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Please provide explanations to answers below. Thank you! 61. The Mann-Whitney te

ID: 3041812 • Letter: P

Question

Please provide explanations to answers below. Thank you!

61. The Mann-Whitney test is used to determine if a single sample is equal to a target value

True or False

62. A one sample t test has been undertaken. The number of plus and minus signs are equal. There is likely no difference between the sample result and target.

True or false

63. Which of the following is not a nonparametric hypothesis test?

a.) Mann - Whitney Test

b.) Two sample t test

c.) Wilxocon Test

d.) Kruskal-Wallis Test

64. In a One sample sign test, the sample outcome is ranked.

True or False

65. A marketing manager would liike to compare three possible national advertising campaigns to determine if there is a difference between them. They have convened group of 30 people and will assign 10 at random to each of the three possible strategies. AFter viewing the marketing campaign they will be asked how likely they would be to purchase the product. Likelihood would be rated on a scale of 1 to 10. Which of the following tests would be used to determine if there is a difference between their responses?

a.) Sign test

b.) Whitney - Mann

c.) One sample t

d.) Kruskal - Wallace

Explanation / Answer

61. In statistics, the Mann–Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test) is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample.

Unlike the t-test it does not require the assumption of normal distributions. It is nearly as efficient as the t-test on normal distributions.

This test can be used to determine whether two independent samples were selected from populations having the same distribution; a similar nonparametric test used on dependentsamples is the Wilcoxon signed-rank test.

Hence for this case answer is False

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