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natali 33 [55]
2 years ago
7

The coordinates of the vertices of quadrilateral HIJK are H (-2, -1), I (2, 1), J (5, 0), and K (-1, -3). Isabella states that q

uadrilateral HIJK is a parallelogram. Is Isabella correct? Support your answer and show all work.
Mathematics
1 answer:
Firlakuza [10]2 years ago
7 0

Answer:

Step-by-step explanation:

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Find the center-radius form of the equation of the circle described.
Kitty [74]

Answer:

Correct answer:  (x-√2)² + (y-√5)² = 3

Step-by-step explanation:

Given data: Center (x,y) = (√2,√5) and r = √3

The canonical or cartesian form of the equation of the circle is:

( x-p )² + ( y-q )² = r²  

Where p is the x coordinate of the center, q is the y coordinate of the center and r is the radius of the circle.

God is with you!!!

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3 years ago
What is the average rate of change of f over the interval [3,4]?
masya89 [10]

Answer:

4

Step-by-step explanation:

The average rate of change of f(x) in the closed interval [ a, b ] is

\frac{f(b)-f(a)}{b-a}

Here [ a, b ] = [ 3, 4 ] , then

f(b) = f(4) = - 1 ← from table

f(a) = f(3) = - 5 ← from table , then

average rate of change = \frac{-1-(-5)}{4-3} = \frac{-1+5}{1} = 4

3 0
3 years ago
A piece of copper with a volume of 8.25 cm³ has a mass of 73.92 g. A piece of iron with a volume of 5 cm³ and has a mass of 39.3
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3 years ago
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Step-by-step explanation:

5 0
2 years ago
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The least squares regression line minimizes the sum of the:
lorasvet [3.4K]

Answer:

d) Squared differences between actual and predicted Y values.

Step-by-step explanation:

Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.

For example,

suppose a = 10 and b = 7. If X is 10, then  predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.

It minimize the squared difference between actual and predicted value.

3 0
3 years ago
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