The following is the output of an analysis looking at the Supercar dataset which contains measurements of top speeds for different brands of supercar.

> fit <- aov(speed ~ car, data=Supercar)

> summary(fit)

Df Sum Sq Mean Sq F valuePr(>F)

car41456364.125.14 1.9e-08 ***

Residuals2536214.5

---

Signif. codes:0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> TukeyHSD(fit)

Tukey multiple comparisons of means

95% family-wise confidence level

Fit: aov(formula = speed ~ car, data = Supercar)

$car

difflwruprp adj

Ferrari-Acura17.066666710.6142478 23.519086 0.0000004

Lotus-Acura0.6833333-5.76908557.135752 0.9978264

Porsche-Acura14.45000007.9975811 20.902419 0.0000064

Viper-Acura6.71666670.2642478 13.169086 0.0384079

Lotus-Ferrari-16.3833333 -22.8357522 -9.930914 0.0000008

Porsche-Ferrari-2.6166667-9.06908553.835752 0.7563408

Viper-Ferrari-10.3500000 -16.8024189 -3.897581 0.0006917

Porsche-Lotus13.76666677.3142478 20.219086 0.0000137

Viper-Lotus6.0333333-0.4190855 12.485752 0.0749333

Viper-Porsche-7.7333333 -14.1857522 -1.280914 0.0132379

Which of the following statements are true.

A) Supercars top-speeds differ

B) All the other brands are faster than Acuras

C) Ferraris and Porsches are faster than the other brands, but we can't tell which of the two is fastest.

answer:

a:All the statements are true

b:A & B are true

c:A & C are true

d: B & C are true