data:image/png;base64,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data:image/png;base64,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

Should report the mean value for your data. With our sample data, this would return 94.47

LABEL 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!12

de:behalte Elemente, die _ aus _ ca:manté els elements on _ de _ es:mantener los elementos donde _ de _ fr:garder les items tels que _ de _ 1

de:kombiniere mit _ die Elemente von _ ca:combina amb _ els elements de _ es:combinar con _ los elementos de _ fr:combine avec _ les items de _ 1

de: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 _ _ 110i

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ 121

de: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 _ _ cont

de:wirf _ ca:llança _ es:lanzar _ fr:lance _ catchtag

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ cont

de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ catchtag

de: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 _ 110

de:wende _ an auf _ ca:mapeja _ sobre _ es:mapear _ sobre _ fr:appliquer _ à _ 1

Reports the Median - ours is 94.5

2You 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]].

1111

Reports a new list containing the items of the input list, but in the opposite order.

1inputresult

Reports 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.

Reports 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#2

The 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.

#11datacountmapmany#

1data listscount

#1datacount1

Calculates the standard deviation. You do NOT need to alter this block.

This statistic is calculated by the (number of successes) / (total number of outcomes)

2004,2.92005,9.12006,16.82007,252008,38.42009,43.42010,46.42011,522012,58.12013,59.62014,7138.4272727272727343.421.1898385127953970.363636363636363654939.1018181818182.9,9.1,16.8,25,38.4,43.4,46.4,52,58.1,59.6,7140.94