I'm trying to regress some simple pooled data. My data has 60 observations and three columns: Weight, Height, and Sex (female=1, male=0).

If I regress thus, Weight ~ Height + Sex, my model is fairly satisfactory, but the residuals are not homoscedastic (green errors are male, blue female):

![plot](https://i1267.photobucket.com/albums/jj541/nbahmanyar/Rplot_zps69001b34.png)

I tried regressing on the log of Weight and/or Height, but that didn't do much. What should I do to make the residuals homescedastic and/or make my model more accurate? Any help would be appreciated.

**Edit**

Doing a generalized regression model gives the following.

    Generalized least squares fit by REML
      Model: Weight ~ h + s 
      Data: P149 
           AIC      BIC    logLik
      514.2221 524.4374 -252.1111
    
    Variance function:
     Structure: Different standard deviations per stratum
     Formula: ~1 | Sex 
     Parameter estimates:
            0         1 
    1.0000000 0.6685307 
    
    Coefficients:
                     Value Std.Error   t-value p-value
    (Intercept)  27.197499  51.88129  0.524226  0.6022
    h             1.852382   0.75634  2.449128  0.0174
    s           -25.284478   5.53300 -4.569755  0.0000
    
     Correlation: 
      (Intr) h     
    h -0.997       
    s -0.524  0.466
    
    Standardized residuals:
           Min         Q1        Med         Q3        Max 
    -1.6655243 -0.6879858 -0.1839396  0.5628971  3.9857544 
    
    Residual standard error: 22.13369 
    Degrees of freedom: 60 total; 57 residual

With this s. residual plot:

![](https://i1267.photobucket.com/albums/jj541/nbahmanyar/Rplot1_zps5ee264a0.png)

Could someone please explain how precisely this model is different from a standard multiple regression model? Thanks.