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Topic: Regression analysis

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	PA 765: Multiple Regression
		Multiple regression shares all the assumptions of correlation: linearity of relationships, the same level of relationship throughout the range of the independent variable ("homoscedasticity"), interval or near-interval data, absence of outliers, and data whose range is not truncated.
		Cubic regression splines operate similar to local polynomial regression, but a constraint is imposed that the regression line in a given bin must join to the start of the regression line in the next bin, thereby avoiding discontinuities in the curve, albeit by increasing error a bit.
		Local regression fits a regression surface not for all the data points as in traditional regression, but for the data points in a "neighborhood." Researchers determine the "smoothing parameter," which is a specified percentage of the sample size, and neighborhoods are the points within the corresponding radius.
	www2.chass.ncsu.edu /garson/pa765/regress.htm (19411 words)

	DSS - Introduction to Regression
		Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables.
		Usually, regression analysis is used with naturally-occurring variables, as opposed to experimentally manipulated variables, although you can use regression with experimentally manipulated variables.
		The purpose of regression analysis is to come up with an equation of a line that fits through that cluster of points with the minimal amount of deviations from the line.
	dss.princeton.edu /online_help/analysis/regression_intro.htm (3860 words)

	Regression Analysis
		Regression analysis is used to model the relationship between a response variable and one or more predictor variables.
		Regression Analysis for Proportions - fits logistic and probit models for binary response data.
		If the number of predictors is not excessive, it is possible to fit regression models involving all combinations of 1 predictor, 2 predictors, 3 predictors, etc, and sort the models according to a goodness-of fit statistic.
	www.statgraphics.com /regression_analysis.htm (993 words)

	Regression Analysis (Site not responding. Last check: )
		Regression analysis is one of the most commonly used statistical techniques in social and behavioral sciences as well as in physical sciences.
		In the particular case when the curve is a straight line, it is called the line of regression and the regression is said to be linear.
		observation, a is the intercept and b is the regression coefficient.
	www.unesco.org /webworld/idams/advguide/Chapt4_4.htm (738 words)

	Regression Analysis: Statistics Help and Statistical Analysis Services
		Simple linear regression is when you have one dependent variable (also known as an outcome, or response variable) and one independent variable (also known as a predictor or explanatory variable).
		One purpose of linear regression analysis is to predict a dependent variable.
		The regression analysis procedure tests the null hypothesis that the slope parameter of the independent variable is 0 versus the alternative hypothesis that the slope parameter is different than 0.
	www.statisticallysignificantconsulting.com /RegressionAnalysis.htm (628 words)

	POWERMUTT: Regression Analysis
		It is called regression analysis because Francis Galton (1822-1911), a pioneer in the application of OLS to the behavioral sciences, used it to study “regression toward the mean.”[2] Regression analysis is a simple but extremely powerful technique with a wide variety of applications.
		Because it expresses relationships in terms of the original units of analysis, the unstandardized equation is often easier to understand.
		In an unstandardized regression equation in which the dependent variable is a “feeling thermometer” for Hillary Clinton, a “b” coefficient of 8.106 associated with gender would mean that, all else being equal, women rate her a little more than 8 points higher than do men.
	www.csupomona.edu /~jlkorey/POWERMUTT/Topics/regression.html (3362 words)

	Introduction to Regression Analysis
		The goal of regression analysis is to determine the values of parameters for a function that cause the function to best fit a set of data observations that you provide.
		The regression analysis performed by NLREG will determine the best values of the three parameters, price, the estimated value when age is 0 (i.e., when the car was new), depage, the depreciation that takes place each year, and depmiles, the depreciation for each mile driven.
		The goal of regression analysis is to determine the values of the parameters that minimize the sum of the squared residual values for the set of observations.
	www.nlreg.com /intro.htm (1015 words)

