Dear all, I have the following output generated by linear regression. Since there is only one regression intercept and one slope for one set of data, what is the meaning of std. error for intercept and that of slope? Thanks in advance. Sincerely, Minghua> data(thuesen) > attach(thuesen) > lm(short.velocity~blood.glucose)Call: lm(formula = short.velocity ~ blood.glucose) Coefficients: (Intercept) blood.glucose 1.09781 0.02196> summary(lm(short.velocity~blood.glucose))Call: lm(formula = short.velocity ~ blood.glucose) Residuals: Min 1Q Median 3Q Max -0.40141 -0.14760 -0.02202 0.03001 0.43490 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.09781 0.11748 9.345 6.26e-09 *** blood.glucose 0.02196 0.01045 2.101 0.0479 * --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 0.2167 on 21 degrees of freedom Multiple R-Squared: 0.1737, Adjusted R-squared: 0.1343 F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479>
Since the intercept and slope are estimated parameters, they have sampling distributions described by their means and standard deviations. The s.d. tells you the size of the uncertainty in intercept & in slope. This is a pretty basic stats question -- you need to refer to a standard textbook or reference material ... Ben Bolker On Fri, 26 Sep 2003, Yao, Minghua wrote:> Dear all, > > I have the following output generated by linear regression. Since there is > only one regression intercept and one slope for one set of data, what is the > > meaning of std. error for intercept and that of slope? Thanks in advance. > > Sincerely, > > Minghua > > > > data(thuesen) > > attach(thuesen) > > lm(short.velocity~blood.glucose) > > Call: > lm(formula = short.velocity ~ blood.glucose) > > Coefficients: > (Intercept) blood.glucose > 1.09781 0.02196 > > > summary(lm(short.velocity~blood.glucose)) > > Call: > lm(formula = short.velocity ~ blood.glucose) > > Residuals: > Min 1Q Median 3Q Max > -0.40141 -0.14760 -0.02202 0.03001 0.43490 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 1.09781 0.11748 9.345 6.26e-09 *** > blood.glucose 0.02196 0.01045 2.101 0.0479 * > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 > > Residual standard error: 0.2167 on 21 degrees of freedom > Multiple R-Squared: 0.1737, Adjusted R-squared: 0.1343 > F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479 > > > > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help >-- 620B Bartram Hall bolker at zoo.ufl.edu Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker Box 118525 (ph) 352-392-5697 Gainesville, FL 32611-8525 (fax) 352-392-3704
Thanks, Ben. Could you tell me the formula for calculating this sd., given (x_i, y_i) (i=1,2,...,N)? We only have one intercept and slope for them. -Minghua -----Original Message----- From: Ben Bolker [mailto:bolker at zoo.ufl.edu] Sent: Friday, September 26, 2003 4:34 PM To: Yao, Minghua Cc: R Help (E-mail) Subject: Re: [R] Std. errors of intercept and slope Since the intercept and slope are estimated parameters, they have sampling distributions described by their means and standard deviations. The s.d. tells you the size of the uncertainty in intercept & in slope. This is a pretty basic stats question -- you need to refer to a standard textbook or reference material ... Ben Bolker On Fri, 26 Sep 2003, Yao, Minghua wrote:> Dear all, > > I have the following output generated by linear regression. Since there is > only one regression intercept and one slope for one set of data, what isthe> > meaning of std. error for intercept and that of slope? Thanks in advance. > > Sincerely, > > Minghua > > > > data(thuesen) > > attach(thuesen) > > lm(short.velocity~blood.glucose) > > Call: > lm(formula = short.velocity ~ blood.glucose) > > Coefficients: > (Intercept) blood.glucose > 1.09781 0.02196 > > > summary(lm(short.velocity~blood.glucose)) > > Call: > lm(formula = short.velocity ~ blood.glucose) > > Residuals: > Min 1Q Median 3Q Max > -0.40141 -0.14760 -0.02202 0.03001 0.43490 