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inna [77]
2 years ago
10

The radius of a circle is 5 feet. What is the circumference?

Mathematics
1 answer:
givi [52]2 years ago
7 0
The answer is 31.42ft
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Ricardo purchased a new car with special 1.9% financing. If the cars price was 17,999 and he financed it for years, find his mon
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the answer is actually $389.71

Step-by-step explanation:

4 0
3 years ago
What 2 numbers multiplied comes to 149
wel

Answer:

12.20655562 or 12.2

Step-by-step explanation:

Just square  149 \sqrt{149\\}

That gives you 12.20655562

Round it to 12.2

4 0
3 years ago
Please help im 25% blind
FrozenT [24]

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c

Step-by-step explanation: all you have to do is work it out

5 0
2 years ago
triangle has sides measuring 2 inches and 7 inches If x represents the length in inches of the third side which inequality gives
il63 [147K]
We know that
The sum of the lengths of any two sides of a triangle is greater than the length of the third side (<span>Triangle Inequality Theorem)
so
inequality 1
</span>(2+7) \ \textgreater \  x \\  9 \ \textgreater \  x \\  x \ \textless \  9 in
<span>
inequality 2
</span>(x+2) \ \textgreater \  7 \\  x \ \textgreater \  7-2 \\  x \ \textgreater \  5 in
<span>
the answer is
</span>5 in  \ \textless \ x \ \textless \ 9 in<span>
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6 0
2 years ago
Consider the following statements:
Artyom0805 [142]

Answer:

d. Both I and II are false

Step-by-step explanation:

When there is a high degree of linear correlation between the predictors the errors are found.

The basic objective of the regression model is to separate the dependent and independent variables. So if the variables have high degree of linear correlation then the multi collinearity causes problems or has errors. It is not necessary that multi collinearity must be present with high degree of linear correlation.

For example we have 3 variable of heat length and time. And all of them have a high degree of correlation. With increase in heat and time the length increases . But for multi collinearity with the increase of time and decrease of heat length does not increase. So this causes errors.

y-hat = 135 + 6x + errors

The linear relationship between height and weight is inexact. The deterministic relation in such cases is then modified to allow the inexact relationship between variables and a non deterministic or probabilistic model is obtained which has  error which are unknown random errors.

y- hat= a + bXi + ei   (i=1,2,3...)

ei are the unknown random errors.

<u><em>So both statements are false.</em></u>

<em><u /></em>

7 0
2 years ago
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