Today I saw a Reel talking about the Central Limit Theorem (https://en.wikipedia.org/wiki/Central_limit_theorem). In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution (https://en.wikipedia.org/wiki/Normal_distribution#Standard_normal_distribution). My project is a simple visualizer of this theory. A line graph is drawn, and several points are placed at the center. For every tick, each point can either move left, right, or stay on the line graph. After several ticks, a curve is drawn. You'll notice that every time you run the project, the curve looks almost the same. The data is very predictable. Even though the points move at random, you still get almost the same result every time. Common applications of the Central Limit Theorem include surveys, risk management, quality control predictions, and much more. If you click the stage before running the project or after the simulation runs, you can change some of the parameters. Axis range: Length of the line graph. Example: a value of 10 makes the line graph 21 spaces long, -10 to 10 Movements per trial: How many times each point can move left, right, or stay. Example: a value of 4 could make a point randomly move left, left, right, stay, or right, left, right, left, or stay, stay, stay, left, etc. Sample size: How many points are placed on the graph. Example: a value of 40 would place 40 points on the graph.data:image/png;base64,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Today I saw a Reel talking about the Central Limit Theorem (https://en.wikipedia.org/wiki/Central_limit_theorem). In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution (https://en.wikipedia.org/wiki/Normal_distribution#Standard_normal_distribution). My project is a simple visualizer of this theory. A line graph is drawn, and several points are placed at the center. For every tick, each point can either move left, right, or stay on the line graph. After several ticks, a curve is drawn. You'll notice that every time you run the project, the curve looks almost the same. The data is very predictable. Even though the points move at random, you still get almost the same result every time. Common applications of the Central Limit Theorem include surveys, risk management, quality control predictions, and much more. If you click the stage before running the project or after the simulation runs, you can change some of the parameters. Axis range: Length of the line graph. Example: a value of 10 makes the line graph 21 spaces long, -10 to 10 Movements per trial: How many times each point can move left, right, or stay. Example: a value of 4 could make a point randomly move left, left, right, stay, or right, left, right, left, or stay, stay, stay, left, etc. Sample size: How many points are placed on the graph. Example: a value of 40 would place 40 points on the graph.
de:Kostüm aus Text _ Größe _ pt:um traje com o texto _ de tamanho _ ca:vestit amb el text _ i mida _ A72xydircstpen state90trailsStage0Stage0costumeobj_name(obj, name)
pl:kostium _ z tłem RGBA _ rozmiar tła _ §_costumesMenu2002Use this block to make "clickable" buttons:
de:animiere Setter _ Getter _ Delta _ über _ Sek. beschleunigt _ ca:modifica setter _ i getter _ amb delta _ en _ segons de forma _ pt:anima com modificador _ e inspector _ de _ em _ s de forma _ pl:animuj setter _ getter _ delta _ przez _ s przyspieszając _ 501
de:Grad _ ca:graus de _ radians pt:_ radianos em graus pl:stopnie _ 0.5
de:Fehler _ pt:assinala o erro _ pl:błąd _
de:gleite _ in _ Sek. beschleunigt: _ ca:llisca _ en _ segons de forma _ fr:glisser _ en _ sec. accélérer _ pt:desliza _ em _ s de forma _ pl:leć _ przez _ s przyspieszając _ 501linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elastic
de:animiere _ um _ in _ Sek. beschleunigt: _ ca:modifica _ amb _ en _ segons de forma _ pt:anima _ de _ em _ s de forma _ pl:animuj _ o _ przez _ s przyspieszając _ x positionsteps x position y position direction size ghost effect color effect saturation effect brightness effect fisheye effect whirl effect pixelate effect mosaic effect negative effect tempo volume balance501linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elasticdeltay positiondirectionsizetempovolumebalance1 12
de:Beschleunigung _ ca:de forma _ pt:a forma _ pl:przyspieszenie _ linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } sigmoid elasticquadratic-outquadratic-inquadratic-in-outcubic-outcubic-incubic-in-outquart-outquart-inquart-in-outsine-outsine-insine-in-outsigmoidelasticunknown easing function ""
de:für _ = _ bis _ _ in _ Sek. beschleunigt: _ _ ca:per _ = _ fins _ _ en _ segons de forma _ _ pt:para _ de _ a _ _ em _ s de forma _ _ pl:dla _ = _ do _ _ przez _ s przyspieszając _ _ 01001linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elastic
de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ pl:przechwyć _ _ cont
de:wirf _ ca:llança _ es:lanzar _ fr:lance _ pt:lança _ pl:wyrzuć _ catchtag
de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ pl:przechwyć _ _ cont
de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ pt:lança _ _ pl:wyrzuć _ _ catchtagThis is a breakpoint block, to be used in debugging. The Boolean input specifies the condition under which the program should pause. (Use True for an unconditional breakpoint.) The intent is that the script should have SHOW VARIABLE blocks for the process's local variables. After the pause, this block automatically hides all temporary variables.
