> culture.mod1 <- lm(y ~ pt_c + culture)
> anova(culture.mod1)
Analysis of Variance Table

Response: y
          Df Sum Sq Mean Sq F value    Pr(>F)    
pt_c       1 665.70  665.70  21.789 0.0002208 ***
culture    1 647.47  647.47  21.193 0.0002531 ***
Residuals 17 519.38   30.55                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> summary(culture.mod1)

Call:
lm(formula = y ~ pt_c + culture)

Residuals:
   Min     1Q Median     3Q    Max 
-6.039 -4.895 -1.983  5.642  9.139 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  16.3540     1.7968   9.102 6.04e-08 ***
pt_c         -0.6135     0.1041  -5.893 1.77e-05 ***
culture2    -12.0081     2.6084  -4.604 0.000253 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 5.527 on 17 degrees of freedom
Multiple R-squared: 0.7166,     Adjusted R-squared: 0.6832 
F-statistic: 21.49 on 2 and 17 DF,  p-value: 2.216e-05 

> 
> culture.mod2a <- lm(y ~ pt_c, subset=(culture==1))
> anova(culture.mod2a)
Analysis of Variance Table

Response: y
          Df  Sum Sq Mean Sq F value    Pr(>F)    
pt_c       1 1403.68 1403.68  153.36 1.686e-06 ***
Residuals  8   73.22    9.15                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> summary(culture.mod2a)

Call:
lm(formula = y ~ pt_c, subset = (culture == 1))

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1318 -2.2187 -0.1055  0.4645  6.2813 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 17.52661    1.00051   17.52 1.15e-07 ***
pt_c        -0.90665    0.07321  -12.38 1.69e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 3.025 on 8 degrees of freedom
Multiple R-squared: 0.9504,     Adjusted R-squared: 0.9442 
F-statistic: 153.4 on 1 and 8 DF,  p-value: 1.686e-06 

> 
> 
> culture.mod2b <- lm(y[culture==2] ~ pt_c[culture==2])
> anova(culture.mod2b) 
Analysis of Variance Table

Response: y[culture == 2]
                   Df Sum Sq Mean Sq F value Pr(>F)
pt_c[culture == 2]  1 29.602 29.6023  3.2004 0.1114
Residuals           8 73.998  9.2497               
> summary(culture.mod2b)

Call:
lm(formula = y[culture == 2] ~ pt_c[culture == 2])

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1146 -1.6137 -0.2256  1.6397  5.5590 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         6.14725    1.02864   5.976 0.000332 ***
pt_c[culture == 2] -0.16319    0.09122  -1.789 0.111417    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 3.041 on 8 degrees of freedom
Multiple R-squared: 0.2857,     Adjusted R-squared: 0.1965 
F-statistic:   3.2 on 1 and 8 DF,  p-value: 0.1114 

> 
> 
> culture.mod3 <- lm(y ~ pt_c + culture + pt_c:culture)
> anova(culture.mod3)
Analysis of Variance Table

Response: y
             Df Sum Sq Mean Sq F value    Pr(>F)    
pt_c          1 665.70  665.70  72.350 2.481e-07 ***
culture       1 647.47  647.47  70.369 2.981e-07 ***
pt_c:culture  1 372.16  372.16  40.447 9.466e-06 ***
Residuals    16 147.22    9.20                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> summary(culture.mod3)

Call:
lm(formula = y ~ pt_c + culture + pt_c:culture)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1146 -2.0486 -0.1055  1.2317  6.2813 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    17.52661    1.00316  17.471 7.61e-12 ***
pt_c           -0.90665    0.07341 -12.351 1.35e-09 ***
culture2      -11.37936    1.43488  -7.931 6.21e-07 ***
pt_c:culture2   0.74346    0.11690   6.360 9.47e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 3.033 on 16 degrees of freedom
Multiple R-squared: 0.9197,     Adjusted R-squared: 0.9046 
F-statistic: 61.06 on 3 and 16 DF,  p-value: 5.581e-09 

>