14 is a <u>multiple</u> of 7 and 14.
It is also <u>the least common multiple</u> of 7 and 14.
Answer:
Step-by-step explanation:
<u>Let the base function be</u>
<u>It has the vertex at (3, -3), so it is translated 3 units right and 3 units down:</u>
It has zero's at (2, 0) and (4, 0)
<u>Substitute and solve for a:</u>
- a(x - 3)^2 - 3 = 0
- a(2 - 3)^2 = 3
- a = 3
or
<u>So the function is:</u>
<em>See the attached</em>
Answer:
We have the following function:
f (x) = 5 • 2 ^ x
We can make a table to represent the function.
For this, we will evaluate the function for some values of x.
We have then:
f (0) = 5 • 2 ^ 0 = 5 * 1 = 5
f (1) = 5 • 2 ^ 1 = 5 * 2 = 10
f (2) = 5 • 2 ^ 2 = 5 * 4 = 20
f (3) = 5 • 2 ^ 3 = 5 * 8 = 40
f (4) = 5 • 2 ^ 4 = 5 * 16 = 80
f (5) = 5 • 2 ^ 5 = 5 * 32 = 160
Answer:
The table that represents the function is:
0 5
1 10
2 20
3 40
4 80
5 160
Answer:
The approximate probability of getting 100000 views or more in January if we assume view counts from day-to-day are independent = 0.22254
Step-by-step explanation:
January has 31 days.
The average number of views per day = 3022 views per day.
In terms of hourly basis, the average number of views = 3022/24 ≈ 126 views per hour
Then we need to find the probability that the number of views in January is equal to or exceeds 100000.
100000 views in January = 100000/31 = 3225.81 ≈ 3226 views per day
On an hourly basis, 3226 views per day ≈ 135 views per hour.
So, mean = λ = 126 views per hour
x = 135 views per hour.
Using Poisson's distribution function
P(X = x) = (e^-λ)(λˣ)/x!
P(X ≥ x) = Σ (e^-λ)(λˣ)/x! (Summation From x to the end of the distribution)
But it's easier to obtain
P(X < x) = Σ (e^-λ)(λˣ)/x! (Summation From 0 to x)
P(X ≥ x) = 1 - P (X < x)
Putting λ = 126 views/hour and x = 135 views/hour in the Poisson distribution formula calculator
P(X ≥ 135) = 0.22254
Answer:
12
Step-by-step explanation:
