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Make a Program that calculates Range (max-min in list)Range: (find max block) - (find min block)sortedsamplelist-180126temp1stnumbertemp1stnumber1weird bug made me have to do this manuall yitem1MinMaxRangeMeanMedian-14015096so i have some space to work with A mode is the number that appears most often. If all numbers appear once, there is no mode. If numbers appear the same number of times, they are all modes.

Take any number of input lists, and create a new list containing the items of the input lists. So APPEND [A B] [C D] where the [...] are lists reports the list [A B C D] not [[A B] [C D]].

ca:concatena _ 11111Reports a new list containing the items of the input list, but in the opposite order.

ca:capgira _ 1inputresultReports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _ 1Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ copy of datasplitmerge11#1#2The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _ 11This block applies the given function to each item of the given list, like the primitive MAP block, but with two differences: 1. The # variable provided will contain the item number of the item currently being used in the given function: 1 for item 1, and so on. You read the variable in your function; don't set it. 2. You can provide more than one input list. If so, the lengths of all the lists should be the same, and the function you supply must expect as many inputs as you have lists (i.e., that many empty input slots in the function).

ca:_ mapeja _ sobre _ #11datacountmapmany##1#1data listscountThis block makes a new list containing selected items from the given list, like the primitive KEEP block. What's different is that it provides the # variable for use in your predicate function. It will contain the item number (in the original list) of the item being examined by the predicate.

ca:_ manté els elements on _ de _ #1datacount1This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _

25,55,59,59,80,5,20,85,74,59,96,66,52,83,54,28,545,20,25,28,52,54,54,55,59,59,59,66,74,80,83,85,96