The answer is -7
I hope this helps
1 quart is 4 cups
so basically 4 cups make up 1 quart
how many quarts do 8 cups make up?
8/4=2
8 cups make up 2 quarts
Answer:
The answer to your question is Vertex (1, - 25)
x-intercepts = -4, 6
Step-by-step explanation:
Data
Function y = x² - 2x - 24
Vertex = ?
x- intercepts = ?
Process
1.- Factor to find the vertex
y = x² - 2x - 24
y + 24 = x² - 2x
y + 24 + (1)² = x² - 2x + (1)²
y + 24 + 1 = x² - 2x + 1
y + 25 = (x - 1)²
Vertex = (1, -25)
2.- See the graph below
From the graph, we get the x-intercepts
x₁ = -4 x₂ = 6
Answer:
is in proportion.
is not in proportion.
is in proportion.
is in proportion.
Step-by-step explanation:
The first example is and it is in proportion.
This is because, you will get the same simplest fraction () from the fraction by dividing its numerator and denominator by 5.
The second example is and it is not in proportion.
This is because, you can not get the simplest fraction () from the fraction after simplification.
The third example is and it is in proportion.
This is because, you will get the same simplest fraction () from the fraction by dividing its numerator and denominator by 2 and from the fraction by dividing its numerator and denominator by 3.
The fourth example is and it is in proportion.
This is because, you will get the same simplest fraction () from the fraction by dividing its numerator and denominator by 5. (Answer)
Answer:
(a) According to the central limit theorem, the distributions of the sample means of sufficiently large samples randomly selected from a population with mean, μ and standard deviation, σ with replacement will be normally distributed
Therefore, given that the size of the population from which the samples were selected (34 petri dishes) is comparable the sizes of the samples, (16 and 18), therefore, the samples are approximately normal
Also given that the petri dishes were prepared with growth medium designed to increase the growth of microorganisms, with an expected amount of growth, the samples therefore came from approximately normal distributions
Step-by-step explanation: