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andrew11 [14]
3 years ago
15

How does the slope formula help you classify figures

Mathematics
1 answer:
Tomtit [17]3 years ago
8 0

Using the distance formula, you can determine the lengths of the sides of the shape.  This will help determine if triangles are equilateral, isosceles or scalene; or if quadrilaterals are squares, rectangles, rhombi, or isosceles trapezoids; or if other polygons are regular polygons.  The slope formula will tell help you determine if two sides are parallel.  In a quadrilateral, this will help you determine if the figure is a parallelogram or a trapezoid.


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Please find answer. <br>​
Inessa [10]
Midpoint formula
(x2-x1 / 2) , (y2-y1/ 2)
x = 4-(-2) / 2 = 3
y= -6-2 / 2= -4
midpoint (3, -4)
8 0
3 years ago
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Solve pls brainliest
12345 [234]

Answer:

36.5 hours

Step-by-step explanation:

It is 36.5 because 7.3x5 would equal 36.5 which tells you that Salma worked 36.5 hours in the last five days.

8 0
3 years ago
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A researcher wishes to estimate the proportion of adults who have​ high-speed Internet access. What size sample should be obtain
slega [8]

Answer: 8359

Step-by-step explanation:

The formula for sample size needed for interval estimate of population proportion :-

n=p(1-p)(\frac{z_{\alpha/2}}{E})^2

Given : The significance level : \alpha=1-0.95=0.05

Critical value : z_{\alpha/2}}=z_{0.025}=\pm1.96

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Margin of error : E=0.01

Now, the required sample size will be :-

n=0.32(1-0.32)(\frac{1.96}{0.01})^2=8359.3216\approx8359

Hence, the required sample size = 8359

5 0
3 years ago
How to rank linear functions
Maurinko [17]

In linear algebra, the rank of a matrix

A

A is the dimension of the vector space generated (or spanned) by its columns.[1] This corresponds to the maximal number of linearly independent columns of

A

A. This, in turn, is identical to the dimension of the vector space spanned by its rows.[2] Rank is thus a measure of the "nondegenerateness" of the system of linear equations and linear transformation encoded by

A

A. There are multiple equivalent definitions of rank. A matrix's rank is one of its most fundamental characteristics.

The rank is commonly denoted by

rank

⁡

(

A

)

{\displaystyle \operatorname {rank} (A)} or

rk

⁡

(

A

)

{\displaystyle \operatorname {rk} (A)}; sometimes the parentheses are not written, as in

rank

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A

{\displaystyle \operatorname {rank} A}.

4 0
3 years ago
Benchmark estimate of 8.01
Nana76 [90]
I think it would just be 8 or 8.00
5 0
3 years ago
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