1) For a value of population parameter 0 equal to 2, which one of the following cases corresponds to an unbiased estimator with higher precision? E(Ô) = 2 and Var(ô) = 2 E(ô – 0) = 0 and Var(ô) = 2 E(ô – 0) = 2 and Var(Ô) = 1 E(Ô) = 2 and Var(ô ) = 1 2) In the ANOVA for a CRD, which one of the following statements is not correct? The ANOVA F-test for treatment main effects is a right-hand one-tailed test. The linear model is Xij = µ + ai + Eij where i = 1, ..., n and j = 1, ., p. The experimental error ɛij is assumed to follow a normal distribution with a zero mean and a variance o. In the case of more than two treatments, the LSD procedure of multiple pairwise comparisons of treatment means should be applied only after the hypothesis of equality of all the treatment population means has been tested and rejected by the ANOVA F-test for treatment main effects.

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1) For a value of population parameter 0 equal to 2, which one of the following cases corresponds
to an unbiased estimator with higher precision?
O E(Ô) = 2 and Var( ) = 2
E(Ô – 0) = 0 and Var( ) = 2
E(ô – 0) = 2 and Var( Ô) = 1
E(Ô) = 2 and Var(ô ) = 1
2)
In the ANOVA for a CRD, which one of the following statements is not correct?
The ANOVA F-test for treatment main effects is a right-hand one-tailed test.
The linear model is Xij = µ + ai + Eij where i = 1, .., n and j = 1, ..., p.
The experimental error ɛij is assumed to follow a normal distribution with a zero mean and a
variance o.
In the case of more than two treatments, the LSD procedure of multiple pairwise comparisons
of treatment means should be applied only after the hypothesis of equality of all the treatment
population means has been tested and rejected by the ANOVA F-test for treatment main
effects.
3) Ho: u = 4 is tested against H1: µ 4 at a = 0.01, with a sample mean X = 2 and a sample variance s?
= 9 obtained from an i.i.d. random sample of size n = 11. On the side of this test, a 99% confidence
interval for u can be built as:
O (L,U]=2±tg9 (10) /9/11
O (L,U]=2±tqse5 (10) /9/11
[L,U]=4±to99s(10)/9/11
0.995
[L,U]=4±t,9»(10) /9/11
0.99
4)
Among the statements below, which one is not correct in the statistical inference for one population
mean?
Increasing the significance level a increases the power of the statistical test.
A Type I error is made when the null hypothesis is rejected while it is true.
The rejection of the null hypothesis means that the null hypothesis is false.
The risk of making a Type II error decreases when the sample size increases.
Transcribed Image Text:1) For a value of population parameter 0 equal to 2, which one of the following cases corresponds to an unbiased estimator with higher precision? O E(Ô) = 2 and Var( ) = 2 E(Ô – 0) = 0 and Var( ) = 2 E(ô – 0) = 2 and Var( Ô) = 1 E(Ô) = 2 and Var(ô ) = 1 2) In the ANOVA for a CRD, which one of the following statements is not correct? The ANOVA F-test for treatment main effects is a right-hand one-tailed test. The linear model is Xij = µ + ai + Eij where i = 1, .., n and j = 1, ..., p. The experimental error ɛij is assumed to follow a normal distribution with a zero mean and a variance o. In the case of more than two treatments, the LSD procedure of multiple pairwise comparisons of treatment means should be applied only after the hypothesis of equality of all the treatment population means has been tested and rejected by the ANOVA F-test for treatment main effects. 3) Ho: u = 4 is tested against H1: µ 4 at a = 0.01, with a sample mean X = 2 and a sample variance s? = 9 obtained from an i.i.d. random sample of size n = 11. On the side of this test, a 99% confidence interval for u can be built as: O (L,U]=2±tg9 (10) /9/11 O (L,U]=2±tqse5 (10) /9/11 [L,U]=4±to99s(10)/9/11 0.995 [L,U]=4±t,9»(10) /9/11 0.99 4) Among the statements below, which one is not correct in the statistical inference for one population mean? Increasing the significance level a increases the power of the statistical test. A Type I error is made when the null hypothesis is rejected while it is true. The rejection of the null hypothesis means that the null hypothesis is false. The risk of making a Type II error decreases when the sample size increases.
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