Answer:
Its either 72 or 162. Dont know which one it is so plx tell me im right with either one!.
Step-by-step explanation:
Step-by-step explanation:
The axis of symmetry: is the line that makes the parabola split in exactly half and lines up with the vertex. For that parabola x=1 is the line of symetry.
The vertex is where the minimum of the graph is, on this graph you can eyeball it to be (1,-9)
The x-intercept is where y is 0 so that's where the lines intersex with the x-axis. (-2,0) and (4,0)
The y-intercept of the function is where x is 0 and where the parabola intersects with the y-axis. On this graph it would be (0,-8)
Hope that helps :)
700x + 375(27 - x) = 15000
Solve for x
X=15 business class
Economy class 27-15=12
It would take 16 days
explanation
if it takes 15 for 4 people to work 8 hours to complete the task, it would be a total of 480 hours. I multiplied 15x4x8. i multiplied 5x6 to get 30. I have to find the third value so i divided 30 from 480 to get 16.
Answer:
The probability table is shown below.
A Poisson distribution can be used to approximate the model of the number of hurricanes each season.
Step-by-step explanation:
(a)
The formula to compute the probability of an event <em>E</em> is:

Use this formula to compute the probabilities of 0 - 8 hurricanes each season.
The table for the probabilities is shown below.
(b)
Compute the mean number of hurricanes per season as follows:

If the variable <em>X</em> follows a Poisson distribution with parameter <em>λ</em> = 7.56 then the probability function is:

Compute the probability of <em>X</em> = 0 as follows:

Compute the probability of <em>X</em> = 1 as follows:

Compute the probabilities for the rest of the values of <em>X</em> in the similar way.
The probabilities are shown in the table.
On comparing the two probability tables, it can be seen that the Poisson distribution can be used to approximate the distribution of the number of hurricanes each season. This is because for every value of <em>X</em> the Poisson probability is approximately equal to the empirical probability.