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2 years ago

6

Jason grew from 36 inches to 40 inches in 1 year. By percent did his growth increase? Round your answer off to the nearest tenth

s

1 answer:

IrinaVladis [17]2 years ago

7 0

Answer:

33%

Step-by-step explanation:

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a golfer needs to hit a ball a distance of 500 feet, but there is a 60-foot tall tree that is 100 feet in front of the point whe

jok3333 [9.3K]

We can create a parabola equation of the trajectory using the vertex form:

y = a (x – h)^2 + k

The center is at h and k, where h and k are the points at the maximum height so:

h = 250

k = 120

Therefore:

y = a (x – 250)^2 + 120

At the initial point, x = 0, y = 0, so we can solve for a:

0 = a (0 – 250)^2 + 120

0 = a (62,500) + 120

a = -0.00192

So the whole equation is:

y = -0.00192 (x – 250)^2 + 120

So find for y when the golf ball is above the tree, x = 400:

y = -0.00192 (400 - 250)^2 + 120

y = 76.8 ft

So the ball cleared the tree by:

76.8 ft – 60 ft = 16.8 ft

Answer:

16.8 ft

8 0

2 years ago

Consider the quadratic function.

goblinko [34]

A standard form for the quadratic function is
f(x) = ax² + bx + c
where the coefficients are a,b, and c.

The given quadratic function is
f(p) = p² - 8p - 5
When this function is compared to the standard form, with p replacing x, we obtain
a = 1 because the leading term is 1*p²,
b = -8 because the linear term is -8*p,
c = -5 because the constant term is -5.

Answer:
Of the answers given the correct one is the 3rd one:
a = 1, b = -8, c = -5.

4 0

2 years ago

The linearized regression equation for an exponential data set is log ŷ = 0.14x + 0.4, where x is the number of years and y is t

djyliett [7]

Option C: $316$ is the predicted population when $x=15$

Explanation:

The regression equation for an exponential data is $\log y=0.14x+0.4$

Where x is the number of years and

y is the population

We need to determine the predicted population when $x=15$

The population x can be determined by substituting $x=15$ in the equation $\log y=0.14x+0.4$

Thus, we have,

$\log y=0.14(15)+0.4$

$\log y=2.1+0.4$

$\log y=2.5$

Using the logarithmic definition $\log _{a}(b)=c$ then $b=a^{c}$

$\log _{10}(y)=2.5 \Rightarrow y=10^{2.5}$

$y=316.22776 \ldots$

Rounding off to the nearest whole number, we get,

$y=316$

Thus, the predicted population when $x=15$ is 316

Hence, Option C is the correct answer.

7 0

2 years ago

A cone-shaped pile of gravel has a diameter of 30 m and a height of 9.1 m.

Arte-miy333 [17]

D/2=r
vcone=(1/3)hpir^2
given
d=30
h=9.1
d/2=30/2=15=r

v=(1/3)9.1pi15^2
v=(9.1/3)pi225
v=682.5pi
use 3.141592 to aprox pi
v=2144.13654
the closese is the first one
answer would be 2120 m³

6 0

2 years ago

Read 2 more answers

A new curing process developed for a certain type of cement results in a mean compressive strength of 5000 kilograms per square

Sedbober [7]

Answer:

$\alpha =0.0668$

Step-by-step explanation:

Data given and notation

The info given by the problem is:

$n=25$ the random sample taken

$\mu =5000$ represent the population mean

$\sigma =100$ represent the population standard deviation

The critical region on this case is $\bar X$ so then if the value of $\bar X \geq 4970$ we fail to reject the null hypothesis. In other case we reject the null hypothesis

Null and alternative hypotheses to be tested

We need to conduct a hypothesis in order to determine if the true mean is 5000, the system of hypothesis would be:

Null hypothesis: $\mu = 5000$

Alternative hypothesis: $\mu \neq 5000$

Let's define the random variable X ="The compressive strength".

We know from the Central Limit Theorem that the distribution for the sample mean is given by:

$\bar X \sim N(\mu , \frac{\sigma}{\sqrt{n}})$

Find the probability of committing a type I error when H0 is true.

The definition for type of error I is reject the null hypothesis when actually is true, and is defined as $\alpha$ the significance level.

So we can define $\alpha$ like this:

$\alpha= P(Error I)= P(\bar X$

And in order to find this probability we can use the Z score given by this formula:

$Z=\frac{\bar X -\mu}{\frac{\sigma}{\sqrt{n}}}$

And the value for the probability of error I is givn by:

$\alpha= P(\bar X$

4 0

2 years ago