	Multiple correlation - Wikipedia, the free encyclopedia
		In statistics, regression analysis is a method for explanation of phenomena and prediction of future events.
		In the regression analysis, a coefficient of correlation r between random variables X and Y is a quantitative index of association between these two variables.
		An intuitive approach to the multiple regression analysis is to sum the squared correlations between the predictor variables and the criterion variable to obtain an index of the over-all relationship between the predictor variables and the criterion variable.
	en.wikipedia.org /wiki/Multiple_correlation (474 words)

	Regression-Discontinuity Analysis (Site not responding. Last check: )
		The regression coefficient associated with the Z term (i.e., the group membership variable) is the estimate of the main effect of the program.
		To accomplish this one should certainly examine the output of the regression analysis in step 4 noting the degree to which the overall model fits the data, the presence of any insignificant coefficients and the pattern of residuals.
		Model specification procedures which involve dropping any term at any stage of the analysis are more dangerous and more likely to yield biased estimates because of the considerable multicolinearity which will exist between the terms in the model.
	www.socialresearchmethods.net /kb/statrd.htm (2879 words)

	GodMoon.com - Multiple Regression Analysis and Forecasting - Multiple Regression Analysis and Forecasting...
		GodMoon.com - Multiple Regression Analysis and Forecasting - Multiple Regression Analysis and Forecasting...
		The Multiple Regression Analysis and Forecasting model provides a solid basis for identifying value drivers and forecasting business plan data.
		While it utilises a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use.
	godmoon.com /Multiple-Regression-Analysis-and-Forecasting.htm (143 words)

	Regression Analysis
		Regression analysis “fits” or derives a model that describes the variation of a response (“dependent”) variable as a function of one or more
		where the systematic component is predictable or explainable by the predictor variables, and is represented by the regression model, while the irregular component is regarded as “noise” or prediction errors
		is the “regression sum of squares” or that component of the total sum of squares “explained” by the regression equation, and
	geography.uoregon.edu /GeogR/topics/regression1.htm (492 words)

	3.1 Correlation & Regression Analysis
		Regression analysis can be thought of as being sort of like the flip side of correlation.
		So, anytime that you see a regression equation with one or more variables that looks like this, no matter what the variables are, you can plug in the values of independent variables, multiply, and get the predicted values, in this case, the infant's expected weight.
		You use or control for multiple variables in that sort of case if you use a weight table, and the results of the regression analysis are included in the table.
	www.uth.tmc.edu /uth_orgs/educ_dev/oser/L3_1.HTM (3582 words)

	Regression analysis - Wikipedia, the free encyclopedia
		If the relationship between the variables being analyzed is not linear in parameters, a number of nonlinear regression techniques may be used to obtain a more accurate regression.
		If the regression error is not normally distributed but is assumed to come from an exponential family, generalized linear models should be used.
		MAP methods are related to Occam's Razor: there is a preference for simplicity among a family of regression models (curves) just as there is a preference for simplicity among competing theories.
	en.wikipedia.org /wiki/Regression_analysis (1222 words)

	Multiple Regression
		The general purpose of multiple regression (the term was first used by Pearson, 1908) is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable.
		Once this so-called regression line has been determined, the analyst can now easily construct a graph of the expected (predicted) salaries and the actual salaries of job incumbents in his or her company.
		The general computational problem that needs to be solved in multiple regression analysis is to fit a straight line to a number of points.
	www.statsoft.com /textbook/stmulreg.html (2189 words)

	Regression Analysis
		The goal of regression analysis is to obtain estimates of the unknown parameters Beta_1,..., Beta_K which indicate how a change in one of the independent variables affects the values taken by the dependent variable.
		In economics, the dependent variable might be a family's consumption expenditure and the independent variables might be the family's income, number of children in the family, and other factors that would affect the family's consumption patterns.
		Denote the corresponding estimate of sigma^2 with the ith observation deleted by s^2 (i) and the corresponding diagonal element of the hat matrix from the regression with the ith observation deleted by h_i tilde.
	elsa.berkeley.edu /sst/regression.html (2389 words)