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 pt:se _ , então _ e faz pausa em tudo $pause-1-255-220-0 pl:jeżeli _ wykonaj _ i pauzuj wszystko $pause-1-255-220-0 This block does nothing. Use it when you are calling a reporter for its side effect and don't care about its return value.
de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _ pt:ignora _ pl:ignoruj _ Call f(f(f(...(f(x))))) n times where the three input slots are n, f, and x from left to right. The # variable can be used inside f to represent how many times f has been called.
pt:o resultado de _ invocações em cascata de _ com argumento inicial _ _ ca:en cascada _ vegades _ _ _ pl:kaskada _ razy _ _ _ Call f(f(f(...(f(x))))) until condition is true, where the three input slots are condition, f, and x from left to right. The # variable can be used inside f or condition to indicate how many times f has been called.
pt:o resultado da invocação em cascata até que _ de _ com argumento inicial _ _ ca:en cascada fins _ _ _ _ pl:kaskada aż _ _ _ _ Returns the function x↦f(g(x)) where f and g are the two inputs.
pt:a composição de _ com _ ca:composa _ _ pl:skomponuj _ _ Like the built-in REPEAT UNTIL block, except that the ending condition is not tested until the script has been run the first time. So the script is run at least once.
pt:repete _ até que _ $loop-0.7 ca:repeteix _ fins _ pl:powtarzaj _ aż _ Run the script repeatedly, as long as the given condition is true. Runs the script at least once before testing the condition.
pt:repete _ enquanto _ $loop-0.7 ca:repeteix _ mentre _ pl:powtarzaj _ gdy _ Runs the script repeatedly, as long as the condition is true. Tests the condition before the first time the script is run. Like the built in REPEAT UNTIL except that in this block the condition must be true, not false.
pt:enquanto _ , repete _ $loop-0.7 pl:powtarzaj gdy _ _ Runs the script the specified number of times, like the built-in REPEAT block, but this one provides the # variable that can be used inside the script. Try REPEAT (200) MOVE (#) STEPS RIGHT 92 with the pen down.
pt:repete _ vezes _ _ $loop-0.7 ca:repeteix _ _ _ pl:powtórz _ _ _ The primitive FOR block uses an implicit step of ±1 depending on which of the starting and ending values is larger. This version allows you to provide an explicit step value. If the sign of the step input is incompatible with the ordering of the starting and ending values, the script will not be run at all.
pt:para _ de _ com passo _ a _ _ $loop-0.7 ca:per _ = _ incrementant _ fins _ _ pl:dla _ = _ co _ do _ _ 1110Provides LOOP as a function of one input that runs the body of the LET with A set to the function input, so the body can run itself recursively. See COPY block in Variables for an example of use.
pt:tendo _ o valor inicial _ , executa _ definido como _ ca:fes que _ sigui _ al _ _ pl:ustaw _ na _ w _ _ new value
de:führe _ mit Continuation aus
de:rufe _ mit Continuation auf 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8.725762141674693-210,-150210300.371428571428571442.603137823975566107010falsefalse