data:image/png;base64,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falsetruedata:image/png;base64,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of all items
Read each item in the list
Add it to "total"
After traversal, show totalAverage of all of numbers:
Read each item in the list
Add it to total
After traversal, calculate average (total / # of items)
Contains negative?:
Read each item in the list
If item negative
Say True
stop traversal
End if
After traversal, Say False
Sorted?:
Read each item except the last one
If next item < item
Say False
stop traversal
End if
After traversal, Say True
Largest:
largest = first item - 1
Read each item in the list
If item > largest
largest = item
End if
After traversal, Say largest
Convert negative to abs:
Read each item in the list
If item negative
Replace with abs of item
End if
After traversal show modified list
Take even numbers:
newlist = empty list
Read each item in the list
If item even
Append item to newlist
End if
After traversal, show newlist
4,5,6,7,8