# Task 1: Create two vectors for Premium and Claims
Premium <- c(1000, 900, 800)
Claims <- c(1100, 780, 600)
print(Premium)
## [1] 1000 900 800
print(Claims)
## [1] 1100 780 600
# Task 2: Create a new vector called LossRatio = Claims / Premium
LossRatio <- Claims / Premium
# Print the Loss Ratio for each year
print(LossRatio)
## [1] 1.1000000 0.8666667 0.7500000
# Task 3: Calculate the mean Premium, mean Claims, and mean Loss Ratio
mean_premium <- mean(Premium)
mean_claims <- mean(Claims)
mean_lossratio <- mean(LossRatio)
# Print the results
cat("Mean Premium:", mean_premium, "\n")
## Mean Premium: 900
cat("Mean Claims:", mean_claims, "\n")
## Mean Claims: 826.6667
cat("Mean Loss Ratio:", mean_lossratio, "\n")
## Mean Loss Ratio: 0.9055556
# Data
Premium <- c(1000, 900, 800)
Claims <- c(1100, 780, 600)
LossRatio <- Claims / Premium
years <- c(2021, 2022, 2023)
# Task 4:Determine which years are above mean Loss Ratio
mean_lossratio <- mean(LossRatio)
above_mean <- years[LossRatio > mean_lossratio]
cat("Mean Loss Ratio:", mean_lossratio, "\n")
## Mean Loss Ratio: 0.9055556
cat("Years with higher Loss Ratio than mean:", above_mean, "\n\n")
## Years with higher Loss Ratio than mean: 2021
# Task 5: Create the matrix with correct values
InsuranceMatrix <- rbind(Premium, Claims, LossRatio)
colnames(InsuranceMatrix) <- c("2021", "2022", "2023")
cat("Insurance Matrix:\n")
## Insurance Matrix:
print(InsuranceMatrix)
## 2021 2022 2023
## Premium 1000.0 900.0000000 800.00
## Claims 1100.0 780.0000000 600.00
## LossRatio 1.1 0.8666667 0.75