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Tom [10]
3 years ago
8

What would the points (400,10) represent on the graph​

Mathematics
2 answers:
Oxana [17]3 years ago
7 0
What graph and what are you measuring. If either of those don’t matter 400 would be x and 10 would be y
olga nikolaevna [1]3 years ago
4 0

Answer:

No solution

Step-by-step explanation:

You didn't include enough information

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Korey is planning to open a comic book store near his home. After completing a population survey for 3,520 homes in a 5 mile rad
Lisa [10]
The is answer is definitely D, because he computed the total population of 3,320 in the survey of his targeted people that he specified which was grade school and high school. In order for his computation to work for his market analysis he used the radius of 5m and the 75% he wanted to achieve.
3 0
3 years ago
Please i need help finishing this test i’ll give brainly to a correct answer
rjkz [21]

imma go with my gut and say b :?

7 0
2 years ago
Which definite integral approximation formula is this: the integral from a to b of f(x)dx ≈ (b-a)/n * [<img src="https://tex.z-d
Stella [2.4K]

The answer is most likely A.

The integration interval [<em>a</em>, <em>b</em>] is split up into <em>n</em> subintervals of equal length (so each subinterval has width (<em>b</em> - <em>a</em>)/<em>n</em>, same as the coefficient of the sum of <em>y</em> terms) and approximated by the area of <em>n</em> rectangles with base (<em>b</em> - <em>a</em>)/<em>n</em> and height <em>y</em>.

<em>n</em> subintervals require <em>n</em> + 1 points, with

<em>x</em>₀ = <em>a</em>

<em>x</em>₁ = <em>a</em> + (<em>b</em> - <em>a</em>)/<em>n</em>

<em>x</em>₂ = <em>a</em> + 2(<em>b</em> - <em>a</em>)/<em>n</em>

and so on up to the last point <em>x</em> = <em>b</em>. The right endpoints are <em>x</em>₁, <em>x</em>₂, … etc. and the height of each rectangle are the corresponding <em>y </em>'s at these endpoints. Then you get the formula as given in the photo.

• "Average rate of change" isn't really relevant here. The AROC of a function <em>G(x)</em> continuous* over an interval [<em>a</em>, <em>b</em>] is equal to the slope of the secant line through <em>x</em> = <em>a</em> and <em>x</em> = <em>b</em>, i.e. the value of the difference quotient

(<em>G(b)</em> - <em>G(a)</em> ) / (<em>b</em> - <em>a</em>)

If <em>G(x)</em> happens to be the antiderivative of a function <em>g(x)</em>, then this is the same as the average value of <em>g(x)</em> on the same interval,

g_{\rm ave}=\dfrac{G(b)-G(a)}{b-a}=\dfrac1{b-a}\displaystyle\int_a^b g(x)\,\mathrm dx

(* I'm actually not totally sure that continuity is necessary for the AROC to exist; I've asked this question before without getting a particularly satisfying answer.)

• "Trapezoidal rule" doesn't apply here. Split up [<em>a</em>, <em>b</em>] into <em>n</em> subintervals of equal width (<em>b</em> - <em>a</em>)/<em>n</em>. Over the first subinterval, the area of a trapezoid with "bases" <em>y</em>₀ and <em>y</em>₁ and "height" (<em>b</em> - <em>a</em>)/<em>n</em> is

(<em>y</em>₀ + <em>y</em>₁) (<em>b</em> - <em>a</em>)/<em>n</em>

but <em>y</em>₀ is clearly missing in the sum, and also the next term in the sum would be

(<em>y</em>₁ + <em>y</em>₂) (<em>b</em> - <em>a</em>)/<em>n</em>

the sum of these two areas would reduce to

(<em>b</em> - <em>a</em>)/<em>n</em> = (<em>y</em>₀ + <u>2</u> <em>y</em>₁ + <em>y</em>₂)

which would mean all the terms in-between would need to be doubled as well to get

\displaystyle\int_a^b f(x)\,\mathrm dx\approx\frac{b-a}n\left(y_0+2y_1+2y_2+\cdots+2y_{n-1}+y_n\right)

7 0
3 years ago
NEED HELP FAST PLEASE.
My name is Ann [436]
I'm pretty sure its A but it could be B
5 0
3 years ago
The least-squares regression line is_____________. A. the line that passes through the most data points.B. the line that makes t
Elanso [62]

Answer:

<h3>The option B) is correct.</h3><h3>That is the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible is correct answer</h3>

Step-by-step explanation:

Given that " The least-squares regression line "

The least-squares regression line is <u>the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.</u>

Therefore option B) is correct

3 0
3 years ago
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