Hi,
using
sysuse auto, clear
reg mpg, nohe
mean mpg
ttest mpg==0
displays the same results. However, how do these tests deal with the assumption, that mpg has to normal distributed?
More precisely , how important is the fact that mpg is normal distributed? Most of the variables in my sample are left or right skewed...
Is ttest also in this case reliable it?
by the way, median mpg require a option. So, how can I test if the median of a var. is significant without using this command? Because I have no idea which by-option would make sense in my sample.
Nick Cox seems not to be fully agreed with LAD/qreg...
b
-------- Original-Nachricht --------
> Datum: Tue, 25 Nov 2008 11:28:23 +0100
> Von: "Martin Weiss" <[email protected]>
> An: [email protected]
> Betreff: RE: st: significance of mean and median
> Line for the server...
>
>
> Agreed! But what is the beauty of your solution compared with the -mean-
> command that was offered as a solution to Bastian`s problem in this
> thread?
> They obviously give the same result...
>
> ********
> sysuse auto, clear
> reg mpg, nohe
> mean mpg
> ********
>
> HTH
> Martin
>
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Ronan Conroy
> Sent: Tuesday, November 25, 2008 11:22 AM
> To: [email protected]
> Subject: Re: st: significance of mean and median
>
> On 24 Nov 2008, at 11:55, Bastian Steingros wrote:
>
> > After creating the descriptive statistics of my sample I want to
> > show that the mean of a certain variable is statistically significant.
> > For example the mean of var1 is 0,45. Now I want to show that the
> > positive sign is significant.
>
> A regression without any predictor variable is a constant-only model,
> and the test for significance of the constant is a test that the
> constant is zero.
>
> . sysuse auto
> (1978 Automobile Data)
>
> . regress mpg
>
> Source | SS df MS Number of obs
> = 74
> -------------+------------------------------ F( 0, 73)
> = 0.00
> Model | 0 0 . Prob > F
> = .
> Residual | 2443.45946 73 33.4720474 R-squared
> = 0.0000
> -------------+------------------------------ Adj R-squared
> = 0.0000
> Total | 2443.45946 73 33.4720474 Root MSE
> = 5.7855
>
> ----------------------------------------------------------------------------
> --
> mpg | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------
> +----------------------------------------------------------------
> _cons | 21.2973 .6725511 31.67 0.000 19.9569
> 22.63769
> ----------------------------------------------------------------------------
> --
>
> The coefficient for the constant is the mean of the data (since the
> mean is the statistic that minimises prediction error, when we define
> prediction error as the squared difference between observed and
> expected).
>
> Similarly, quantile regression will test that the median is zero
>
> . qreg mpg
> Iteration 1: WLS sum of weighted deviations = 330.13202
>
> Iteration 1: sum of abs. weighted deviations = 330
> Iteration 2: sum of abs. weighted deviations = 328
>
> Median regression Number of obs
> = 74
> Raw sum of deviations 328 (about 20)
> Min sum of deviations 328 Pseudo R2
> = 0.0000
>
> ----------------------------------------------------------------------------
> --
> mpg | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------
> +----------------------------------------------------------------
> _cons | 20 .7799751 25.64 0.000 18.44551
> 21.55449
> ----------------------------------------------------------------------------
> --
>
> the median being the statistic which minimises error, when we define
> error as the absolute difference between the observed and expected.
>
>
>
> Ronan Conroy
> =================================
>
> [email protected]
> Royal College of Surgeons in Ireland
> Epidemiology Department,
> Beaux Lane House, Dublin 2, Ireland
> +353 (0)1 402 2431
> +353 (0)87 799 97 95
> +353 (0)1 402 2764 (Fax - remember them?)
> http://rcsi.academia.edu/RonanConroy
>
> P Before printing, think about the environment
>
>
>
>
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