Linearity/ non-linearity using residual plot

[Rochan Mathur]

How can I predict linearity or non-linearity of my regression model using this residuals Vs fitted plot?

[William Lisowski]

For those who may want to address this question, note that there is a description of the data and the model being fit at

https://www.statalist.org/forums/for...ssion-on-stata

[Carlo Lazzaro]

Rochan:
you can also consider fitted vs predictor graph to have a more comprehensive picture of non-linearity:

use "http://www.stata-press.com/data/r15/nlswork.dta"
. xtreg ln_wage i.race c.age##c.age

Random-effects GLS regression                   Number of obs     =     28,510
Group variable: idcode                          Number of groups  =      4,710

R-sq:                                           Obs per group:
     within  = 0.1087                                         min =          1
     between = 0.1175                                         avg =        6.1
     overall = 0.1048                                         max =         15

                                                Wald chi2(4)      =    3498.50
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------
     ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        race |
      black  |  -.1237269   .0127651    -9.69   0.000    -.1487461   -.0987077
      other  |   .0965773   .0532529     1.81   0.070    -.0077965    .2009511
             |
         age |   .0594573   .0027157    21.89   0.000     .0541346      .06478
             |
 c.age#c.age |  -.0006835    .000045   -15.18   0.000    -.0007717   -.0005952
             |
       _cons |   .5761164   .0398472    14.46   0.000     .4980173    .6542155
-------------+----------------------------------------------------------------
     sigma_u |  .36094993
     sigma_e |  .30245467
         rho |   .5874941   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. predict fitted, xb
(24 missing values generated)

. twoway (scatter fitted age)