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Quality control tool
In statistical quality control, the regression control chart allows for monitoring a change in a process where two or more variables are correlated. The
Regression_control_chart
Tool to assess control of a manufacturing process
Control charts (also known as Shewhart charts, after Walter A. Shewhart, or process-behavior charts) are graphical plots used in statistical process control
Control_chart
Statistical modeling method
is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. In linear regression, the relationships
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 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
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
process Regression analysis – see also linear regression Regression Analysis of Time Series – proprietary software Regression control chart Regression diagnostic
List_of_statistics_articles
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
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
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
Plot using the dispersal of scattered dots to show the relationship between variables
of quality control. According to Michael Friendly and Daniel Denis, the defining characteristic distinguishing scatter plots from line charts is the representation
Scatter_plot
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
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
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
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 measure of the magnitude of a phenomenon
sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, the standardised mean differences
Effect_size
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
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)
Circular statistical graph of proportionality
pie chart (or a circle chart) is a circular statistical graphic which is divided into slices to illustrate numerical proportion. In a pie chart, the
Pie_chart
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
Method of quality control
specifications) output can be measured. Key tools used in SPC include run charts, control charts, a focus on continuous improvement, and the design of experiments
Statistical_process_control
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)
Statistical method for handling multiple comparisons
hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries"
False_discovery_rate
Type of statistics
their applicability. Robust confidence intervals Robust regression Unit-weighted regression Sarkar, Palash (2014-05-01). "On some connections between
Robust_statistics
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
Dividing things between two categories
A common statistical technique to effect the classification is binary regression. When measuring the accuracy of a binary classifier, the simplest way
Binary_classification
Processes that maintain quality at a constant level
Quality control (QC) is a process by which entities review the quality of all factors involved in production. ISO 9000 defines quality control as "a part
Quality_control
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
Type of chart
measures. Radar charts are also known as web charts, spider charts, spider graphs, spider web charts, star charts, star plots, cobweb charts, irregular polygons
Radar_chart
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
Level of water from which depths displayed on a nautical chart are measured
A chart datum is the water surface serving as origin (or coordinate surface) of depths displayed on a nautical chart and for reporting and predicting tide
Chart_datum
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
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
Type of chart
A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that
Bar_chart
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
Collection of statistical models
notation in place, we now have the exact connection with linear regression. We simply regress response yk against the vector Xk. However, there is a concern
Analysis_of_variance
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
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
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
Value that appears most often in a set of data
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Mode_(statistics)
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
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
Astrological chart or diagram
chart, astrological chart, astro-chart, celestial map, sky-map, star-chart, cosmogram, vitasphere, radical chart, radix, chart wheel or simply chart)
Horoscope
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 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
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
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
Concepts from statistical hypothesis testing
[citation needed] A test statistic is robust if the type I error rate is controlled. Varying different threshold (cut-off) values could also be used to make
Type_I_and_type_II_errors
Scientific procedure performed to validate a hypothesis
comparisons (e.g. tasting a range of chocolates to find a favorite), to highly controlled (e.g. tests requiring complex apparatus overseen by many scientists that
Experiment
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
Statistical method
conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In
Matching_(statistics)
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
Mathematical relation assigning a probability event to a cost
including t-tests, regression models, design of experiments, and much else, use least squares methods applied using linear regression theory, which is based
Loss_function
Statistic quantifying the association between two events
regression. This technique has the advantage of allowing users to regress case-control status against multiple risk factors from matched case-control
Odds_ratio
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)
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
Type of statistical measure over subsets of a dataset
Venkataramani, C. (2003). "A multivariate exponentially weighted moving average control chart for monitoring process variability" (PDF). Journal of Applied Statistics
Moving_average
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
Data visualization with quartiles
A fan chart is made of a group of dispersion fan diagrams, which may be positioned according to two categorising dimensions. A dispersion fan diagram
Fan_chart_(statistics)
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
Statistical hypothesis test for the presence of serial correlation
autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic
Breusch–Godfrey_test
Type of chart
A line chart or line graph, also known as curve chart, is a type of chart that displays information as a series of data points called 'markers' connected
Line_chart
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
Interpretation of probability
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Bayesian_probability
Statistical matching technique
membership—e.g., treatment versus control group—based on observed predictors, usually obtained from logistic regression to create a counterfactual group
Propensity_score_matching
Graph that displays observed data in a time sequencer
decreases. Run charts are similar in some regards to the control charts used in statistical process control, but do not show the control limits of the
Run_chart
Relationship between items in a set
tables group teams of a particular league, conference, or division in a chart based on how well each is doing in a particular season of a sports league
Ranking
publication on an optimal design for regression-models in 1876. A pioneering optimal design for polynomial regression was suggested by Gergonne in 1815.[citation
History_of_statistics
Statistical measure of variability
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Median_absolute_deviation
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
Complete set of items that share at least one property in common
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Statistical_population
Methods employed to reduce error in science tests
A scientific control is an element of an experiment or observation designed to minimize the influence of variables other than the independent variable
Scientific_control
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
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)
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
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
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
Type of study based on universal sampling
cross-sectional studies typically involve the use of cross-sectional regression, in order to sort out the existence and magnitude of causal effects of
Cross-sectional_study
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
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
Approach of analyzing data sets in statistics
size of the party. The primary analysis task is approached by fitting a regression model where the tip rate is the response variable. The fitted model is
Exploratory_data_analysis
Distinction between nominal, ordinal, interval and ratio variables
3.398. Mosteller, Frederick; Tukey, John W. (1977). Data analysis and regression: a second course in statistics. Reading, Mass: Addison-Wesley Pub. Co
Level_of_measurement
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
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
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
Concept in probability theory and statistics
for including other right-side variables in a multiple regression; but while multiple regression gives unbiased results for the effect size, it does not
Partial_correlation
Statistic which divides a data set into 100 parts and analyzes it as a percentage
comparison to national median and other percentiles which are found in growth charts. The 85th percentile speed of traffic on a road is often used as a guideline
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
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
Form of scientific experiment
the data and include: For dichotomous (binary) outcome data, logistic regression (e.g., to predict sustained virological response after receipt of peginterferon
Randomized_controlled_trial
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
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
Type of average of a collection of numbers
squares and regression analysis Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard
Arithmetic_mean
Statistical method that summarizes and/or integrates data from multiple sources
have been executed using Bayesian methods, mixed linear models and meta-regression approaches. Specifying a Bayesian network meta-analysis model involves
Meta-analysis
Test statistic
when using OLS regression gretl: Automatically calculated when using OLS regression Stata: the command estat dwatson, following regress in time series
Durbin–Watson_statistic
Generates a forecast of future values of a time series
t-1})^{2}=\sum _{t=1}^{T}e_{t}^{2}} Unlike the regression case (where we have formulae to directly compute the regression coefficients which minimize the SSE) this
Exponential_smoothing
Distribution of an uncertain quantity
hdl:20.500.11850/547969. S2CID 234681651. Congdon, Peter D. (2020). "Regression Techniques using Hierarchical Priors". Bayesian Hierarchical Models (2nd ed
Prior_probability
Algorithmically generated data that have a similar distribution as sampled data
with missing data. Similarly, they developed the technique of Sequential Regression Multivariate Imputation. Researchers test the framework on synthetic data
Synthetic_data
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
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REGRESSION CONTROL-CHART
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