###### Regressionanalysis
Applied Regression Analysis (3rd ed.). John Wiley. ISBN 0-471-17082-8. Fox, J. (1997). Applied Regression Analysis, Linear ... Analysis of Variance," pp. 541-554. Lindley, D.V. (1987). "Regression and correlation analysis," New Palgrave: A Dictionary of ... In all cases, a function of the independent variables called the regression function is to be estimated. In regression analysis ... Grey System Theory, from which stems Grey relational analysis, has its own method to conduct regression analysis using grey ...
###### Outline of regressionanalysis
... regression Isotonic regression Semiparametric regression Local regression Total least squares regression Deming regression ... The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis - use of ... Simple linear regression Trend estimation Ridge regression Polynomial regression Segmented regression Nonlinear regression ... Regression analysis Linear regression Least squares Linear least squares (mathematics) Non-linear least squares Least absolute ...
###### Projection matrix
ISBN 0-471-93412-7. Draper, N. R.; Smith, H. (1998). Applied Regression Analysis. Wiley. ISBN 0-471-17082-8. Amemiya, Takeshi ( ... A few examples are linear least squares, smoothing splines, regression splines, local regression, kernel regression, and linear ... Practical applications of the projection matrix in regression analysis include leverage and Cook's distance, which are ... which allows one to analyze the effects of adding an intercept term to a regression. Another use is in the fixed effects model ...
###### Coefficient of determination
Glantz, Stanton A.; Slinker, B. K. (1990). Primer of Applied Regression and Analysis of Variance. McGraw-Hill. ISBN 0-07-023407 ... In regression, the R2 coefficient of determination is a statistical measure of how well the regression line approximates the ... Applied Regression Analysis. Wiley-Interscience. ISBN 0-471-17082-8. Devore, Jay L. (2011). Probability and Statistics for ... In least squares regression, R2 is weakly increasing with increases in the number of regressors in the model. Because increases ...
###### Grey box model
These have special analysis methods. In particular linear regression techniques are much more efficient than most non-linear ... Draper, Norman R.; Smith, Harry (25 August 2014). Applied Regression Analysis. John Wiley & Sons. pp. 657-. ISBN 978-1-118- ... This relation can be specified as q = Ac where A is a matrix of unknown coefficients, and c as in linear regression includes a ... Then the more efficient linear regression can be used to predict q using c thus selecting the non-zero values in A and ...
###### Homogeneity (statistics)
See regression analysis. The initial stages in the analysis of a time series may involve plotting values against time to ... There should then be a later stage of analysis to examine whether the errors in the predictions from the regression behave in ... The technic of homogeneous tests compared with some aspects of scale analysis and factor analysis. Psychological Bulletin, 45, ... In meta-analysis, which combines the data from several studies, homogeneity measures the differences or similarities between ...
###### Gender pay gap
A Meta-Regression Analysis". The Journal of Human Resources. 33 (4): 962. doi:10.2307/146404. JSTOR 146404. Manning, Alan (2006 ... "An Analysis of Reasons for the Disparity in Wages Between Men and Women" (PDF). Consad.com. Archived from the original (PDF) on ... Another meta-analysis of 41 empirical studies on the wage gap performed in 1998 found a similar time trend in estimated pay ... "Analysis , Here Are the Facts behind That '79 Cent' Pay Gap Factoid". The Washington Post, WP Company, 14 April 2016 Mooney ...
###### General linear model
1998). "Applied Regression Analysis". Springer Texts in Statistics. doi:10.1007/b98890. ISBN 0-387-98454-2. ... Multiple linear regression is a generalization of linear regression by considering more than one independent variable, and a ... ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression model ... The basic model for linear regression is Y i = β 0 + β 1 X i 1 + β 2 X i 2 + … + β p X i p + ϵ i . {\displaystyle Y_{i}=\beta ...
###### Multicollinearity
Lipovestky; Conklin (2001). "Analysis of Regression in Game Theory Approach". Applied Stochastic Models and Data Analysis. 17 ( ... A principal danger of such data redundancy is that of overfitting in regression analysis models. The best regression models are ... Ridge regression or principal component regression or partial least squares regression can be used. If the correlated ... Chatterjee, S.; Hadi, A. S.; Price, B. (2000). Regression Analysis by Example (Third ed.). John Wiley and Sons. ISBN 0-471- ...
