Question 1

r <- c(2,4,6,8,9)
s <- c(1,3,5,7,9)
print(r*s)
## [1]  2 12 30 56 81
print(r^s)
## [1]         2        64      7776   2097152 387420489
print(r%%s)
## [1] 0 1 1 1 0

Question 2

t <- 1
while (t<12) {
  print ("t")
  t=t+1
}
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"
## [1] "t"

Question 3

vecna <- 1:10
for(i in vecna)
  print(i)
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10

Question 4

seqna <- seq(0,6)
print(seqna)
## [1] 0 1 2 3 4 5 6
print(max(seqna))
## [1] 6
print(min(seqna))
## [1] 0

Question 5

mike <- c(1,1,2,3,5,5,6,6,6,7,9)
print(mean(mike))
## [1] 4.636364
print(median(mike))
## [1] 5
getmode <- function(mike){
  unimike <- unique(mike)
  unimike[which.max(tabulate(match(mike,unimike)))]
}
print(getmode(mike))
## [1] 6

Question 6

x <- c(15,17,18,20,21,24,26,29)
y <- c(73.4,74.5,75.8,77.2,79.1,78.8,80.4,81.1)
relation <- lm(y~x)

print(relation)
## 
## Call:
## lm(formula = y ~ x)
## 
## Coefficients:
## (Intercept)            x  
##     65.6635       0.5588
print(summary(relation))
## 
## Call:
## lm(formula = y ~ x)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76803 -0.64953 -0.09781  0.24647  1.70219 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 65.66348    1.50507  43.628 9.71e-09 ***
## x            0.55878    0.06931   8.062 0.000195 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8754 on 6 degrees of freedom
## Multiple R-squared:  0.9155, Adjusted R-squared:  0.9014 
## F-statistic: 64.99 on 1 and 6 DF,  p-value: 0.000195
a <- data.frame(x=19)
print(predict(relation,a))
##        1 
## 76.28025
plot(x, y, 
     main = "Regression Line: y vs x",
     xlab = "x values", 
     ylab = "y values",
     pch = 16, col = "blue")
abline(relation, col = "red", lwd = 2)

# Question 7

pumpkin <- c(8,4,7,9,1,5)
lbls <- c("Older", "Younger", "Mom", "Dad", "Baby", "Middle")
pie(pumpkin, labels = lbls, main="Family Pumpkin Pie Chart")

# Question 8

data(mtcars)
boxplot(disp ~ gear, data = mtcars,
        main = "Displacement by Gear",
        xlab = "Gear",
        ylab = "Displacement")