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	Explanatory variable - Wikipedia, the free encyclopedia
		In statistics, an explanatory variable (also regressor or independent variable) is a variable in a regression model which appears on the right hand side of the equation.
		When graphing, the explanatory variable is plotted on the X-axis.
		Its function is to explain the evolution of the response or dependent variable.
	en.wikipedia.org /wiki/Regressor (86 words)

	Instrumental variable - Wikipedia, the free encyclopedia
		In statistics, an instrumental variable (IV, or instrument) can be used in regression analysis to produce a consistent estimator when the explanatory variables (covariates) are correlated with the error terms.
		An instrument is a variable that does not itself belong in the regression, that is correlated with the suspect explanatory variable, and that is uncorrelated with the error term.
		An instrumental variable is one that is correlated with the independent variable but not with the error term.
	en.wikipedia.org /wiki/Instrumental_variable (843 words)

	PlanetMath: general linear model (Site not responding. Last check: 2007-10-17)
		The response variable is considered random, where as the explanatory variable(s) may or may not be random.
		A linear regression model is a special case of the general linear model where all explanatory variables are assumed to be continuous.
		Analysis of variance model, or ANOVA, is another special case of the general linear model, where all of the explantory variables are categorical in nature (for example, gender, marital status, etc..).
	planetmath.org /encyclopedia/AnalysisOfCovariance.html (338 words)

	PlanetMath: data types in statistics (Site not responding. Last check: 2007-10-17)
		Each column in the matrix represents a data variable (slightly different from the mathematical definition of a variable), and each row respesents an observation or outcome, in which case only one data variable is involved, or a vector of observations or outcomes where several data variables are involved.
		Conversely, to turn a categorical variable into a continuous one, either the method of extension or transformation, or both, are used.
		Variable AccSt is not a quantitative variable even though it is numeric in nature, since its values have no intrinsic numerical meanings.
	planetmath.org /encyclopedia/ExplanatoryVariable.html (914 words)

	eco310regress
		Ordinarily data on the dependent variable and the explainers are unavailable for everyone in the population, hence the population regression coefficients are unknown.
		Because the magnitude of the estimated coefficients reflects the magnitudes of the explanatory variables, we cannot determine which explanatory variables have the greatest influence on the dependent variable simply by examining the size of the estimated coefficients.
		If only the explanatory variable is logged (i.e., the log of X1 is used to explain the behavior of Y), then the estimated coefficients on the logged explainers measure the change in Y if the explainer changes by 1 proportionate unit (which is +100%).
	www.iona.edu /faculty/rjantzen/eco310/eco310regress.htm (2779 words)

	An Evaluability Assessment of Responsible Fatherhood Programs: EXPLANATORY VARIABLES (Site not responding. Last check: 2007-10-17)
		There are several reasons for using explanatory variables in multivariate models, and an understanding of these reasons is helpful in determining the value of collecting data for explanatory variables in a specific evaluation, and the types of data to be collected.
		The variables chosen for inclusion as explanatory variables in a multivariate model should be factors that vary across fathers in the sample and that are believed to influence or "explain" differences in the outcome being estimated.
		Demographic variables such as age and race/ethnicity allow the evaluator to describe the characteristics of fathers who participate in both the treatment and control groups, ensure that the two groups are comparable, and, if not, control for the differences by including demographic characteristics as explanatory variables in the multiple regression model.
	aspe.hhs.gov /search/fatherhood/htdocs/evaluaby/chapter5.htm (2391 words)

	Untitled
		This impact of a unit change in one explanatory variable on the dependent variable of a nonlinear model is difficult to determine since the change in the predicted value is conditional on all of the other variables for that observation.
		In this paper, we present an alternative approach to evaluating the marginal effects of a unit change in the explanatory variables which uses individual observations to calculate the change in the predicted value brought about by a unit change in one of the explanatory variables to illustrate the variable's distributional impact on the dependent variable.
		It is also interesting to note that the impact of a unit change in these two explanatory variables on the probability of cancer remission produces a sigmoidal shape, which is characteristic of the logit function, despite the fact that the explanatory variable varies across observations and that the YDIFF variable.
	www.nd.edu /~meg/LSR/SAS/MCGLYNN.html (1800 words)

	Glossary
		In regression, the independent variable is the one that is supposed to explain the other; the term is a synonym for "explanatory variable." Usually, one regresses the "dependent variable" on the "independent variable." There is not always a clear choice of the independent variable.
		A probability histogram for a random variable is analogous to a histogram of data, but instead of plotting the area of the bins proportional to the relative frequency of observations in the class interval, one plots the area of the bins proportional to the probability that the random variable is in the class interval.
		(the sample variance) is an unbiased estimator of the square of the SD of the population (the variance of the population).
	www.stat.berkeley.edu /~stark/SticiGui/Text/gloss.htm (13846 words)

