteration be made in the latter there will be a consequent alteration in the former. Each quantity is said to be a function of the other. Thus, the circumference of a circle is a function of the diameter. If x be a symbol to which different numerical values can be assigned, such expressions as x2, 3x, Log. x, and Sin. x, are all functions of x.
a.
Pertaining to, or connected with, a function or duty; official.
a.
Relatively small; inconsiderable; insignificant; as, a fractional part of the population.
a.
Of or pertaining to fractions or a fraction; constituting a fraction; as, fractional numbers.
n.
The appropriate action of any special organ or part of an animal or vegetable organism; as, the function of the heart or the limbs; the function of leaves, sap, roots, etc.; life is the sum of the functions of the various organs and parts of the body.
n.
An angle upon which the value of some function depends; -- a term used more especially in connection with elliptic functions.
a.
Pertaining to the function of an organ or part, or to the functions in general.