Lab 2 Assignment

y=c(-2,-1,0,-1,0,1,0,1,2) 
group=c('g1','g1','g1','g2','g2','g2','g3','g3','g3') 

sub=as.factor(1:9)

ques1.df=data.frame(sub,y,group)

ques1.lm=lm(y~group., data=lab2)
ques1.anova=anova(ques1.lm)
print(ques1.anova)

## Analysis of Variance Table
## 
## Response: y
##           Df Sum Sq Mean Sq F value Pr(>F)
## group.     2      6       3       3  0.125
## Residuals  6      6       1

ques1.aov=aov(ques1.lm)
print(ques1.aov)

## Call:
##    aov(formula = ques1.lm)
## 
## Terms:
##                 group. Residuals
## Sum of Squares       6         6
## Deg. of Freedom      2         6
## 
## Residual standard error: 1
## Estimated effects may be unbalanced

summary(ques1.aov)

##             Df Sum Sq Mean Sq F value Pr(>F)
## group.       2      6       3       3  0.125
## Residuals    6      6       1

model.tables(ques1.aov, type='means')

## Tables of means
## Grand mean
##              
## 3.700743e-17 
## 
##  group. 
## group.
## g1 g2 g3 
## -1  0  1

boxplot(y~group., data=lab2)

Yes, the numbers match the ones we produced in class.

y=c(5, 12, 9, 8, 11, 1, 2, 7, 3, 8, 10, 13, 16, 12, 17) 
group=c('g1','g1','g1','g1','g1','g2','g2','g2','g2','g2','g3','g3','g3','g3','g3') 

sub=as.factor(1:15)

ques2.lm=lm(Y~Group, data=Lab2.2)
ques2.anova=anova(ques2.lm)
print(ques2.anova)

## Analysis of Variance Table
## 
## Response: Y
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## Group      2 220.93  110.47  12.996 0.0009929 ***
## Residuals 12 102.00    8.50                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ques2.aov=aov(ques2.lm)
print(ques2.aov)

## Call:
##    aov(formula = ques2.lm)
## 
## Terms:
##                    Group Residuals
## Sum of Squares  220.9333  102.0000
## Deg. of Freedom        2        12
## 
## Residual standard error: 2.915476
## Estimated effects may be unbalanced

summary(ques2.aov)

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Group        2  220.9   110.5      13 0.000993 ***
## Residuals   12  102.0     8.5                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

model.tables(ques2.aov, type='means')

## Tables of means
## Grand mean
##          
## 8.933333 
## 
##  Group 
## Group
##   g1   g2   g3 
##  9.0  4.2 13.6

plot(Y~Group,data=Lab2.2)

y=c(7, 8, 9, 10, 11, 5, 6, 7, 8, 9, 3, 4, 5, 6, 7, 11, 12, 13, 14, 15) 
group=c('A1B1','A1B1','A1B1','A1B1','A1B1','A1B2','A1B2','A1B2','A1B2','A1B2','A2B1','A2B1','A2B1','A2B1','A2B1','A2B2','A2B2','A2B2','A2B2','A2B2') 

sub=as.factor(1:20)
ques4.lm=lm(Y~A*B, data=Lab2.4)
ques4.anova=anova(ques4.lm)
print(ques4.anova)

## Analysis of Variance Table
## 
## Response: Y
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## A          1      5     5.0       2 0.1764632    
## B          1     45    45.0      18 0.0006207 ***
## A:B        1    125   125.0      50 2.646e-06 ***
## Residuals 16     40     2.5                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

par(mfrow=c(1,2))
plot(Y ~ A + B, data=Lab2.4)

interaction.plot(Lab2.4$A, Lab2.4$B, Lab2.4$Y)