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Mathematics, 04.05.2021 18:00 alicat20

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Mathematics, 22.06.2019 00:30
Kevin has a spinner that has 10 equal sections and 2 sections of each color—red, blue, green, yellow, and purple. kevin spins the spinner 180 times. kevin determines about how many times the spinner will land on red or green, and his work is shown below. -kevin has the formula reversed; it should be the total number of sections over the number of red or green sections. -kevin should have used a 4 in the numerator because there are 2 red sections and 2 green sections. -kevin should multiply by the number of sections in the spinner rather than the total number of spins. -kevin calculated the prediction correctly and did not make any mistakes.
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Mathematics, 22.06.2019 02:10
Triangle xyz, with vertices x(-2, 0), y(-2, -1), and z(-5, -2), undergoes a transformation to form triangle x′y′z′, with vertices x′(4, -2), y′(4, -3), and z′(1, -4). the type of transformation that triangle xyz undergoes is a . triangle x′y′z′ then undergoes a transformation to form triangle x′y′z′, with vertices x″(4, 2), y″(4, 3), and z″(1, 4). the type of transformation that triangle x′y′z′ undergoes is a .
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Mathematics, 22.06.2019 04:30
The maximum distance (in nautical miles) that a radio transmitter signal can be sent is represented by the expression 1.23h√, where h is the height (in feet) above the transmitter. estimate the maximum distance x (in nautical miles) between the plane that is receiving the signal and the transmitter. round your answer to the nearest tenth.
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Mathematics, 22.06.2019 04:30
Consider the linear model for a two-stage nested design with b nested in a as given below. yijk=\small \mu + \small \taui + \small \betaj(i) + \small \varepsilon(ij)k , for i=1,; j= ; k=1, assumption: \small \varepsilon(ij)k ~ iid n (0, \small \sigma2) ; \small \taui ~ iid n(0, \small \sigmat2 ); \tiny \sum_{j=1}^{b} \small \betaj(i) =0; \small \varepsilon(ij)k and \small \taui are independent. using only the given information, derive the least square estimator of \small \betaj(i) using the appropriate constraints (sum to zero constraints) and derive e(msb(a) ).
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