lm(formula = v50_sq ~ layers + sharp + fsp + I(layers * sharp) + I(layers * fsp))
Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        3.64332    0.83615   4.357 0.000339 ***
layers             0.85469    0.03372  25.349 4.12e-16 ***
sharp              0.76866    1.18249   0.650 0.523453    
fsp                0.49887    1.13634   0.439 0.665602    
I(layers * sharp)  0.14962    0.04768   3.138 0.005417 ** 
I(layers * fsp)    0.13697    0.04670   2.933 0.008538 ** 

Residual standard error: 1.219 on 19 degrees of freedom
Multiple R-squared: 0.9925,     Adjusted R-squared: 0.9905 
F-statistic: 502.9 on 5 and 19 DF,  p-value: < 2.2e-16 
Analysis of Variance Table
                  Df Sum Sq Mean Sq   F value    Pr(>F)    
layers             1 3645.4  3645.4 2452.5665 < 2.2e-16 ***
sharp              1   25.5    25.5   17.1805 0.0005503 ***
fsp                1   48.0    48.0   32.2873 1.774e-05 ***
I(layers * sharp)  1    5.4     5.4    3.6406 0.0716164 .  
I(layers * fsp)    1   12.8    12.8    8.6018 0.0085382 ** 
Residuals         19   28.2     1.5                        
__________________________________________________________________________________
lm(formula = v50_sq ~ layers + sharp + fsp)
Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.5880     0.7237   2.194 0.039592 *  
layers        0.9514     0.0234  40.659  < 2e-16 ***
sharp         3.9482     0.7435   5.310 2.89e-05 ***
fsp           3.3681     0.7230   4.659 0.000135 ***

Residual standard error: 1.487 on 21 degrees of freedom
Multiple R-squared: 0.9877,     Adjusted R-squared: 0.9859 
F-statistic: 560.6 on 3 and 21 DF,  p-value: < 2.2e-16 
Analysis of Variance Table
          Df Sum Sq Mean Sq  F value    Pr(>F)    
layers     1 3645.4  3645.4 1648.530 < 2.2e-16 ***
sharp      1   25.5    25.5   11.548 0.0027092 ** 
fsp        1   48.0    48.0   21.702 0.0001347 ***
Residuals 21   46.4     2.2                       
_______________________________________________________________________________
lm(formula = v50_sq ~ layers)
Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.10651    0.87799   4.677 0.000104 ***
layers       0.94937    0.03591  26.437  < 2e-16 ***

Residual standard error: 2.284 on 23 degrees of freedom
Multiple R-squared: 0.9681,     Adjusted R-squared: 0.9668 
F-statistic: 698.9 on 1 and 23 DF,  p-value: < 2.2e-16 
Analysis of Variance Table
          Df Sum Sq Mean Sq F value    Pr(>F)    
layers     1 3645.4  3645.4  698.91 < 2.2e-16 ***
Residuals 23  120.0     5.2                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 
> anova(model1,model2)
Analysis of Variance Table

Model 1: v50_sq ~ layers + sharp + fsp + I(layers * sharp) + I(layers * 
    fsp)
Model 2: v50_sq ~ layers + sharp + fsp
  Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
1     19 28.241                                
2     21 46.438 -2   -18.197 6.1212 0.008874 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> anova(model2,model3)
Analysis of Variance Table

Model 1: v50_sq ~ layers + sharp + fsp
Model 2: v50_sq ~ layers
  Res.Df     RSS Df Sum of Sq      F  Pr(>F)    
1     21  46.438                                
2     23 119.966 -2   -73.528 16.625 4.7e-05 ***