	Regression Analysis
		The goal of regression analysis is to obtain estimates of the unknown parameters Beta_1,..., Beta_K which indicate how a change in one of the independent variables affects the values taken by the dependent variable.
		Since the residuals from a regression will generally not be independently or identically distributed (even if the disturbances in the regression model are), it is advisable to weight the residuals by their standard deviations (this is what is meant by
		Denote the corresponding estimate of sigma^2 with the ith observation deleted by s^2 (i) and the corresponding diagonal element of the hat matrix from the regression with the ith observation deleted by h_i tilde.
	emlab.berkeley.edu /sst/regression.html (2389 words)

	Regression Analysis Features Available in NCSS
		Regression analysis refers to a group of techniques for studying the relationships among two or more variables.
		You can perform a regression analysis with modern graphical and numeric residual analysis.
		Major options include multiple regression, stepwise regression, correlation matrix, residual analysis, robust regression, all-possible regressions, response surface regression, Poisson regression, Cox regression, ridge regression, Weibull regression, and logistic regression.
	www.ncss.com /regression.html (251 words)

	Multiple Regression Analysis and Forecasting
		The Multiple Regression Analysis and Forecasting template provides a solid basis for identifying value drivers and forecasting time series data.
		Find more Excel Regression Analysis and Forecasting Solutions in the Business Finance Section of the Excel Add-ins and Templates Directory.
		Share experiences with the Multiple Regression Analysis and Forecasting template in the Multiple Regression Analysis and Forecasting Section of the Excel Help Forums for Business.
	www.business-spreadsheets.com /regfor.htm (239 words)

	NLREG -- Nonlinear Regression Analysis Program
		NLREG performs true nonlinear regression analysis and curve fitting, it does not transform the function into a linear form.
		In addition to performing classic nonlinear regression, NLREG can be used to find the root or minimum value of a general multivariate, nonlinear function.
		It can also be used in a special form where the independent variable is omitted; an interesting application of this is "circular regression" where a circle is fitted to a set of data points.
	www.nlreg.com (613 words)

	Regression Analysis (Site not responding. Last check: )
		For a i686/Linux-ELF platform, no tests failed since this is the v6.2.1 regression testing reference platform.
		These differences in messages will result in a "failed" regression test which can be validated by inspection.
		If you run the regression test on a non-virgin database or run it multiple times, the OID's reported will have different values.
	w3.gazi.edu.tr /~ertan/sql/c2604.htm (797 words)

	eBooks.com - Applied Regression Analysis eBook
		A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis.
		Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers.
		It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course.Applied Regression Analysis emphasizes the concepts and the analysis of data sets.It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression.
	www.ebooks.com /cj.asp?IID=219439 (493 words)

	SPSS Regression Models \| Logistic Regression Analysis (Site not responding. Last check: )
		SPSS Regression Models enables you to apply more sophisticated models to your data using its wide range of nonlinear regression models.
		Probit analysis: Evaluate the value of stimuli using a logit or probit transformation of the proportion responding
		SPSS Regression Models is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian.
	www.spss.com /regression (246 words)

	Multiple Regression Analysis and Forecasting
		While it utilises a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use.
		Find more Excel Regression Analysis and Forecasting Solutions in the Business Finance Section of the Excel Add-ins and Templates Directory.
		Share experiences with the Multiple Regression Analysis and Forecasting template in the Multiple Regression Analysis and Forecasting Section of the Excel Help Forums for Business.
	www.excelbusinesstools.com /regfor.htm (239 words)

	Excel - Multiple Regression Analysis and Forecasting. Excel trial free download.
		Multiple Regression Analysis and Forecasting 1.0 Producing statistically sound value driver identification for forecasting data
		Software.com - Multiple Multiple Regression Analysis and Forecasting
		Installgen.com - Plan Multiple Regression Analysis and Forecasting
	trialfreedownload.com /Excel.htm (575 words)

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