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 1.09781 0.11748 9.345 6.26e-09 *** > blood.glucose 0.02196 0.01045 2.101 0.0479 * > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 > > Residual standard error: 0.2167 on 21 degrees of freedom > Multiple R-Squared: 0.1737, Adjusted R-squared: 0.1343 > F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479 > > > > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help >-- 620B Bartram Hall bolker at zoo.ufl.edu Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker Box 118525 (ph) 352-392-5697 Gainesville, FL 32611-8525 (fax) 352-392-3704
Thanks, Mr. Graves. This is exactly what I need. -MY -----Original Message----- From: Spencer Graves [mailto:spencer.graves at pdf.com] Sent: Saturday, September 27, 2003 8:34 AM To: bolker at zoo.ufl.edu Cc: Yao, Minghua; R Help (E-mail) Subject: Re: [R] Std. errors of intercept and slope See especially any discussion of "the matrix formulation of regression". I'm sure this is in many books. I'm not familiar with the recent literature, but I know it is in Draper and Smith, Applied Regression Analysis and Box, Hunter and Hunter, Statistics for Experimenters. Briefly, suppose we write the regression model as y = X b + e, where y and e are N x 1 vectors, X is an N x k matrix, and e is a vector of normal, independent errors with standard deviation s.e. Then the least squares and maximum likelihood estimate of b is b.hat = (inverse(X' X))*(X'y), and the covariance matrix for b.hat is var(b.hat) = s.e^2 * inverse(X'X). I apologize if this is too terse for you; if so, please see any good book on regress. hope this helps. spencer graves Ben Bolker wrote:> I'm afraid you're going to have to look it up in a basic statistics >textbook. > > Ben Bolker > > >On Fri, 26 Sep 2003, Yao, Minghua wrote: > > > >>Thanks, Ben. >> >>Could you tell me the formula for calculating this sd., given (x_i, y_i) >>(i=1,2,...,N)? >>We only have one intercept and slope for them. >> >>-Minghua >> >>-----Original Message----- >>From: Ben Bolker [mailto:bolker at zoo.ufl.edu] >>Sent: Friday, September 26, 2003 4:34 PM >>To: Yao, Minghua >>Cc: R Help (E-mail) >>Subject: Re: [R] Std. errors of intercept and slope >> >> >> >> Since the intercept and slope are estimated parameters, they have >>sampling distributions described by their means and standard deviations. >>The s.d. tells you the size of the uncertainty in intercept & in slope. >> >> This is a pretty basic stats question -- you need to refer to a standard>>textbook or reference material ... >> >> Ben Bolker >> >>On Fri, 26 Sep 2003, Yao, Minghua wrote: >> >> >> >>>Dear all, >>> >>>I have the following output generated by linear regression. Since thereis>>>only one regression intercept and one slope for one set of data, what is >>> >>> >>the >> >> >>>meaning of std. error for intercept and that of slope? Thanks in advance. >>> >>>Sincerely, >>> >>>Minghua >>> >>> >>> >>> >>>>data(thuesen) >>>>attach(thuesen) >>>>lm(short.velocity~blood.glucose) >>>> >>>> >>>Call: >>>lm(formula = short.velocity ~ blood.glucose) >>> >>>Coefficients: >>> (Intercept) blood.glucose >>> 1.09781 0.02196 >>> >>> >>> >>>>summary(lm(short.velocity~blood.glucose)) >>>> >>>> >>>Call: >>>lm(formula = short.velocity ~ blood.glucose) >>> >>>Residuals: >>> Min 1Q Median 3Q Max >>>-0.40141 -0.14760 -0.02202 0.03001 0.43490 >>> >>>Coefficients: >>> Estimate Std. Error t value Pr(>|t|) >>>(Intercept) 1.09781 0.11748 9.345 6.26e-09 *** >>>blood.glucose 0.02196 0.01045 2.101 0.0479 * >>>--- >>>Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 >>> >>>Residual standard error: 0.2167 on 21 degrees of freedom >>>Multiple R-Squared: 0.1737, Adjusted R-squared: 0.1343 >>>F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479 >>> >>> >>> >>>______________________________________________ >>>R-help at stat.math.ethz.ch mailing list >>>https://www.stat.math.ethz.ch/mailman/listinfo/r-help >>> >>> >>> >> >> > > >