Mathematics
Mathematics, 05.05.2020 04:27, ggdvj9gggsc

Help on this this is the last one promise!!


Help on this this is the last one promise!!

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Other questions on the subject: Mathematics

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Mathematics, 21.06.2019 21:00, batmanmarie2004
The functions below show the amount of money bella and sweet t had saved after earning money for doing chores. which description best compares the two functions?
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Mathematics, 21.06.2019 22:00, d0ram0UsE
Rick is driving to his uncles house in greenville, which is 120 miles from ricks town .after covering x miles rick she's a sign stating that greensville is 20 miles away. which equation when solved will give the value of x. a: x+120 = 20 b: x x120 =20 c: x +20 equals 120 d: x x20= 120
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Mathematics, 21.06.2019 23:00, davisbrittany5784
Apoll is being conducted at a mall nothingto obtain a sample of the population of an entire country. what is the frame for this type of​ sampling? who would be excluded from the survey and how might this affect the results of the​ survey? what is the frame for this type of​ sampling? a. the frame is people who need new clothes. b. the frame is people who shop at the mall. c. the frame is people who like to shop. d. the frame is the entire population of the country. who would be excluded from the survey and how might this affect the results of the​ survey? a. any person that does not need new clothes is excluded. this could result in sampling bias due to undercoverage. b. any person who does not shop at the mall is excluded. this could result in sampling bias due to undercoverage. c. any person who does not shop at the mall is excluded. this could result in nonresponse bias due to people not participating in the poll. d. there is nobody that is being excluded from the survey.
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Mathematics, 22.06.2019 04:30, glocurlsprinces
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) ).
Answers: 2
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