--------------------------------------------------------------------------------

name:  <unnamed>

> win2013\class10.log

log type:  text

opened on:  12 Feb 2013, 13:30:36

. *class starts here

. *(8 variables, 11 observations pasted into data editor)

* Import the data from the Anscombe Excel file on my website, and copy in Stata’s data editor (with first row as variable names)

. twoway (scatter y2 x2) (lfit y2 x2)

. regress y2 x2

Source |       SS       df       MS              Number of obs =      11

-------------+------------------------------           F(  1,     9) =   17.97

Model |  27.5000024     1  27.5000024           Prob > F      =  0.0022

Residual |   13.776294     9  1.53069933           R-squared     =  0.6662

Total |  41.2762964    10  4.12762964           Root MSE      =  1.2372

------------------------------------------------------------------------------

y2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

x2 |         .5   .1179638     4.24   0.002     .2331475    .7668526

_cons |   3.000909   1.125303     2.67   0.026     .4552978     5.54652

------------------------------------------------------------------------------

. regress y1 x1

Source |       SS       df       MS              Number of obs =      11

-------------+------------------------------           F(  1,     9) =   17.99

Model |  27.5100011     1  27.5100011           Prob > F      =  0.0022

Residual |  13.7626904     9  1.52918783           R-squared     =  0.6665

Total |  41.2726916    10  4.12726916           Root MSE      =  1.2366

------------------------------------------------------------------------------

y1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

x1 |   .5000909   .1179055     4.24   0.002     .2333701    .7668117

_cons |   3.000091   1.124747     2.67   0.026     .4557369    5.544445

------------------------------------------------------------------------------

. regress y2 x2

Source |       SS       df       MS              Number of obs =      11

-------------+------------------------------           F(  1,     9) =   17.97

Model |  27.5000024     1  27.5000024           Prob > F      =  0.0022

Residual |   13.776294     9  1.53069933           R-squared     =  0.6662

Total |  41.2762964    10  4.12762964           Root MSE      =  1.2372

------------------------------------------------------------------------------

y2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

x2 |         .5   .1179638     4.24   0.002     .2331475    .7668526

_cons |   3.000909   1.125303     2.67   0.026     .4552978     5.54652

------------------------------------------------------------------------------

. predict m2

(option xb assumed; fitted values)

* The predict command generates a new variable that has the predicted values for the model, for each X, Y_predicted=3+(0.5)X.

. gen resid_m2=y2-m2

* a new variable for the residuals, that is the difference between the actual Y and the predicted Y.

. twoway (scatter resid_m2 x2) (line m2 x2) (scatter y2 x2) (lfit y2 x2)

. gen abs_resid_m2=abs( resid_m2)

. gsort - abs_resid_m2

. list  x2 y2 resid_m2 abs_resid_m2 if _n<4

+----------------------------------+

| x2     y2    resid_m2   abs_re~2 |

|----------------------------------|

1. | 14    8.1   -1.900909   1.900909 |

2. |  4    3.1   -1.900909   1.900909 |

3. |  9   8.77    1.269091   1.269091 |

* The above 3 commands create a new variable with absolute value residuals, then sort the data from largest to smallest, then list the first 3 (i.e. the biggest 3 residuals in absolute value).

. clear all

. use "C:\Users\Michael\Desktop\cps_mar_2000_new_unchanged.dta", clear

* How to generate the dummy variable for Vietnam veterans.

. codebook vetlast

--------------------------------------------------------------------------------

vetlast                                  Veteran's most recent period of service

--------------------------------------------------------------------------------

type:  numeric (byte)

label:  vetlastlbl

range:  [0,9]                        units:  1

unique values:  6                        missing .:  0/133710

tabulation:  Freq.   Numeric  Label

30904         0  NIU

91149         1  No service

2428         4  World War II

1716         6  Korean War

3683         8  Vietnam Era

3830         9  Other service

. gen viet_veteran=0

. replace viet_veteran=1 if vetlast==8

. tabulate vetlast viet_veteran, miss

Veteran's most recent |     viet_veteran

period of service |         0          1 |     Total

----------------------+----------------------+----------

NIU |    30,904          0 |    30,904

No service |    91,149          0 |    91,149

World War II |     2,428          0 |     2,428

Korean War |     1,716          0 |     1,716

Vietnam Era |         0      3,683 |     3,683

Other service |     3,830          0 |     3,830

----------------------+----------------------+----------

Total |   130,027      3,683 |   133,710

. log close

name:  <unnamed>