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Dennis_Churaev [7]
3 years ago
13

Describe the effects on the graph of y = f(x) when y = –f(x + 4)

Mathematics
1 answer:
Anni [7]3 years ago
7 0

Answer:

Step-by-step explanation:

Graph is shifted 4 units to the right and reflected over the x-axis.

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Leya [2.2K]
A) The vertices of the ABCD square are:

(2,2), (1(4), (3,5), and (4,3).

The reflection over the x-axys keeps the same x-coordinate and changes the y-coordinate ot its negative.

So, the vertices of the square A'B'C'D' are:

(2,-2), (1,-4), (3,-5), and (4,-3).

You can see the figure attached showing the four vertices.

<span>B. Is AB congruent to A'B'? Explain

Answer:

Yes, they are congruent. You can affirm that because reflections are transformations that do not change either sizes or angles, but keep them, so the two squares are congruent.

C. Is the area of ABCD ≈ area of A'B'C'D'? Explain.

Answer:

Yes, the two areas are equal since you have shown that the two figures are congruent.
</span>

8 0
3 years ago
Witch is true statement about the dot plot?
lora16 [44]

Answer:

Could you please supply a dot plot?

Step-by-step explanation:

Sorry if this is trouble

8 0
3 years ago
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Rainbow [258]

Answer:

B

Step-by-step explanation:

7 0
2 years ago
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A piano has 52 white keys. Write and use an equation to find the number of octaves on a piano keyboard.
Firdavs [7]
There are 7 keys in each octave (C,D,E,F,G,A and B) so it would make sense to divide 52 to 7.
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3 0
3 years ago
Read this E[2X^2 â€" Y].
djyliett [7]

Looks like a badly encoded/decoded symbol. It's supposed to be a minus sign, so you're asked to find the expectation of 2<em>X </em>² - <em>Y</em>.

If you don't know how <em>X</em> or <em>Y</em> are distributed, but you know E[<em>X</em> ²] and E[<em>Y</em>], then it's as simple as distributing the expectation over the sum:

E[2<em>X </em>² - <em>Y</em>] = 2 E[<em>X </em>²] - E[<em>Y</em>]

Or, if you're given the expectation and variance of <em>X</em>, you have

Var[<em>X</em>] = E[<em>X</em> ²] - E[<em>X</em>]²

→   E[2<em>X </em>² - <em>Y</em>] = 2 (Var[<em>X</em>] + E[<em>X</em>]²) - E[<em>Y</em>]

Otherwise, you may be given the density function, or joint density, in which case you can determine the expectations by computing an integral or sum.

6 0
3 years ago
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