# A tibble: 12 × 2
Time Program
<dbl> <dbl>
1 59 1
2 64 1
3 57 1
4 62 1
5 52 2
6 58 2
7 54 2
8 58 3
9 65 3
10 71 3
11 63 3
12 64 3
> lm12=aov(Time~as.factor(Program),data=ex1112)
> anova(lm12)
Analysis of Variance Table
Response: Time
Df Sum Sq Mean Sq F value Pr(>F)
as.factor(Program) 2 170.45 85.225 5.7042 0.02512 *
Residuals 9 134.47 14.941
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> lsmeans(ls.lm12,"Program")
Program lsmean SE df lower.CL upper.CL
1 60.50000 1.932663 9 56.12801 64.87199
2 54.66667 2.231647 9 49.61833 59.71500
3 64.20000 1.728626 9 60.28958 68.11042
Confidence level used: 0.95
> ls.lm12 <- update(ls.lm12, by.vars = NULL, level = 0.99)
> lsmeans(ls.lm12,"Program")
Program lsmean SE df lower.CL upper.CL
1 60.50000 1.932663 9 54.21916 66.78084
2 54.66667 2.231647 9 47.41418 61.91915
3 64.20000 1.728626 9 58.58225 69.81775
Confidence level used: 0.99
> TukeyHSD(lm12)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Time ~ as.factor(Program), data = ex1112)
$`as.factor(Program)`
diff lwr upr p adj
2-1 -5.833333 -14.075867 2.409201 0.1738314
3-1 3.700000 -3.539501 10.939501 0.3684099
3-2 9.533333 1.651963 17.414703 0.0201421
> plot(TukeyHSD(lm12))
