R Notebook

Primer ejercicio

#Ejercicio en la guía ANOVA

efi <- c(63,60,61,62,67,63,71,69,68,67,70,71)
set <- as.factor(c(1,1,1,1,2,2,2,2,3,3,3,3))
summary(aov(efi~set))

            Df Sum Sq Mean Sq F value  Pr(>F)   
set          2    126   63.00   11.34 0.00347 **
Residuals    9     50    5.56                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TukeyHSD(aov(efi~set))

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = efi ~ set)

$set
    diff       lwr       upr     p adj
2-1  6.0  1.346657 10.653343 0.0143238
3-1  7.5  2.846657 12.153343 0.0038138
3-2  1.5 -3.153343  6.153343 0.6537573

gplots::plotmeans(efi~set,n.label = F, ci.label = T)

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Segundo ejercicio

# Ejercicio Trainees

time <- c(9,12,11,14,13,10,6,9,9,10,12,14,11,13,11,9,8,11,7,8)
program <- as.factor(c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4))
fit0 <- aov(time~program)
summary (fit0)

            Df Sum Sq Mean Sq F value  Pr(>F)   
program      3  54.95   18.32   7.045 0.00311 **
Residuals   16  41.60    2.60                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TukeyHSD(fit0)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = time ~ program)

$program
    diff        lwr         upr     p adj
2-1 -3.0 -5.9176792 -0.08232082 0.0427982
3-1  0.4 -2.5176792  3.31767918 0.9788127
4-1 -3.2 -6.1176792 -0.28232082 0.0291638
3-2  3.4  0.4823208  6.31767918 0.0197459
4-2 -0.2 -3.1176792  2.71767918 0.9972140
4-3 -3.6 -6.5176792 -0.68232082 0.0133087

gplots::plotmeans(time~program, n.label = F, ci.label = T)