Data = read.table(textConnection(Input),header=TRUE)

plot(x   = Data$Temp,      y   = Data$Pulse,      col = Data$Species,      pch = 16,     xlab = "Temperature",     ylab = "Pulse")legend('bottomright',        legend = levels(Data$Species),        col = 1:2,        cex = 1,           pch = 16)

 Anova Table (Type II tests)             Sum Sq Df  F value    Pr(>F)   Temp         4376.1  1 1388.839 < 2.2e-16 ***Species       598.0  1  189.789 9.907e-14 ***Temp:Species    4.3  1    1.357    0.2542    ### Interaction is not significant, so the slope across groups### is not different. model.2 = lm (Pulse ~ Temp + Species,              data = Data)library(car)Anova(model.2, type="II")Anova Table (Type II tests)          Sum Sq Df F value    Pr(>F)   Temp      4376.1  1  1371.4 < 2.2e-16 ***Species    598.0  1   187.4 6.272e-14 ***### The category variable (Species) is significant,### so the intercepts among groups are differentCoefficients:             Estimate Std. Error t value Pr(>|t|)   (Intercept)  -7.21091    2.55094  -2.827  0.00858 **Temp          3.60275    0.09729  37.032  < 2e-16 ***Speciesniv  -10.06529    0.73526 -13.689 6.27e-14 ***###   but the calculated results will be identical.### The slope estimate is the same.### The intercept for species 1 (ex) is (intercept).### The intercept for species 2 (niv) is (intercept) + Speciesniv.### This is determined from the contrast coding of the Species### variable shown below, and the fact that Speciesniv is shown in### coefficient table above.    nivex    0niv   1

plot(x   = Data$Temp,      y   = Data$Pulse,      col = Data$Species,      pch = 16,     xlab = "Temperature",     ylab = "Pulse")

Multiple R-squared:  0.9896,  Adjusted R-squared:  0.9888F-statistic:  1331 on 2 and 28 DF,  p-value: < 2.2e-16

### additional model checking plots with: plot(model.2)### alternative: library(FSA); residPlot(model.2) 

### --------------------------------------------------------------### Analysis of covariance, hypothetical data### --------------------------------------------------------------Data = read.table(textConnection(Input),header=TRUE)

plot(x   = Data$Temp,      y   = Data$Pulse,      col = Data$Species,      pch = 16,     xlab = "Temperature",     ylab = "Pulse")legend('bottomright',        legend = levels(Data$Species),        col = 1:3,        cex = 1,           pch = 16)

options(contrasts = c("contr.treatment", "contr.poly"))   ### These are the default contrasts in RAnova(model.1, type="II")             Sum Sq Df   F value Pr(>F)   Temp         7026.0  1 2452.4187 <2e-16 ***Species      7835.7  2 1367.5377 <2e-16 ***Temp:Species    5.2  2    0.9126 0.4093   ### Interaction is not significant, so the slope among groups### is not different. Anova(model.2, type="II")          Sum Sq Df F value    Pr(>F)   Temp      7026.0  1  2462.2 < 2.2e-16 ***Species   7835.7  2  1373.0 < 2.2e-16 ***Residuals  125.6 44 ### The category variable (Species) is significant,### so the intercepts among groups are differentsummary(model.2)Coefficients:             Estimate Std. Error t value Pr(>|t|)   (Intercept)  -6.35729    1.90713  -3.333  0.00175 **Temp          3.56961    0.07194  49.621  < 2e-16 ***Speciesfake  19.81429    0.66333  29.871  < 2e-16 ***Speciesniv  -10.18571    0.66333 -15.355  < 2e-16 ***### The slope estimate is the Temp coefficient.### The intercept for species 1 (ex) is (intercept).### The intercept for species 2 (fake) is (intercept) + Speciesfake.### The intercept for species 3 (niv) is (intercept) + Speciesniv.### This is determined from the contrast coding of the Species### variable shown below.contrasts(Data$Species)     fake nivex      0   0fake    1   0niv     0   1

Multiple R-squared:  0.9919,  Adjusted R-squared:  0.9913F-statistic:  1791 on 3 and 44 DF,  p-value: < 2.2e-16

hist(residuals(model.2),      col="darkgray")

plot(fitted(model.2),      residuals(model.2))

### additional model checking plots with: plot(model.2)### alternative: library(FSA); residPlot(model.2)