Which of the following is an incorrect pairing of a parametric test to its nonparametric equivalent? A) One Way Analysis of Variance: Kruskal-Wallis Test B) Independent t-test: Mann-Whitney rank Sum Test C) One-sample t-test: Spearman s rank correlation coefficient D) One-sample t-test: Wilcoxon signed-rank test Thank you!

Which of the following is an incorrect pairing of a parametric test to its nonparametric equivalent? A) One Way Analysis of Variance: Kruskal-Wallis Test B) Independent t-test: Mann-Whitney rank Sum Test C) One-sample t-test: Spearman s rank correlation coefficient D) One-sample t-test: Wilcoxon signed-rank test Thank you!

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Match the following test statistics with the names of the tests: a, t = v One sample t-test for statistical significance: b, t = v Two-sample t-test with independent samples and equal variances: N2 v Two-sample t-test with independent samples and unequal variances: (- P2) - (-) v Two-sample t-test with dependent samples ct = v F-test for equality of variances: N2 v t-test for significance of the correlation coefficeint: d. t = 2 Covir.r2) such that oa> %3D e, F NN-2 f. t=
Suppose we want to test that the average age of a The following table shows the Excel output for this test. Pstudent is not 27 years old. t-Test: Paired Two Sample for Means Score Dummy Mean 25.89474 Variance 5.43275 Observations Pearson Correlation Hypothesized Mean Difference 19 19 #DIV/O! 27 df 18 t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail -2.06696 0.02671 1.7341 0.05343 2.1009 Select the p-value for this test. 2.1009 O 0.05343 0.02671
The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient ?(Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of ? yet. However, there is a quick way to determine if the sample evidence based on ? is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if ? ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of ? in the table gives us the probability of concluding that ? ≠ 0 when, in fact, ? = 0 and there is no population correlation. We have two choices for ?: ? = 0.05 or ? = 0.01. (a) Look at the data below regarding the variables x = age of a Shetland pony and y = weight of that pony. Is the value of |r| large enough to conclude that weight and age of Shetland ponies are correlated? Use ? = 0.05. (Round your answer for r to four decimal…
Show the: Hypothesis Level of significance t-test for independent samples Decision Rule Decision Conclusion
An experiment was conducted to determine the effect of exercises on elder’s (above 60 years old) memory. Two groups of elders were analyzed. One group was treated with no exercise, and the other group was treated with 20 minutes of exercise daily. Given a non-normally distributed data, identify a suitable non-parametric test. a. Mann-Whitney U test b. Friedman K test c. Wilcoxon signed-rank test d. Kruskal-Wallis test
Use the Excel printout to answer the following questions. t-Test: Two-Sample Assuming Equal Variances E Variable 1 Variable 2 Mean 28.671 28.215 Variance 5.061 2.225 Observations 8 7 Pooled Variance 3.752 Hypothesized Mean Difference df 13 t Stat 0.455 P(T <= t) one-tail 0.328 t Critical one-tail 1.771 P(T <= t) two-tail 0.657 t Critical two-tail 2.160 In testing for a difference between the two population means, would you conclude that the assumption of a common variance is reasonable? O Yes, the ratio of the the larger variance to the smaller variance is less than three. O Yes, the ratio of the larger variance to the smaller variance is more than three. O No, the ratio of the larger variance to the smaller variance is less than three. O No, the ratio of the larger variance to the smaller variance is more than three. O It is not possible to check that assumption with the given information. What is the observed value of the test statistic? t = What are the degrees of freedom for the…
The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient ? (Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of ? yet. However, there is a quick way to determine if the sample evidence based on ? is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if ? ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of ? in the table gives us the probability of concluding that ? ≠ 0 when, in fact, ? = 0 and there is no population correlation. We have two choices for ?: ? = 0.05 or ? = 0.01.
Answer the following questions: the test is to be used? The critical value is The test statistics/computed value is Based on the comparison of the critical value and the computed value, the decision is
Which of the following conclusions is correct for these test results? > bartlett.test(len~river, data=rivers) Bartlett test of homogeneity of variances data: len by river Bartlett's K-squared = 0.61394, df = 4, p-value = 0.5226 %3D Hint* the first line of code should tell you the type of test you are dealing with. There is NO significant difference in variances among the rivers. There is a significant difference in variances among the rivers. There is NO significant difference in means among the rivers.
Consider the following 29 years worth of observed annual rainfall data (in inches) for a certain weather station shown. Treat the data as samples only. (Note: The data are arranged in chronological manner.) 43.30 53.02 63.52 45.93 48.26 50.51 49.57 43.93 46.77 59.12 54.49 47.38 40.78 45.05 50.37 54.91 51.28 40.01 53.29 67.59 58.71 42.96 55.77 41.31 58.83 48.21 44.67 67.72 43.11 (a) Make a time-series line graph showing the trend of observed rainfall data through time. (For year, just denote it as Year 1, Year 2 etc.). Draw the line graph by hand. (b) Make a grouped data representing the given rainfall data. Use the following class intervals: 40<=X<45, 45<=X<50, 50<=X<55, 55<=X<60, 60<=X<65, and 65<=X<70.
The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient ? (Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of ? yet. However, there is a quick way to determine if the sample evidence based on ? is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if ? ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of ? in the table gives us the probability of concluding that ? ≠ 0 when, in fact, ? = 0 and there is no population correlation. We have two choices for ?: ? = 0.05 or ? = 0.01. (a) Look at the data below regarding the variables x = age of a Shetland pony and y = weight of that pony. Is the value of |r| large enough to conclude that weight and age of Shetland ponies are correlated? Use ? = 0.05. (Round your answer for r to four decimal…
Start A sales manager for a large department store believes that customer spending per visit with a sale is lower than customer spending without a sale, and would like to test that claim. A simple random sample of customer spending is taken from without a sale and with a sale. The results are shown below. Without sale with sale 82.904 71.316 Mean Variance 1984.96 1826.41 Observations 150 Hypothesized Mean Difference 328 PT<=t) one-tail 0.0071 Critical one-tail -1.65 PT<=t) two-tail Critical two-tail -1.967 6 Confidence Level 3 Ex: 1.234 Samples from without sale: n₁ = Ex 9 Samples from with sale: n₂ Point estimate for spending without sale: 1 Point estimate for spending with sale: 2 = 0 J. Ex: 1.234 EN P -7 T P= = t= DE UNDE IS-