data:image/png;base64,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totalt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AAQKBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==Sum of all items
Read each item in the list
Add it to "total"
After traversal, show totaltotal0total0Average of all of numbers:
Read each item in the list
Add it to total
After traversal, calculate average (total / # of items)
falseContains 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
largest0Largest:
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
13,2,11,4,9002,4