Question 5:
Consider the following vectors representing the number of field goals
made and attempted by a basketball player in five games:
Field_Goals_Made <- c(18, 7, 6, 9, 10,13)
Field_Goals_Attempted <- c(36, 23, 12, 18, 24,22)
Calculate the field goal percentage for each game and select the
correct average field goal percentage for the five games.
avg_goals <- round(Field_Goals_Made/Field_Goals_Attempted, 3)
avg_goals
[1] 0.500 0.304 0.500 0.500 0.417 0.591
mean_avg <- round(mean(avg_goals), 2)
mean_avg
[1] 0.47
avg_goals <- round(sum(Field_Goals_Made)/sum(Field_Goals_Attempted),3)
avg_goals
[1] 0.467
Question 6
Consider the following vectors in R representing the number of
three-pointers made (3PM) and attempted (3PA) by a basketball player
over a seven-game span:
Three_Pointers_Made <- c(3, 5, 10, 6, 3, 7, 1)
Three_Pointers_Attempted <- c(9, 10, 18, 12, 11, 12, 11)
Calculate both the average of the individual per-game three-point
percentages and the overall (cumulative) three-point shooting percentage
across all seven games.
avg_pointers <- round(Three_Pointers_Made/ Three_Pointers_Attempted, 3)
avg_pointers
[1] 0.333 0.500 0.556 0.500 0.273 0.583 0.091
#individual per game three-point percentages
round(mean(avg_pointers), 2)
[1] 0.41
#cumulative three point shooting percentage
round(sum(Three_Pointers_Made)/ sum(Three_Pointers_Attempted), 3)
[1] 0.422
Question 7
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).
hits <- c(112, 124, 121, 106, 140)
at_bats <- c(400, 450, 380, 500, 402)
HR <- c(25, 22, 8, 20, 11)
BB <- c(50, 60, 19, 150, 55)
SO <- c(60, 65, 67, 92, 70)
Compute the On-Base Percentage (OBP) for each player and select the
player with the highest OBP.
(Formula: OBP = (Hits + Walks) / (At-Bats + Walks))
OBP <- round((hits + BB)/(at_bats + BB),3)
OBP
[1] 0.360 0.361 0.351 0.394 0.427
The highest OBP value comes from player 5
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