Answer:
Kindly check explanation
Step-by-step explanation:
Given the following :
Equation of regression line :
Yˆ = −114.05+2.17X
X = Temperature in degrees Fahrenheit (°F)
Y = Number of bags of ice sold
On one of the observed days, the temperature was 82 °F and 66 bags of ice were sold.
X = 82°F ; Y = 66 bags of ice sold
1. Determine the number of bags of ice predicted to be sold by the LSR line, Yˆ, when the temperature is 82 °F.
X = 82°F
Yˆ = −114.05+2.17(82)
Y = - 114.05 + 177.94
Y = 63.89
Y = 64 bags
2. Compute the residual at this temperature.
Residual = Actual value - predicted value
Residual = 66 - 64 = 2 bags of ice
Step-by-step explanation:
just subtract it
answer is 122.709
Answer:
P = (12/27)*(11/26) = 0.188
Step-by-step explanation:
Si todos tienen la misma probabilidad de estar en cargo de presidente y vicepresidente, entonces:
La probabilidad de que una mujer sea presidente, es igual al cociente entre el número de mujeres y el número total de personas:
p = 12/27
Ahora queremos calcular lo mismo para vicepresidente, y el cálculo es exactamente igual, con la diferencia de que en esta situación, ya hay una mujer en el cargo de presidente, entonces hay 11 mujeres que pueden ser vicepresidente, y un total de: (11 + 15 = 26 personas)
Entonces ahora la probabilidad es:
q = 11/26
La probabilidad conjunta (es decir, la probabilidad de que ambos eventos pasen) es igual al producto de las probabilidades individuales.
P = (12/27)*(11/26) = 0.188
So, first, you have to find the smallest number that is divisible by both 12 and 10. Which is 60. So, you need 5 boxes of trophies and 6 boxes of stands
Now to get how much it will cost you now multiply the respective costs by the amount of boxes so you get
5 x 10 = 50
6 x 6 = 36
Then you add both
50 + 36 = 86
He will spend a total of $86 on both trophies and stands.
