Homework 4 — Functions + Hypothesis Testing

This homework has two parts. Part 1 asks you to write your own functions. Part 2 applies hypothesis tests to two real scenarios.

Part 1 — Functions

# Q1. Write a function called calculate_area_of_rectangle that takes two parameters
#     (length, width) and returns the area (area = length * width).
#     Test it with 2 different inputs.
# I used the function function, set the inputs to be length and width, and multiplied them together. 

calculate_area_of_rectange <- function(length, width){length*width}

calculate_area_of_rectange(2, 4)

## [1] 8

calculate_area_of_rectange(3, 5)

## [1] 15

# Q2. Write a function called calculate_average that takes a numeric vector and returns
#     its average. Handle the case of an empty vector by printing a message.
#     (Hint: use if/else and length(x) == 0)
# I used the function function and used if else. I set the if condition to be length(x)==0 and the output to be a pasted message. I set the other condition to output the sum of the vector divided by its length. 

calculate_average <- function(x){
  if(length(x)==0){
    paste("The vector is empty")
  }
  else{
    sum(x)/length(x)
    }
}
calculate_average(c())

## [1] "The vector is empty"

calculate_average(c(2,5,2))

## [1] 3

# Q3. Write a function called check_even_odd that takes an integer and prints whether
#     it is "Even" or "Odd".
#     Test it on 14 and 27.
#     (Hint: use the %% modulus operator)
# I used the function function and if else. I set the if condition to be if there is no remained when dividing the input by 2 and the output to be "even." The output of the else condition is "odd." 
check_even_odd <- function(x){
  if(x%%2==0){
    paste("Even")
  }
  else{
    paste("Odd")
  }
}
check_even_odd(5)

## [1] "Odd"

check_even_odd(284)

## [1] "Even"

Part 2 — Hypothesis Testing

Problem 1 — Two-Proportion z-test

In 2017, of the 144,790 students who took the AP Biology exam, 84,200 were female. That same year, of the 211,693 students who took the AP Calculus AB exam, 102,598 were female.

Is there enough evidence to show that the proportion of female students taking the Biology exam is HIGHER than the proportion taking the Calculus AB exam? Test at the 5% level.

State your hypotheses:

H₀: p₁ = p₂
H₁: p₁ > p₂

# Q4. Run the appropriate two-proportion test.
#     (Hint: prop.test(c(84200, 102598), c(144790, 211693), alternative = "greater"))
# Following directions given in the hint. 
prop.test(c(84200, 102598), c(144790, 211693), alternative = "greater")

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(84200, 102598) out of c(144790, 211693)
## X-squared = 3234.9, df = 1, p-value < 2.2e-16
## alternative hypothesis: greater
## 95 percent confidence interval:
##  0.09408942 1.00000000
## sample estimates:
##    prop 1    prop 2 
## 0.5815319 0.4846547

# Q5. What is the p-value? At α = 0.05, do you reject H₀?
# If p < α, then we reject H₀.
# p < 2.2e-16, yes we reject H₀.

Q6. Write your conclusion in plain English (one or two sentences): We have strong evidence to suggest that the proportion of female students that took the Biology exam is higher than the proportion of female students that took the Calculus AB exam (p < 2.2e-16). —

Problem 2 — Paired t-test

A vitamin K shot is given to infants soon after birth. Researchers want to see if how the infants are handled can reduce the pain. They measured how long (in seconds) the infant cried after the shot. One group received the shot the conventional way; the other group received it while the mother held the infant.

Is there enough evidence to show that infants cried LESS on average when held by their mothers vs. the conventional method? Test at the 5% level.

Old <- c(63, 0, 2, 46, 33, 33, 29, 23, 11, 12, 48, 15, 33, 14, 51,
         37, 24, 70, 63, 0, 73, 39, 54, 52, 39, 34, 30, 55, 58, 18)

New <- c(0, 32, 20, 23, 14, 19, 60, 59, 64, 64, 72, 50, 44, 14, 10,
         58, 19, 41, 17, 5, 36, 73, 19, 46, 9, 43, 73, 27, 25, 18)