This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.

plot(cars)

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

EXAM 2

It reads the following:

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.

# Vectors
fg_made <- c(18, 7, 6, 9, 10, 13)
fg_made
[1] 18  7  6  9 10 13
fg_attempted <- c(36, 23, 12, 18, 24, 22)
fg_attempted
[1] 36 23 12 18 24 22
# Field goal percentage per game
fg_percentages <- (fg_made / fg_attempted) * 100

# Average field goal percentage across the six games
average_fg_percentage <- mean(fg_percentages)

# Output the result
fg_percentages
[1] 50.00000 30.43478 50.00000 50.00000 41.66667 59.09091
average_fg_percentage
[1] 46.86539
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