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From | Rodolphe Desbordes <rodolphe.desbordes@strath.ac.uk> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | RE: st: RE: Interpretation of quadratic terms |
Date | Tue, 9 Mar 2010 00:15:12 +0000 |
Dear Rosie, Centering will not affect your estimates and their uncertainty. However, centering allows you to directly obtain the estimated effect of X on Y for a meaningful value of X, i.e. the mean of X. Rodolphe . sysuse auto.dta,clear (1978 Automobile Data) . gen double mpg2=mpg^2 . reg price mpg mpg2 Source | SS df MS Number of obs = 74 -------------+------------------------------ F( 2, 71) = 18.28 Model | 215835615 2 107917807 Prob > F = 0.0000 Residual | 419229781 71 5904644.81 R-squared = 0.3399 -------------+------------------------------ Adj R-squared = 0.3213 Total | 635065396 73 8699525.97 Root MSE = 2429.9 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mpg | -1265.194 289.5443 -4.37 0.000 -1842.529 -687.8593 mpg2 | 21.36069 5.938885 3.60 0.001 9.518891 33.20249 _cons | 22716.48 3366.577 6.75 0.000 16003.71 29429.24 ------------------------------------------------------------------------------ . sum mpg Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- mpg | 74 21.2973 5.785503 12 41 . local m=r(mean) . lincom _b[mpg]+2*_b[mpg2]*`m' ( 1) mpg + 42.59459 mpg2 = 0 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | -355.3442 58.86205 -6.04 0.000 -472.7118 -237.9766 ------------------------------------------------------------------------------ . gen double mpgm=mpg-`m' . gen double mpgm2=mpgm^2 . reg price mpgm mpgm2 Source | SS df MS Number of obs = 74 -------------+------------------------------ F( 2, 71) = 18.28 Model | 215835615 2 107917807 Prob > F = 0.0000 Residual | 419229781 71 5904644.81 R-squared = 0.3399 -------------+------------------------------ Adj R-squared = 0.3213 Total | 635065396 73 8699525.97 Root MSE = 2429.9 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mpgm | -355.3442 58.86205 -6.04 0.000 -472.7118 -237.9766 mpgm2 | 21.36069 5.938885 3.60 0.001 9.518891 33.20249 _cons | 5459.933 343.8718 15.88 0.000 4774.272 6145.594 ------------------------------------------------------------------------------ . end of do-file ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Rosie Chen [jiarongchen2002@yahoo.com] Sent: 08 March 2010 21:15 To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: Interpretation of quadratic terms Thanks a lot for the advice, Rodolphe. I found several resources that suggest centering before creating quadratic terms. Below is one example. http://www.ats.ucla.edu/stat/mult_pkg/faq/general/curves.htm Rosie ----- Original Message ---- From: Rodolphe Desbordes <rodolphe.desbordes@strath.ac.uk> To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> Sent: Mon, March 8, 2010 1:42:31 PM Subject: st: RE: Interpretation of quadratic terms Dear Rosie, If the coefficient on X is positive and the coefficient on X^2 is negative, that suggests that X has a positive effect on Y until a turning point is reached, e.g. 1.3/(2*0.2)=3.25. Beyond that value, X has a negative impact on Y. Rodolphe PS: I am not sure that `centering' reduces multicollinearity. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Rosie Chen Sent: lundi 8 mars 2010 18:28 To: statalist@hsphsun2.harvard.edu Subject: st: Interpretation of quadratic terms Dear all, I have a question regarding how to interpret quadratic terms in regression, and would appreciate your help very much. Because the non-linear nature of the relationship between X and Y; I need to include quadratic terms in the model. To avoid multicollinearity problem with the original variable and its quadratic term, I centered the variable first (X) and then created the square term (Xsq). The model with the quadratic term (Xsq) was proved to be significantly better. Suppose the output is like the following (both coefficients are significant), how to interpret the results? The two signs are opposite. Could anyone provide some insight? Thank you very much in advance! --Rosie y= a + 1.3*X - 0.2*Xsq + e * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/