6.
1 answer:
Answer:
Step-by-step explanation:
Comparing the triangles in the given questions, the following can be deduced:
5. The scale factor = =
≠
So that;
QR is similar to UT, PR is similar to US, but PQ is NOT similar to TS.
Thus, the triangles are NOT similar.
6. Scale factor = =
=
= 1.5
So that;
QR is similar to UT, PR is similar to US, but PQ is similar to TS.
Thus, the triangles are similar by Side-Side-Side (SSS).
7. Here, an exact scale factor can not be determined. Thus, the triangles are NOT similar.
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Let z = 3x+2. Then, by substitution, your equation is
.. z^2 +7z -8 = 0
_____
This equation can be factored as
.. (z +8)(z -1) = 0
.. x = -8 or +1
Then
.. 3x +2 = -8 or +1
.. 3x = -10 or -1
.. x = -10/3 or -1/3
.. z^2 +7z -8 = 0
_____
This equation can be factored as
.. (z +8)(z -1) = 0
.. x = -8 or +1
Then
.. 3x +2 = -8 or +1
.. 3x = -10 or -1
.. x = -10/3 or -1/3
Answer:
Step-by-step explanation:
Hello!
To compete in the touch screen phone market a manufacturer aims to release a new touch screen with a battery life said to last more than two hours longer than the leading product which is the desired feature in phones.
To test this claim two samples were taken:
Sample 1
X: battery lifespan of a unit of the new product (min)
n= 93 units of the new product
mean battery life X[bar]= 8:53hs= 533min
S= 84 min
Sample 2
X: battery lifespan of a unit of the leading product (min)
n= 102 units of the leading product
mean battery life X[bar]= 5:40 hs = 340min
S= 93 min
The population variances of both variances are unknown and distinct.
To test if the average battery life of the new product is greater than the average battery life of the leading product by 2 hs (or 120 min) the parameters of interest will be the two population means and we will test their difference, the hypotheses are:
H₀: μ₁ - μ₂ ≤ 120
H₁: μ₁ - μ₂ > 120
Considering that there is not enough information about the distribution of both variables, but both samples are big enough, we can apply the central limit theorem and approximate the distribution of both sample means to normal, this way we can use the standard normal:
Z≈N(0;1)
I hope this helps!