coin3 <- data.frame(
N_Heads = c(0, 1, 2, 3),
C_Heads = c(1, 3, 3, 1)
)
attach(coin3)
coin3$P_Heads <- C_Heads/sum(C_Heads)

attach(coin3)
E_X = sum(N_Heads * P_Heads)
E_X

```
The following objects are masked from coin3 (pos = 3):
C_Heads, N_Heads
```

options(repr.plot.width=7, repr.plot.height=4)
barplot(coin3$P_Heads, names = coin3$N_Heads, main = "Discrete Probability Distribution of flipping three coins")

barplot(prop.table(table(c('HHH', 'HHT', 'HHT','HTT','HHT','HTT','HTT', 'TTT'))),
main = "Discrete Probability Distribution of tossing three coins",
xlab = "x = Number of Heads", ylab = "P(x)", col = "#6633FF")

Uniform_Random_Number <- runif(10000, -2, 1)
hist(Uniform_Random_Number, probability = TRUE)

normal_dist <- function(x, b){
mu = mean(x)
sigma = sd(x)
x1 <- seq(mu - 6*sigma, mu + 6*sigma, length = 100)
# Normal curve
fun <- dnorm(x1, mean = mu, sd = sigma)
# Histogram
options(repr.plot.width=7, repr.plot.height=4)
hist(x, prob = TRUE, col = "white", breaks = b,
xlim = c(mu - 6*sigma, mu + 6*sigma),
ylim = c(0, max(fun)),
main = "Histogram overlayed with normal curve")
lines(x1, fun, col = 2, lwd = 2)
}

data(chickwts)
attach(chickwts)
normal_dist(chickwts$weight, 15)

```
The following objects are masked from chickwts (pos = 3):
feed, weight
```

shapiro.test(chickwts$weight)

chickwts$zscore = (weight - mean(weight))/sd(weight)
tail(chickwts)

z_sophiaV = (160 - 151)/7
z_sophiaQ = (157 - 153)/7.67
cat("Z Score for Verbal= ", round(z_sophiaV,2), "\nZ Score for Quantitative Reasoning= ", round(z_sophiaQ, 2))

```
Z Score for Verbal= 1.29
Z Score for Quantitative Reasoning= 0.52
```

z_sophiaV = (160 - 151)/7
z_sophiaQ = (157 - 153)/7.67
cat("Z Score for Verbal= ", round(z_sophiaV,2), "\nZ Score for Quantitative Reasoning= ", round(z_sophiaQ, 2))

```
Z Score for Verbal= 1.29
Z Score for Quantitative Reasoning= 0.52
```