	UCLA; Economics 143; Cameron; Research Proposal
		Explanatory variables include a female dummy, years of education, whether it is a big plane,...
		Explanatory variables: whether this is a house or a condo, number of floor, family yearly income,...price of an alarm system...number of additional functions in an alarm system.
		Explanatory variables include: family economic status, number of parents in household, number of siblings, etc. RHS variables all pertain to individual college-aged Mexican Americans, yet dependent variable is total number at university level.
	www.sscnet.ucla.edu /ssc/labs/cameron/e143w98/bloop97.htm (3391 words)

	Technical Brief on the Statistical Model
		The correlation between the response variables and the explanatory variables is shown in Table 2.
		The study of multiple explanatory variables interacting simultaneously to produce the outcome on one or many response variables is termed Multivariate Analysis.
		The variable that explains the most variance is the better predictor of the variance in the dependent variable.
	www.infoworks.ride.uri.edu /2002/techbrief/appendix-A.asp (1837 words)

	Linear Regression
		One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
		A valuable numerical measure of association between two variables is the correlation coefficient, which is a value between -1 and 1 indicating the strength of the association of the observed data for the two variables.
		Since such a variable might be a factor of time (for example, the effect of political or economic cycles), a time series plot of the data is often a useful tool in identifying the presence of lurking variables.
	www.stat.yale.edu /Courses/1997-98/101/linreg.htm (1057 words)

	Partial Leverage Plots (Site not responding. Last check: 2007-10-17)
		When there is more than one explanatory variable in a model, the relationship of the residuals to one explanatory variable can be obscured by the effects of other explanatory variables.
		In each plot in Figure 14.9, the x-axis represents the residuals of the explanatory variable from a model that regresses that explanatory variable on the remaining explanatory variables.
		The y-axis represents the residuals of the response variable calculated with the explanatory variable omitted.
	www.asu.edu /it/fyi/unix/helpdocs/statistics/sas/sasdoc/sashtml/insight/chap14/sect10.htm (273 words)

	GAUSS Programming for Econometricians: Chapter VII (Site not responding. Last check: 2007-10-17)
		Instrumental variable estimation for a lagged dependent variable model is the focus of Lesson 27.
		To handle a lagged dependent variable model estimation with instrumental variables, LSQ implements an estimation technique which use the current and lagged explanatory variables as the instruments for the lagged dependent variable.
		If longer lags of the dependent variable are adopted or an autocorrelated error structure is identified, instrumental variables may need to include more lags of the explanatory variables as well.
	eclab.econ.pdx.edu /gpe/chap8.htm (1406 words)

	SSRN-Liberate Your Regressions: Increasing Information from Explanatory Variables by Recognizing and Discarding ... (Site not responding. Last check: 2007-10-17)
		The explanatory variables in empirical research are very often aggregate variables (sums of observable component variables).
		Use of an aggregate variable as an explanatory variable corresponds to invisible restrictions in the regression forcing the coefficients of the basic component variables to be equal.
		That is, the components of these variables have different effects on the explained variable and the invisible restrictions force the coefficients the dissimilar components to be equal.
	papers.ssrn.com /sol3/papers.cfm?abstract_id=301393 (636 words)

	Cochran-Armitage Test for Trend
		The trend test is based upon the regression coefficient for the weighted linear regression of the binomial proportions on the scores of the levels of the explanatory variable.
		For character variables, the table scores for the row variable are the row numbers (for example, 1 for the first row, 2 for the second row, and so on).
		When you perform the trend test, the explanatory variable may be numeric (for example, dose of a test substance), and these variable values may be appropriate scores.
	www.okstate.edu /sas/v7/sashtml/books/stat/chap26/sect24.htm (513 words)

	[No title]
		Assignment requirements: Discuss the model: the dependent and independent variables you will use Discuss expected relationships between the dependent and the independent variables Estimate the regression [Directions on running a regression in SPSS and Excel are at the end of this assignment] Answer the discussion questions I.
		The model: the dependent and independent variables Pick one dependent variable from the data set and two or three independent (or explanatory) variables.
		The explanatory variables can be quantitative, or dummy variables (that is, two categories coded as a 1 or 0), but not categorical variables with more than two categories.
	courses.washington.edu /pbafdl/528/pdf/assign2_2004.doc (997 words)

	Ordination Methods - an Overview
		Although both multiple regression and CCA find the best linear combination of explanatory variables, they are not guaranteed to find the true underlying gradient (which may be related to unmeasured or unmeasurable factors), nor are they guaranteed to explain a large portion of variation in the data.
		Figure 14 is a triplot of a CCA for the forested vegetation of the Tallgrass Prairie Preserve in Osage County, Oklahoma.
		Dummy variables are useful if you have discrete experimental treatments, year effects, different bedrock types, or in the case of the bryophyte example (Table 1), host tree species.
	ordination.okstate.edu /overview.htm (8684 words)

