[color=#0000ff][size=150]NOTA: Si se les dificulta resolver los problemas pueden repasar [color=#0000ff][size=150]en el libro "Cálculo Aplicado" de Deborah Hughes[/size][/color] el ejemplo 1 de la página 3 y el ejemplo 1 de la página 8.[/size][/color]
[list][*]En los [color=#ff0000]problemas 1, 2 y 3 [/color]marca la opción que consideres correcta para cada pregunta.[/*][*]En el [color=#ff0000]problema 4[/color] escribe tu respuesta a las preguntas que se plantean.[/*][/list]
[color=#ff0000]Problema 1)[/color] [color=#0000ff]La población P de una ciudad (en millones) es una función del tiempo t, el número años desde 1950. [/color][br](Nota: La función se escribe P=f(t) porque no hay una fórmula explícita en términos de t. Pero debe entenderse que al evaluar f en algún valor de t, lo que obtenemos es un valor de P)
1.1 ¿Cuál es la variable independiente?
1.2 ¿Cuál es la variable dependiente?
1.3 ¿Qué significa “f(35)=12” en términos de la población de esta ciudad?
[color=#ff0000]Problema 2)[/color] La siguiente figura muestra la cantidad de nicotina, N=f(t), en miligramos que hay en el torrente sanguíneo de una persona, como una función del tiempo t (en horas) desde que la persona terminó de fumar un cigarrillo.[br][img]data:image/png;base64,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[/img]
2.1 Calcule f(3) e interprete el resultado en términos de la cantidad de nicotina.
2.2 Aproximadamente, ¿cuántas horas han transcurrido desde que el nivel de nicotina bajó a 0.1 mg?
2.3 ¿Cuál es el valor de la ordenada en el origen? ¿Qué representa en términos de la nicotina?
2.4 Si esta función tuviera una abscisa en el origen (donde la gráfica cortara al eje horizontal), ¿Qué representaría?
[color=#ff0000]Problema 3)[/color] [color=#0000ff]En las montañas de los Andes, en Perú, el número N de especies de murciélagos es una función de la altura la altura h (en pies sobre el nivel del mar). Es decir, N=f(h). [/color][br](Nota: la notación N=f(h) significa que al evaluar f en algún valor de h, lo que obtenemos es un valor de N.
3.1 ¿Cuál es la variable independiente?
3.2 ¿Cuál es la variable dependiente?
3.3 ¿Qué significa “f(500)=100” en términos del número de especies de murciélagos?
A los 500 pies sobre el nivel del mar, hay una diversidad de 100 especies de murciélagos en las montañas.
3.4 ¿Cuál es el significado de la ordenada en el origen “a” y la abscisa en el origen “b” en la siguiente gráfica de la función N=f(h)?[br][img]data:image/png;base64,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[/img]
La ordenada al origen "a" significa que al nivel del mar hay una diversidad de “a” especies de murciélagos en las montañas. Y la abscisa al origen "b" significa que a una altura de “b” pies sobre el nivel del mar no hay murciélagos en las montañas.[br]
[color=#ff0000]Problema 4)[/color] [color=#0000ff]En un día frío, al tiempo t=0 (medido en minutos) se deja un objeto a la intemperie. Su temperatura como función del tiempo, H=f(t) en °C, está graficada en la siguiente figura:[br][/color][img 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[/img]
4.1) ¿Cuál es la variable independiente?
4.2) ¿Cuál es la variable dependiente?
4.3) Explique qué significa "f(30)=10" en términos de temperatura? Incluya unidades para los números 30 y 10 en su respuesta.
A los 30 minutos después de que se dejó el objeto a la intemperie, la temperatura del objeto será de 10 °C.
4.4) Explique qué representan la ordenada en el origen “a” y la abscisa en el origen “b”, en términos de la temperatura del objeto y del tiempo de exposición.
La ordenada en el origen "a" es la temperatura inicial del objeto que se deja a la intemperie, es decir, "a" es la temperatura que tiene el objeto justo cuando lo ponen a la intemperie.[br][br]La abscisa en el origen "b" son los minutos que tardó el objeto en llegar a una temperatura de 0°C.