data:image/png;base64,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data:image/png;base64,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 report the mean value for your data. With our sample data, this would return 94.47LABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.de:drucke _ in Größe _
ca:etiqueta _ de mida _
es:etiqueta _ de tamaño _
fr:étiquette _ d'une taille de _
Hello!12de:behalte Elemente, die _ aus _
ca:manté els elements on _ de _
es:mantener los elementos donde _ de _
fr:garder les items tels que _ de _
1de:kombiniere mit _ die Elemente von _
ca:combina amb _ els elements de _
es:combinar con _ los elementos de _
fr:combine avec _ les items de _
1de:wenn _ dann _ sonst _
ca:si _ llavors _ si no _
es:si _ entonces _ sino _
fr:si _ alors _ sinon _
de:für _ = _ bis _ _
ca:per _ = _ fins _ _
es:para _ = _ hasta _ _
fr:pour _ allant de _ à _ _
110ide:füge Wörter zusammen _
ca:uneix les paraules _
es:unir las palabras _
fr:fusionne les mots _
121de:Liste $arrowRight Satz _
ca:llista $arrowRight frase _
es:lista $arrowRight frase _
fr:liste $arrowRight phrase _
de:Satz $arrowRight Liste _
ca:frase $arrowRight llista _
es:frase $arrowRight lista _
fr:phrase $arrowRight liste _
de:fange _ _
ca:agafa _ _
es:atrapar _ _
fr:attrape _ _
cont3de:wirf _
ca:llança _
es:lanzar _
fr:lance _
catchtagde:fange _ _
ca:agafa _ _
es:atrapar _ _
fr:attrape _ _
contde:wirf _ _
ca:llança _ _
es:lanzar _ _
fr:lance _ _
catchtagde:für jedes _ von _ _
ca:per cada _ de _ _
es:para cada _ de _ _
fr:pour chaque _ de _ _
de:falls _ dann _ und pause $pause-1-255-220-0
ca:si _ fes _ i atura-ho tot $pause-1-255-220-0
es:si _ haz _ y páralo todo $pause-1-255-220-0
fr:si _ faire _ et mettre tout en pause $pause-1-255-220-0
de:Wort $arrowRight Liste _
ca:paraula $arrowRight llista _
es:palabra $arrowRight lista _
fr:mot $arrowRight liste _
de:ignoriere _
ca:ignora _
es:ignorar _
fr:ignore _
de:Liste $arrowRight Wort _
ca:llista $arrowRight paraula _
es:lista $arrowRight palabra _
fr:liste $arrowRight mot _
de:Zahlen von _ bis _
ca:nombres des de _ a _
es:números de _ a _
fr:nombres de _ à _
1101de:wende _ an auf _
ca:mapeja _ sobre _
es:mapear _ sobre _
fr:appliquer _ à _
1Reports the Median - ours is 94.5You need to update. How do you get the middle value? What if the list is of even length? Odd length?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]].11111Reports a new list containing the items of the input list, but in the opposite order.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.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.1even itemsmerge11#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.11#11datacountmapmany##1#1data listscount#1datacount1Calculates the standard deviation. You do NOT need to alter this block.This statistic is calculated by the (number of successes) / (total number of outcomes)IL acceptance %Brown,6.6Columbia,5.1Cornell,10.6Dartmouth,7.9Harvard,4.5Penn,7.4Princeton,5.8Yale,5.96.7249999999999996.251.8026022855860360.525.99500000000000525.9950000000000056400006.255.9,6.64500hi