There is an opportunity for 4 Extra Credit Points on Problem 2. 1. When constructing confidence intervals and conducting hypothesis tests for the comparison of two population means µi and 42, we...

show workThere is an opportunity for 4 Extra Credit Points on Problem 2.<br>1. When constructing confidence intervals and conducting hypothesis tests for the comparison of two<br>population means µi and 42, we analyze the difference (41 - 42). In doing so, we turn to the sampling<br>distribution of (rī – F2).<br>a) The mean, HT-3), of the sampling distribution of (Fī – 12) is equal to<br>(i) H142<br>(ii) uỉ – u3<br>(iii)<br>(iv) H1 - 42<br>b) Suppose the two populations have standard deviations o1 and o2, and the sample sizes are, respectively,<br>nị and n2 (and that the samples are independent). Then the standard deviation, o(7-7), of the sampling<br>distribution of (T- F2) is equal to<br>(i) ta<br>(ii) of – o<br>(ii)<br>(iv) đ102<br>c) Due to the Central Limit Theorem, for large sample sizes (n 2 30 and ng 2 30), the sampling<br>distribution of (FT – F2) is (approximately)<br>(i) normal<br>(ii) uniform<br>(iii) exponential<br>(iv) log-normal<br>d) Also, for large sample sizes, s? and sž will provide good approximations to of and of. Therefore, for<br>large sample sizes, we can approximate o(- using<br>(i)<br>(i) -品<br>(iii)<br>(iv) $182<br>

Extracted text: There is an opportunity for 4 Extra Credit Points on Problem 2. 1. When constructing confidence intervals and conducting hypothesis tests for the comparison of two population means µi and 42, we analyze the difference (41 - 42). In doing so, we turn to the sampling distribution of (rī – F2). a) The mean, HT-3), of the sampling distribution of (Fī – 12) is equal to (i) H142 (ii) uỉ – u3 (iii) (iv) H1 - 42 b) Suppose the two populations have standard deviations o1 and o2, and the sample sizes are, respectively, nị and n2 (and that the samples are independent). Then the standard deviation, o(7-7), of the sampling distribution of (T- F2) is equal to (i) ta (ii) of – o (ii) (iv) đ102 c) Due to the Central Limit Theorem, for large sample sizes (n 2 30 and ng 2 30), the sampling distribution of (FT – F2) is (approximately) (i) normal (ii) uniform (iii) exponential (iv) log-normal d) Also, for large sample sizes, s? and sž will provide good approximations to of and of. Therefore, for large sample sizes, we can approximate o(- using (i) (i) -品 (iii) (iv) $182

Jun 11, 2022
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