data:image/png;base64,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

false

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==

Sum of all items Read each item in the list Add it to "total" After traversal, show total

Average 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

trueSorted?: 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 13,2,11,4,19