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Type of plot in applied statistics
partial regression plot attempts to show the effect of adding another variable to a model that already has one or more independent variables. Partial
Partial_regression_plot
Graphical technique in statistics to show error in a model
}}_{i}} = regression coefficient from the i-th independent variable in the full model, Xi = the i-th independent variable. Partial residual plots are widely
Partial_residual_plot
Graphical technique for data sets
Nichols plot Normal probability plot Nyquist plot Partial regression plot : In applied statistics, a partial regression plot attempts to show the effect of
Plot_(graphics)
of that variable in automatic regression model building procedures. Leverage Partial residual plot Partial regression plot Variance inflation factor for
Partial_leverage
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
In statistics, a regression diagnostic is one of a set of procedures available for regression analysis that seek to assess the validity of a model in any
Regression_diagnostic
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
Overview of and topical guide to regression analysis
outline is provided as an overview of and topical guide to regression analysis: Regression analysis – use of statistical techniques for learning about
Outline of regression analysis
Outline_of_regression_analysis
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
Method for estimating the unknown parameters in a linear regression model
especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent
Ordinary_least_squares
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
Concept in probability theory and statistics
computing the partial correlation coefficient. This is precisely the motivation for including other right-side variables in a multiple regression; but while
Partial_correlation
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
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
Comparison of two distributions
the Q–Q plot. Some Q–Q plots indicate the deciles to make determinations such as this possible. The intercept and slope of a linear regression between
Q–Q_plot
Plot using the dispersal of scattered dots to show the relationship between variables
Density scatter plot for large datasets (hundreds of millions of points) Importance of Scatter Plots - essential in correlation and regression Interactive
Scatter_plot
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)
correlation Partial least squares Partial least squares regression Partial leverage Partial regression plot Partial residual plot Particle filter Partition of
List_of_statistics_articles
Partial correlation of a time series with its lagged values
analysis, the partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, regressed the values
Partial autocorrelation function
Partial_autocorrelation_function
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
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
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
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
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 term
{i}^{th}} point in the partial regression plot for the j t h {\displaystyle {j}^{th}} variable. Data points with large partial leverage for an independent
Leverage_(statistics)
Least squares approximation of linear functions to data
^{\mathsf {T}}\mathbf {y} .} Optimal instruments regression is an extension of classical IV regression to the situation where E[εi | zi] = 0. Total least
Linear_least_squares
Data visualization
In descriptive statistics, a box plot or boxplot is a method for demonstrating graphically the locality, spread and skewness groups of numerical data through
Box_plot
Class of statistical survival models
hazard regression parameter. The Lasso estimator of the regression parameter β is defined as the minimizer of the opposite of the Cox partial log-likelihood
Proportional_hazards_model
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
Free and open-source statistical program
analyses for regression, classification and clustering: Regression Boosting Regression Decision Tree Regression K-Nearest Neighbors Regression Neural Network
JASP
Sub-class of survival models
regression structures. When first hitting time models are equipped with regression structures, accommodating covariate data, we call such regression structure
First-hitting-time_model
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
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
Concept in statistical analysis
Through regression analysis, one can derive the equation for the curve or straight line and obtain the correlation coefficient. Simple linear regression is
Bivariate_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 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
Method of estimating the parameters of a statistical model, given observations
analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when the random errors are assumed to have
Maximum_likelihood_estimation
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)
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 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
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
Method of plotting numeric data
A violin plot (also known as a bean plot) is a statistical graphic for comparing probability distributions. It is similar to a box plot, but has enhanced
Violin_plot
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
Statistics models class
splines or local linear regression smoothers) via the backfitting algorithm. Backfitting works by iterative smoothing of partial residuals and provides
Generalized_additive_model
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
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 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
Tools to represent statistical uncertainty
in regression analysis. In the case of a simple regression involving a single independent variable, results can be presented in the form of a plot showing
Confidence and prediction bands
Confidence_and_prediction_bands
Multinomial probit Multiple correspondence analysis Odds ratio Poisson regression Powered partial least squares discriminant analysis Qualitative variation Randomization
List of analyses of categorical data
List_of_analyses_of_categorical_data
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
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
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 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
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
Smooth function in statistics
