# Re: st: from scientific to real numbers...

 From n j cox To statalist@hsphsun2.harvard.edu Subject Re: st: from scientific to real numbers... Date Fri, 14 Oct 2005 15:44:49 +0100

I see only real numbers here...

Stata doesn't offer much scope for user tuning of output format
in instances like this. A very good reason is that in many
typical cases a user format would make many problems worse.

Your example of prices is moderately spectacular in that
most coefficients are rather large. At a wild guess most
or all of your covariates are dummy variables such as presence
or absence of garage and all of
your house prices are large in the currency you are using.

In practice, scaling your units by (e.g.) dividing prices
by 1000 or an even larger number is likely to be your best
route forward. In other words, tackle this problem upstream
so that the results end up smaller and format is no longer
an issue.

An alternative is to look at saved results.

Nick
n.j.cox@durham.ac.uk

Christer Thrane

I want to get the precise price prediction for a house with certain
attributes, given the follwing regression, in real numbers. That is, 1.5e+06
(see bottom) does not tell me enough. Does anyone know how to "force" Stata
to come up with the exact figure?

reg pris bta antsov leil garasje

Source | SS df MS Number of obs =
313
-------------+------------------------------ F( 4, 308) =
103.23
Model | 6.0351e+13 4 1.5088e+13 Prob > F =
0.0000
Residual | 4.5015e+13 308 1.4615e+11 R-squared =
0.5728
0.5672
Total | 1.0537e+14 312 3.3771e+11 Root MSE =
3.8e+05

------------------------------------------------------------------------------
pris | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
bta | 5877.52 528.658 11.12 0.000 4837.282
6917.759
antsov | 96936.37 29516.44 3.28 0.001 38856.98
155015.8
leil | 168382.4 57136.84 2.95 0.003 55954.5
280810.4
garasje | 127576.9 47204.03 2.70 0.007 34693.7
220460.1
_cons | 277688.5 82658.97 3.36 0.001 115040.8
440336.3
------------------------------------------------------------------------------

. adjust bta = 130 antsov = 3 leil = 0 gar = 1

--------------------------------------------------------------------------------
Dependent variable: pris Command: regress
Covariates set to value: bta = 130, antsov = 3, leil = 0, garasje = 1
--------------------------------------------------------------------------------

----------------------
All | xb
----------+-----------
| 1.5e+06
----------------------
Key: xb = Linear Prediction
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