Can anyone explain to me what the meaning of %9.0g is?

[Yao Zhao]

I'm confused about Stata formats. I try to see Stata help format file, but I'm still confused.

The current format is %9.0g, which is the default format for numeric data in the Data Editor. This format instructs Stata to try to print numbers within nine columns.

(From: A Gentle Introduction to Stata)

[Red Owl]

See

help format##gformat

Red Owl
Stata/IC 16 (Windows 10, 64-bit)

[Richard Williams]

As a sidelight I often like to use the cformat option on estimation commands, e,g.

use https://www3.nd.edu/~rwilliam/statafiles/shuttle2.dta, clear
ologit distress date temp, nolog
ologit distress date temp, nolog cformat(%9.3f)

. use https://www3.nd.edu/~rwilliam/statafiles/shuttle2.dta, clear
(First 25 space shuttle flights)

. ologit distress date temp, nolog

Ordered logistic regression                     Number of obs     =         23
                                                LR chi2(2)        =      12.32
                                                Prob > chi2       =     0.0021
Log likelihood =  -18.79706                     Pseudo R2         =     0.2468

------------------------------------------------------------------------------
    distress |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        date |    .003286   .0012662     2.60   0.009     .0008043    .0057677
        temp |  -.1733752   .0834475    -2.08   0.038    -.3369293   -.0098212
-------------+----------------------------------------------------------------
       /cut1 |   16.42813   9.554822                     -2.298978    35.15524
       /cut2 |   18.12227   9.722302                      -.933092    37.17763
------------------------------------------------------------------------------

. ologit distress date temp, nolog cformat(%9.3f)

Ordered logistic regression                     Number of obs     =         23
                                                LR chi2(2)        =      12.32
                                                Prob > chi2       =     0.0021
Log likelihood =  -18.79706                     Pseudo R2         =     0.2468

------------------------------------------------------------------------------
    distress |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        date |      0.003      0.001     2.60   0.009        0.001       0.006
        temp |     -0.173      0.083    -2.08   0.038       -0.337      -0.010
-------------+----------------------------------------------------------------
       /cut1 |     16.428      9.555                        -2.299      35.155
       /cut2 |     18.122      9.722                        -0.933      37.178
------------------------------------------------------------------------------

.

If 3 decimal place accuracy isn't good enough for you you might want to consider rescaling variables anyway so the output is easier to read.