	[No title]
		The objective of the analysis is to determine if the mean of the response variable is different for different levels of the explanatory variable.
		Also, produce simultaneous confidence intervals that compare the mean response variable for the different levels of the explanatory variable, with lines connecting the means that are not significantly different.
		Produce simultaneous confidence intervals that compare the mean response variable for the different levels of the explanatory variable, with lines connecting the means that are not significantly different.
	www-personal.umich.edu /~bobwolfe/503/lab/lab10.doc (1214 words)

	Using MINITAB to do Regression (Site not responding. Last check: 2007-10-17)
		Consider C2 (Years Employed) as the explanatory variable and C1 (Salary) as the response variable and generate a scatterplot.
		Calculate the regression equation from the regression of Y (the response variable) on X (the explanatory variable).
		Double-click the explanatory variable for the horizontal axis.
	www.siu.edu /~epse1/leitner/e402/RegUsingMinitab.html (407 words)

	Panel Data Estimators for Nonseparable Models with Endogenous Regressors
		The basic idea is to first estimate the slope of the mean of the dependent variable conditional on both the explanatory variable and z and then undo the effect of conditioning on z by taking the average of the slope over the distribution of z conditional on a particular value of the explanatory variable.
		A shift in the value of an explanatory variable for member 1 of a group has both a direct effect on the distribution of the dependent variable for member 1 and an indirect effect through the distribution of the error.
		We isolate the direct effect by comparing the effect of the explanatory variable on the distribution of the dependent variable for member 1 to its effect on the distribution for the other panel members.
	ideas.repec.org /p/nbr/nberte/0267.html (757 words)

	ASSIGNMENT #5
		- interpretations of the coefficients of the significant explanatory variables.
		Select the response variable and all of the explanatory variables and place them in the “Data” box. Click OK.
		The graph that appears on the right is the pairwise scatter plot of each variable versus each other variable.
	oregonstate.edu /instruct/st352/kollath/assignments/assign4.htm (1175 words)

	[No title] (Site not responding. Last check: 2007-10-17)
		Choose the explanatory variable with the highest squared correlation with the response variable and include this variable in the model if its p-value is less than 0.05 in simple linear regression.
		Among the explanatory variables that have not been entered in the model, choose the remaining one with the highest squared correlation with the residuals.
		The F ratio for a variable X_j that has not been included in the model is the F statistic for testing the reduced model that includes only the variables already included in the model versus the full model that includes variable X_j in addition to the variables that have already been included in the model.
	www-stat.wharton.upenn.edu /~stat102/Sections/modelbuilding.doc (863 words)

	MODEL Statement
		The MODEL statement identifies the variables to be used as the failure time variables, the optional censoring variable, and the explanatory variables.
		The variables following the equal sign are the explanatory variables (sometimes called independent variables or covariates) for the model.
		specifies the name of an offset variable, which is an explanatory variable with a regression coefficient fixed as one.
	www.asu.edu /sas/sasdoc/sashtml/stat/chap49/sect9.htm (1774 words)

	ST 352
		If the null hypothesis is not rejected, stop the analysis!!! None of the explanatory variables are useful in predicting the response, so there is no need to continue.
		Continue until all p-values are less than.05. The variables left are the significant predictors of the response variable and the regression equation should be formed from model that includes only these explanatory variables.
		: the percentage of the variation in the response variable that is explained by the regression on the explanatory variables.
	oregonstate.edu /instruct/st352/kollath/handouts/multiplereg/multipleregsteps.htm (643 words)

	[No title]
		When you find a variable (or variables) that is a potential for your term paper, check to see what years the question was asked.
		When you find a variable that seems appropriate, check to see that the question was asked in the same year(s) as your outcome variable (see instructions in step 1).
		For example, if you want to examine trends in attitudes towards abortion over time, year would be the appropriate explanatory variable, but you might also want to see how those trends have differed for men and women.
	www.bsos.umd.edu /socy/smartin/202/202lab05.doc (1450 words)

	[No title] (Site not responding. Last check: 2007-10-17)
		The proportion of the variability that is explained by the regression model is 1 — the proportion that is unexplained.
		Plot of residual distances vs. explanatory variable Same as original plot rotated so regression line is horizontal and rescaledresistant04-3A measure whose value is relatively unaffected by outliers.
		Put explanatory variables in columns, response variables in rows.variability01-2Values assumed by variables differ from one observational unit to the next.
	www.stolaf.edu /people/huberty/Stat110/glossaryblankunit3.doc (1472 words)

	Fitness
		Evolution by random heritable variation and natural selection will explain ever increasing adaptation to given environments, increasing diversity in the occupation of new environments, and the complexity of organisms and their parts as their lineages adapt to one another and to their environments.
		These concepts have explanatory power even though they are defined in terms of causes and effects from which they are nevertheless distinct, and are realized by the molecular properties of matter on which they supervene.
		The charge of tautology against the theory thus rests on the mistaken demand that an explanatory variable must always be defined in terms distinct from its causes and effects.
	plato.stanford.edu /entries/fitness (4660 words)

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