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suter [353]
3 years ago
11

What does it mean when a line of best fit has a correlation coefficient close to 0?

Mathematics
1 answer:
Vinvika [58]3 years ago
7 0
It means the data is totally random havin no correlation at all
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Can someone help me with the question in the image. if correct i will mark as brainliest
Alex777 [14]

Answer:

Multiply~the~length~of~the~leg~by~\sqrt{2}~to~arrive~at~the~length of~the~hypotenuse = 6.

Step-by-step explanation:

To~find~the~length~of~the~ hypotenuse,~we~multiply~the~length~of~the~leg by~\sqrt{2}:

3\sqrt{2} *\sqrt{2}=3*2=6

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3 years ago
Write 6(9+2k)= -3 in written form
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27k+3=0

Step-by-step explanation:

Write in standard form.

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Please Help Me!<br> *Question Is Below Here*
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The product of this equation is 21 when the values of X and Y are imputed into the equation.
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Walter used the iterative process to determine that 13 is between 3.61 and 3.62.
Rom4ik [11]

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Step-by-step explanation:

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a. Among a shipment of 5,000 tires, 1,000 are slightly blemished. If one purchases 10 of these tires, what is the probability th
hodyreva [135]

Answer:

<h2>See the explanation.</h2>

Step-by-step explanation:

3 or less than 3 tires among the 10 tires should be blemished.

There are some possibilities.

1000 tires are blemished, (5000 - 1000) = 4000 tires are not blemished.

From the 5000 tires, 10 tires can be chosen in ^{5000}C_{10} ways.

Possibility 1: <u>3 tires are blemished.</u>

Total 10 tires can be chosen with 3 blemished tires in ^{1000}C_3 \times {4000}C_7 ways.

Possibility 2:  <u>2 tires are blemished.</u>

Total 10 tires can be chosen with 2 blemished tires in ^{1000}C_2 \times ^{4000}C_8  ways.

Possibility 3: <u>1 tires are blemished.</u>

Total 10 tires can be chosen with 1 blemished tires in ^{1000}C_1 \times^{4000}C_9 ways.

Possibility 4:  <u>No tires are blemished.</u>

Total 10 tires can be chosen with no blemished tires in ^{1000}C_0 \times^{4000}C_{10} ways.

Hence, the required probability is \frac{^{1000}C_1 \times^{4000}C_9 + ^{1000}C_0 \times^{4000}C_{10} + ^{1000}C_2 \times^{4000}C_8 + ^{1000}C_3 \times^{4000}C_7}{^{5000}C_{10} }.

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