Assignment - 2

Data frame:

exam_score <- data.frame(
  ID = c(1, 2, 3, 4, 5),
  Name = c("Alice", "Bob", "David", "John", "Jenny"),
  Age = c(20, 25, 30, 22, 18),
  Score = c(100, 78, 90, 55, 81)
)

Answer to question number a:

new_rows <- data.frame(
  ID = c(6, 7),
  Name = c("Emily", "Mike"),
  Age = c(28, 35),
  Score = c(85, 70)
)

exam_score <- rbind(exam_score, new_rows)
exam_score$Income <- c(50000, 60000, 70000, 45000, 55000, 65000, 48000)

Answer to question number b:

cat("Age Summary Statistics:\n")

Age Summary Statistics:

cat("Min Age:", min(exam_score$Age), "\n") # Lowest age in the vector

Min Age: 18 

cat("Max Age:", max(exam_score$Age), "\n") # Highest age

Max Age: 35 

cat("Mean Age:", mean(exam_score$Age), "\n") # Average age

Mean Age: 25.42857 

cat("Sum of Ages:", sum(exam_score$Age), "\n") # Sum of all ages

Sum of Ages: 178 

cat("Median Age:", median(exam_score$Age), "\n") # Median age

Median Age: 25 

cat("Standard Deviation of Ages:", sd(exam_score$Age), "\n") # Standard deviation

Standard Deviation of Ages: 5.99603 

cat("Variance of Ages:", var(exam_score$Age), "\n") # Variance

Variance of Ages: 35.95238 

cat("Quantiles of Ages:", quantile(exam_score$Age), "\n") # Quantile

Quantiles of Ages: 18 21 25 29 35 

cat("\nScore Summary Statistics:\n")

Score Summary Statistics:

cat("Min Score:", min(exam_score$Score), "\n") # Lowest score in the vector

Min Score: 55 

cat("Max Score:", max(exam_score$Score), "\n") # Highest score

Max Score: 100 

cat("Mean Score:", mean(exam_score$Score), "\n") # Average score

Mean Score: 79.85714 

cat("Sum of Scores:", sum(exam_score$Score), "\n") # Sum of all scores

Sum of Scores: 559 

cat("Median Score:", median(exam_score$Score), "\n") # Median score

Median Score: 81 

cat("Standard Deviation of Scores:", sd(exam_score$Score), "\n") # Standard deviation

Standard Deviation of Scores: 14.46177 

cat("Variance of Scores:", var(exam_score$Score), "\n") # Variance

Variance of Scores: 209.1429 

cat("Quantiles of Scores:", quantile(exam_score$Score), "\n") # Quantile

Quantiles of Scores: 55 74 81 87.5 100 

cat("\nIncome Summary Statistics:\n")

Income Summary Statistics:

cat("Min Income:", min(exam_score$Income), "\n") # Lowest income in the vector

Min Income: 45000 

cat("Max Income:", max(exam_score$Income), "\n") # Highest income

Max Income: 70000 

cat("Mean Income:", mean(exam_score$Income), "\n") # Average income

Mean Income: 56142.86 

cat("Sum of Incomes:", sum(exam_score$Income), "\n") # Sum of all incomes

Sum of Incomes: 393000 

cat("Median Income:", median(exam_score$Income), "\n") # Median income

Median Income: 55000 

cat("Standard Deviation of Incomes:", sd(exam_score$Income), "\n") # Standard deviation

Standard Deviation of Incomes: 9263.343 

cat("Variance of Incomes:", var(exam_score$Income), "\n") # Variance

Variance of Incomes: 85809524 

cat("Quantiles of Incomes:", quantile(exam_score$Income), "\n") # Quantile

Quantiles of Incomes: 45000 49000 55000 62500 70000 

Answer to question number c:

cor_age_score <- cor(exam_score$Age, exam_score$Score)
cor_age_income <- cor(exam_score$Age, exam_score$Income)
cor_score_income <- cor(exam_score$Score, exam_score$Income)

print("Correlation between Age and Score:")

[1] "Correlation between Age and Score:"

print(cor_age_score)

[1] -0.1279532

print("Correlation between Age and Income:")

[1] "Correlation between Age and Income:"

print(cor_age_income)

[1] 0.2597717

print("Correlation between Score and Income:")

[1] "Correlation between Score and Income:"

print(cor_score_income)

[1] 0.5115078

Answer to question number d:

high_score_rows <- exam_score[exam_score$Score >= 80, ]
high_score_rows

Answer to question number e:

age_range_rows <- exam_score[exam_score$Age >= 20 & exam_score$Age <= 30, ]

print("Rows where the score is greater than or equal to 80:")

[1] "Rows where the score is greater than or equal to 80:"

high_score_rows

print("Rows with an age range of 20 to 30:")

[1] "Rows with an age range of 20 to 30:"

age_range_rows