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From |
n j cox <n.j.cox@durham.ac.uk> |

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

-------------+------------------------------ Adj R-squared =

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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