###### Overfitting
Austin, P. C.; Steyerberg, E. W. (2015). "The number of subjects per variable required in linear regression analyses". Journal ... Draper, Norman R.; Smith, Harry (1998). Applied Regression Analysis (3rd ed.). Wiley. ISBN 978-0471170822. Jim Frost (2015-09- ... In the process of regression model selection, the mean squared error of the random regression function can be split into random ... For logistic regression or Cox proportional hazards models, there are a variety of rules of thumb (e.g. 5-9, 10 and 10-15 - the ...
###### Logistic distribution
Ritzema (ed.), H.P. (1994). Frequency and Regression Analysis (PDF). Chapter 6 in: Drainage Principles and Applications, ... where the logistic distribution plays the same role in logistic regression as the normal distribution does in probit regression ... One of the most common applications is in logistic regression, which is used for modeling categorical dependent variables (e.g ... The rainfall data are represented by plotting positions as part of the cumulative frequency analysis. Τhe United States Chess ...
###### Partial leverage
Applied Regression Analysis (3rd ed.). John Wiley. Cook and Weisberg (1982). Residuals and Influence in Regression. Chapman and ... In regression analysis, partial leverage is a measure of the contribution of the individual independent variables to the ... Belsley, Kuh, and Welsch (1980). Regression Diagnostics. John Wiley. CS1 maint: Multiple names: authors list (link) Paul ... the partial leverage is a measure of how hi changes as a variable is added to the regression model. The partial leverage is ...
###### Quantitative genetics
Such coefficients are used particularly in regression analysis. A standardized version of regression analysis is path analysis ... Path analysis includes partial correlations as well as partial regression coefficients (the latter are the path coefficients). ... Draper, Norman R.; Smith, Harry (1981). Applied regression analysis (Second ed.). New York: John Wiley & Sons. ISBN 0 471 02995 ... Following the principles of standardized partial regression, of which path analysis is a pictorially oriented version, Sewall ...
###### Partial residual plot
Applied Regression Analysis (3rd ed.). John Wiley. Cook and Weisberg (1982). Residuals and Influence in Regression. Chapman and ... Partial regression plot Partial leverage plot Variance inflation factors for a multi-linear fit. Tom Ryan (1997). Modern ... When performing a linear regression with a single independent variable, a scatter plot of the response variable against the ... Belsley, Kuh, and Welsch (1980). Regression Diagnostics. John Wiley. CS1 maint: Multiple names: authors list (link) Paul ...
###### Poisson distribution
Poisson regression and negative binomial regression are useful for analyses where the dependent (response) variable is the ... A. Colin Cameron; Pravin K. Trivedi (1998). Regression Analysis of Count Data. Retrieved 2013-01-30. (p.5) The law of rare ... 2005). Bayesian Data Analysis (2nd ed.). p. 60. Clevenson, M. L.; Zidek, J. V. (1975). "Simultaneous Estimation of the Means of ... On pages 23-25, Bortkiewicz presents his famous analysis of "4. Beispiel: Die durch Schlag eines Pferdes im preussischen Heere ...
###### Poisson regression
In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and ... Poisson regression creates proportional hazards models, one class of survival analysis: see proportional hazards models for ... 1] Cameron, A. C.; Trivedi, P. K. (1998). Regression analysis of count data. Cambridge University Press. ISBN 0-521-63201-3. ... Negative binomial regression is a popular generalization of Poisson regression because it loosens the highly restrictive ...
###### James Durbin
Durbin, J.; Watson, G. S. (1950). "Testing for Serial Correlation in Least Squares Regression: I". Biometrika. Biometrika Trust ... Durbin, J. (2012). Time Series Analysis by State Space Methods. Oxford Statistical Science Series. Oxford University Press. ... Time Series Analysis by State Space Methods, was published by Oxford University Press in May 2012. His last books were co- ... known particularly for his work on time series analysis and serial correlation. The son of a greengrocer, Durbin was born in ...