linear model framework and a tool used in non-parametric regression, semiparametric regression and functional data analysis. In parametric modeling, variance
Variance_function
Function related to statistics and probability theory
{\partial \log f}{\partial \theta _{r}}}\,,\quad {\frac {\partial ^{2}\log f}{\partial \theta _{r}\partial \theta _{s}}}\,,\quad {\frac {\partial ^{3}\log
Likelihood_function
Nonparametric test of the null hypothesis
groups are in fact identical. If a test of medians is required, quantile regression explicitly tests this. Under this location shift assumption, we can also
Mann–Whitney_U_test
Optimization algorithm for artificial neural networks
{\frac {\partial E}{\partial w_{ij}}}={\frac {\partial E}{\partial o_{j}}}{\frac {\partial o_{j}}{\partial {\text{net}}_{j}}}{\frac {\partial {\text{net}}_{j}}{\partial
Backpropagation
Information-theoretic measure
cross-entropy loss for logistic regression is equal to the gradient of the squared-error loss for linear regression (up to a constant factor). To see
Cross-entropy
Form of causal modeling that fit networks of constructs to data
itself from correlation and regression when Sewall Wright provided explicit causal interpretations for a set of regression-style equations based on a solid
Structural_equation_modeling
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
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
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
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
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
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
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)
Method of data analysis
principal components and then run the regression against them, a method called principal component regression. Dimensionality reduction may also be appropriate
Principal_component_analysis
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
Graphical display of scientific results
Wikimedia Commons has media related to Forest plots. A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number
Forest_plot
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
Chart of correlation statistics
pandas: pandas.plotting.autocorrelation_plot R: functions acf and pacf Corrgrams: python seaborn: heatmap, pairplot R: corrgram Partial autocorrelation
Correlogram
Method of statistical inference
product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression validation Mixed
Statistical_hypothesis_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
Type of statistics
covariance (which reflects the scale variables are measured on). The slope, in regression analysis, also reflects the relationship between variables. The unstandardised
Descriptive_statistics
Experimental design that is optimal with respect to some statistical criterion
criterion results in minimizing the average variance of the estimates of the regression coefficients. C-optimality This criterion minimizes the variance of a
Optimal_experimental_design
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
Statistics concept
multiple regression analysis or causal modelling. To quantify the effect of a moderating variable in multiple regression analyses, regressing random variable
Moderation_(statistics)
Statistical hypothesis test
product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression validation Mixed
Wilcoxon_signed-rank_test
Nonparametric measure of rank correlation
product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression validation Mixed
Spearman's rank correlation coefficient
Spearman's_rank_correlation_coefficient
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
Probability distribution
These processes are used for regression, prediction, Bayesian optimization and related problems. For multivariate regression and multi-output prediction
Student's_t-distribution
Type of statistics
their applicability. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections between
Robust_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
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
Concepts from statistical hypothesis testing
descriptions of redirect targets Receiver operating characteristic – Diagnostic plot of binary classifier ability Sensitivity and specificity – Statistical measure
Type_I_and_type_II_errors
Apparent lack of pattern or predictability in events
product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression validation Mixed
Randomness
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
Statistic which divides a data set into 100 parts and analyzes it as a percentage
may often be represented by reference to a normal curve plot. The normal distribution is plotted along an axis scaled to standard deviations, or sigma (
Percentile
Statistical method
be sampled and variables fixed. Factor regression model is a combinatorial model of factor model and regression model; or alternatively, it can be viewed
Factor_analysis
Theorem related to ordinary least squares
of the Regression Model". Econometric Theory. Oxford: Blackwell. pp. 17–36. ISBN 0-631-17837-6. Goldberger, Arthur (1991). "Classical Regression". A Course
Gauss–Markov_theorem
Grouping a set of objects by similarity
product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression validation Mixed
Cluster_analysis
Statistical test
however, not actually t-distributed except for the special case of linear regression with normally distributed errors. In general, it follows an asymptotic
Wald_test
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
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
Statistical methods to improve the quality of manufactured goods
(help) Gaffke, N. & Heiligers, B. "Approximate Designs for Polynomial Regression: Invariance, Admissibility, and Optimality". pp. 1149–1199. {{cite book}}:
Taguchi_methods
Graphical representation of the distribution of numerical data
x-axis are all 1, then a histogram is identical to a relative frequency plot. Histograms are sometimes confused with bar charts. In a histogram, each
Histogram
Mathematical model used for classification or regression
Examples of discriminative models include: Logistic regression, a type of generalized linear regression used for predicting binary or categorical outputs
Discriminative_model
Selection of data points in statistics
product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression validation Mixed
Sampling_(statistics)
Statistical distribution for dependence between random variables
\\f_{XY}(x,y)={}&{\partial ^{2}C(u,v) \over \partial u\,\partial v}\cdot {\partial F_{X}(x) \over \partial x}\cdot {\partial F_{Y}(y) \over \partial y}\\\vdots
Copula_(statistics)
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