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name: <unnamed>
log: C:\Documents and Settings\Michael Rosenfeld\My Documents\newer web
> pages\soc_meth_proj3\fall_2011_381_logs\class11.log
log type: text
opened on: 27 Oct 2011, 12:14:15
* Some quick stata graphing routines, you won't be able to see or appreciate the output unless you run these yourself (since this log file doesn't have the graphics embedded in it).
. *(8 variables, 11 observations pasted into data editor)
. twoway (scatter y2 x2) (lfit y2 x2)
*scatter plot plus the best fit line overlaid.
. 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
-------------+------------------------------ Adj R-squared = 0.6292
Total | 41.2762964 10 4.12762964 Root MSE = 1.2372
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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
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*The regression that makes that best fit line
. predict m2
(option xb assumed; fitted values)
. gen resid_m2=y2-m2
*generate residuals
. twoway (scatter resid_m2 x2)
* plot the residuals against X
. rvfplot, yline(0)
* a built-in post estimation command to plot residuals versus fitted values.
. twoway (scatter y2 x2) (lfit y2 x2) (line y2 m2)
* Note that the above graph produced a weird result because the X's are not sorted, and connected the points via a line made a mess.
. twoway (scatter y2 x2) (lfit y2 x2) (line m2 x2, sort)
* This was better.
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