Transcription of Title stata.com regress — Linear regression
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Title stata.com regress — Linear regression
Linear regressionSyntaxMenuDescriptionOptionsRe marks and examplesStored resultsMethods and formulasAcknowledgmentsReferencesAlso seeSyntaxregressdepvar[indepvars] [if] [in] [weight] [,options]optionsDescriptionModelnoconst antsuppress constant termhasconshas user-supplied constanttssconscompute total sum of squares with constant; seldom usedSE/Robustvce(vcetype)vcetypemay beols,robust,clusterclustvar,bootstrap,j ackknife,hc2, orhc3 Reportinglevel(#)set confidence level; default islevel(95)betareport standardized beta coefficientseform(string)report exponentiated coefficients and label asstringdepname(varname)substitute dependent variable name; programmer s optiondisplayoptionscontrol column formats, row spacing, line width, display of omittedvariables and base and empty cells, and factor-variable labelingnoheadersuppress output headernotablesuppress coefficient tableplusmake table extendablemse1force mean squared error to1coeflegenddisplay legend instead of statisticsindepvarsmay contain factor variables; see[U] Factor contain time-series operators; see[U] Time-series ,by,fp,jackknife,mfp,mi estimate,nestreg,rolling,statsby,stepwis e, andsvyare allowed;see[U] Prefix (boo)
are interested in the history of regression. Least squares, which dates back to the 1790s, was discovered independently by Legendre and Gauss. Ordinary least squares Example 1: Basic linear regression Suppose that we have data on the mileage rating and weight of 74 automobiles. The variables in our data are mpg, weight, and foreign.
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