Exam 2

Consider the following vectors representing the number of field goals made and attempted by a basketball player in five games:

Field Goals Made: c(8, 7, 6, 9, 10) Field Goals Attempted: c(15, 12, 18, 14, 13)

Calculate the field goal percentage for each game and select the correct average field goal percentage for the five games.

#Defined vectors representing number of field goals made and attempted by a basketball player in 5 games
Field_Goals_Made <-c(8, 7, 6, 9, 10)
Field_Goals_Attempted <-c(15, 12, 18, 14, 13)

#calculate the field goal percentage for each game
Field_Goal_Percentage <- (Field_Goals_Made/Field_Goals_Attempted)*100
Field_Goal_Percentage

[1] 53.33333 58.33333 33.33333 64.28571 76.92308

Consider the following vectors representing the number of three-pointers made and attempted by a basketball player in five games:

Three-Pointers Made: c(4, 5, 3, 6, 7) Three-Pointers Attempted: c(9, 10, 8, 11, 12)

Calculate the three-point shooting percentage for each game and select the correct average three-point shooting percentage for the five games.

#Define the vectors representing the number of three-pointers made and attempted 
Three_Pointers_Made <- c(4, 5, 3, 6, 7)
Three_Pointers_Attempted <- c(9, 10, 8, 11, 12)

#Calculate the three-point shooting percentage for each game
Three_Point_Shooting_Percentage <-(Three_Pointers_Made/Three_Pointers_Attempted) * 100
Three_Point_Shooting_Percentage

[1] 44.44444 50.00000 37.50000 54.54545 58.33333

Consider the following dataset representing the performance of baseball players in a season. It includes the following variables: PlayerID, Hits, At-Bats, Home Runs (HR), Walks (BB), and Strikeouts (SO).

PlayerID Hits At-Bats HR BB SO

1             120       400           15          40     80

2             140       450           12          50     75

3             110       380            8            30    60

4             160       500           20          60     90

5             130       420           10          45     70

Compute the on-base percentage (OBP) for each player and select the player with the highest OBP.

1. Player 1 b) Player 2 c) Player 3 d) Player 4 e) Player 5

To calculate OBP, you can use the following formula:

OBP = (Hits + Walks) / (At-Bats + Walks)

#Create a data frame with the player statistics
data <- data.frame(
  PlayerID = 1:5, 
  Hits = c(120, 140, 110,160, 130),
  At_Bats = c(400, 450, 380, 500,420),
  HR = c(15, 12, 8, 20, 10),
  BB = c(40,50,30,60,45),
  SO = c(80,75,60,90,70)
)

data$OBP <-(data$Hits + data$BB) / (data$At_Bats + data$BB)
data$OBP

[1] 0.3636364 0.3800000 0.3414634 0.3928571 0.3763441

#Find the player with the highest OBP
player_with_highest_OBP <- data$PlayerID[which.max(data$OBP)]

#Print the player with the highest OBP
player_with_highest_OBP

[1] 4

#Player 4 has the highest OBP