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Sum of squares
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121	Package ‘leaps’ July 2, 2014 Title regression subset selection Version 2.9 Author Thomas Lumley using Fortran code by Alan Miller Description Regression subset selection including exhaustiv Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 12:17:09 Matrices Estimation theory Parametric statistics Linear regression Matrix Design matrix Residual sum of squares R Degrees of freedom Statistics Regression analysis Econometrics
122	Data-Based Models for Global Temperature Variations Bert W. Rust and Add to Reading List Source URL: math.nist.gov Language: English - Date: 2009-09-28 11:10:44 Coefficient of determination Linear regression Spline Residual sum of squares Errors and residuals in statistics Durbin–Watson statistic Smoothing spline Statistics Regression analysis Least squares
123	Microsoft Word - Fitting Equations.doc Add to Reading List Source URL: biometrics.forestry.ubc.ca Language: English - Date: 2013-06-11 13:13:08 Estimation theory Econometrics Parametric statistics Residual sum of squares Errors and residuals in statistics Simple linear regression Linear regression Coefficient of determination Variance Statistics Regression analysis Least squares
124	Microsoft PowerPoint - Day3Afternoon-UpdatedMay23.ppt Add to Reading List Source URL: www.waterboards.ca.gov Language: English - Date: 2011-08-11 14:31:00 Simple linear regression Linear regression Residual sum of squares Least squares Coefficient of determination T-statistic Variance Prediction interval Correlation and dependence Statistics Regression analysis Errors and residuals in statistics
125	Basis selection for SOS programs via facial reduction and polyhedral approximations Frank Permenter1 Abstract— We develop a monomial basis selection procedure for sums-of-squares (SOS) programs that is based on facial Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2014-07-15 10:53:14 Homogeneous polynomials Polynomials Mathematical optimization Sum-of-squares optimization Monomial Positive polynomial Elementary symmetric polynomial Symmetric polynomial Mathematics Algebra Abstract algebra
126	92 CHAPTER 4. PREDICTIVE MODELS defines a probability space for which (X, E, P ) is a Bayesian network. Furthermore, P (Xi \|C(Xi)) is either 0 or fi (Xi ). Here C(Xi ) denotes the parents (or direct causes) of Xi . Add to Reading List Source URL: datamining.anu.edu.au Language: English - Date: 2011-05-31 01:27:15 Mean squared error Variance Errors and residuals in statistics Gauss–Markov theorem Bias of an estimator Multivariate adaptive regression splines Residual sum of squares Linear regression Ordinary least squares Statistics Regression analysis Least squares
127	[removed]Lecture 4: Simple Linear Regression[removed] Add to Reading List Source URL: www.stat.cmu.edu Language: English - Date: 2014-09-04 09:46:06 Coefficient of determination Linear regression Errors and residuals in statistics Variance Simple linear regression Least squares Statistics Regression analysis Residual sum of squares
128	Odd squares There are many different ways to classify numbers. One way is by using shapes. The sum of consecutive odd numbers forms square numbers. Use the chart below to illustrate this interesting math truth. # Square Add to Reading List Source URL: mathflix.luc.edu - Date: 2013-03-12 22:44:35
129	Watson Lake Limno-corral Study: Phase II Final David Walker, PhD University of Arizona, Environmental Research Laboratory [removed[removed] Add to Reading List Source URL: www.azdeq.gov Language: English - Date: 2014-04-07 15:18:10 Regression analysis Least squares Analysis of variance Variance Sum of squares Errors and residuals in statistics Mean Statistics Design of experiments Statistical tests
130	PDF Document Add to Reading List Source URL: www.nature.nps.gov Language: English - Date: 2008-11-06 17:43:00 Least squares Statistical tests Design of experiments Standard error Sum of squares Mean Normal distribution Errors and residuals in statistics Variance Statistics Regression analysis Analysis of variance