-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  Q:\C-modelling\runmlwin\website\logfiles\2020-03-27\16\3.5.smcl
  log type:  smcl
 opened on:  27 Mar 2020, 18:21:24

. ****************************************************************************
. * Module 3: Multiple Regression Stata Practicals
. *
. *     P3.5: Checking Model Assumptions in Multiple Regression
. *
. *           George Leckie
. *           Centre for Multilevel Modelling, 2010
. ****************************************************************************
. *     Stata do-file to replicate all analyses using runmlwin
. *
. *     George Leckie
. *     Centre for Multilevel Modelling, 2013
. *     http://www.bristol.ac.uk/cmm/software/runmlwin/
. ****************************************************************************
. 
. use "http://www.bristol.ac.uk/cmm/media/runmlwin/3.5.dta", clear

. 
. runmlwin score cons cohort90 sclass1 sclass2 sclass4 female ///
>         cohort90Xfemale cohort90Xsclass1 cohort90Xsclass2 cohort90Xsclass4, /
> //
>         level1(caseid: cons, residuals(estd, standardised)) ///
>         mlwinsettings(optimat) nopause 
 
MLwiN 3.05 multilevel model                     Number of obs      =     33988
Normal response model (hierarchical)
Estimation algorithm: IGLS

Run time (seconds)   =       1.10
Number of iterations =          2
Log likelihood       = -139166.25
Deviance             =   278332.5
------------------------------------------------------------------------------
       score |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cons |     23.806    .172611  137.92    0.000     23.46769    24.14431
    cohort90 |   1.297353   .0329408   39.38    0.000      1.23279    1.361916
     sclass1 |   13.27072   .2052898   64.64    0.000     12.86836    13.67308
     sclass2 |   6.899982    .208843   33.04    0.000     6.490657    7.309307
     sclass4 |  -4.349855   .2938594  -14.80    0.000    -4.925809   -3.773901
      female |   1.975961   .1580878   12.50    0.000     1.666115    2.285808
cohort90Xf~e |   .1328717   .0295069    4.50    0.000     .0750392    .1907041
cohort90Xs~1 |  -.3174493   .0385311   -8.24    0.000     -.392969   -.2419297
cohort90Xs~2 |   -.241989   .0397581   -6.09    0.000    -.3199134   -.1640645
cohort90Xs~4 |   .1846463   .0533755    3.46    0.001     .0800323    .2892603
------------------------------------------------------------------------------

------------------------------------------------------------------------------
   Random-effects Parameters |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 1: caseid              |
                   var(cons) |   210.8745   1.617619       207.704     214.045
------------------------------------------------------------------------------

. 
. egen estd0rank = rank(estd0)

. 
. generate estd0uniform = estd0rank/(_N + 1)

. 
. generate estd0nscore = invnorm(estd0uniform)

. 
. 
. 
. * P3.5.1 Checking the normality assumption
. 
. scatter estd0 estd0nscore

. 
. 
. 
. * P3.5.2 Checking the homoskedasticity assumption
. 
. predict predscore

. 
. scatter estd0 predscore

. 
end of do-file