Non-Parametric Tests

The post is about MCQs nonparametric quiz. There are 22 multiple-choice questions covering different nonparametric tests such as Wilcoxon rank sum test, Spearman’s Rank Correlation test, Mann-Whitney U test, Sign test, Runs Test, Kruskal Wallis test, and Chi-Square goodness of fit test. Let us start with the MCQs nonparametric Quiz.

Online MCQs Nonparametric Quiz with Answers

• The Wilcoxon rank-sum test can be
• The Wilcoxon rank-sum test compares
• The Wilcoxon signed rank is used
• Which of the following test use rank sums?
• Which of the following tests must be two-sided?
• In testing for the difference between two populations, it is possible to use
• In a Wilcoxon rank-sum test
• The Spearman rank-correlation test requires that the
• The sign test is
• The nonparametric equivalent of an unpaired samples t-test is
• The Mann-Whitney U test is preferred to a t-test when
• When using the Sign test, if two scores are tied, then we
• The sign test assumes that the samples are
• When testing for randomness, we can use
• The Runs test results in rejecting the null hypothesis of randomness when:
• To perform a run test for randomness the data must be
• Three brands of coffee are rated for taste on a scale of 1 to 10. Six persons are asked to rate each brand so that there is a total of 18 observations. The appropriate test to determine if three brands taste equally good is
• If a Chi-square goodness of fit test has 6 categories and an N=30, then the correct number of degrees of freedom is
• Comparing the times-to-failure of radar transponders made by firms A, B, and C, based on an airline’s sample experience with the three types of instruments, one may well call for:
• Which of the following tests is most likely assessing the null hypothesis of “the number of violations per apartment in the population of all city apartments is binomially distributed with a probability of success in any one trial of $P=0.4$
• In the Kruskal-Wallis test of $k$ samples, the appropriate number of degrees of freedom is
• Compare to parametric methods, the nonparametric methods are

Online Quiz Website gmstat.com

The post is about the Chi-Square Test MCQS with Answers. The Chi-square test is used to find the association between attributes. Let us start with the Chi-Square Test MCQs with Answers.

Please go to Important Chi-Square Test MCQs with Answers 4 to view the test

The relationship/ Dependency between the attributes is called association and the measure of degrees of relationship between attributes is called the coefficient of association. The Chi-Square Statistic is used to test the association between the attributes. The Chi-Square Association is defined as

$$\chi^2 = \sum \frac{(of_i – ef_i)^2}{ef_i}\sim \chi^2_{v}$$

where $v$$ denotes the degrees of freedom.

The Chi-Square tests:

• are appropriate for categorical data, not continuous data (like height or weight).
• The data needs to be from a random sample and have sufficient sample size for the test to be reliable.
• The test results in a chi-square statistic and a p-value.

Chi-Square Test MCQs with Answers

• A characteristic which cannot be measured numerically is called
• Which of the following is not an example of an attribute
• The eye colour of students in a girl’s college is an example of
• Religions of the people of a country is
• The degree of relationship between two attributes is called
• In a contingency table with $r$ rows and $c$ columns, the degree of freedom is
• The $\chi^2$ distribution is
• If $\chi^2_{calculated}$ is greater than the critical region, then the attributes are
• In a $3 \times 3$ contingency table, the degrees of freedom is
• The Spearman’s coefficient of rank correlation always lies between
• The Yule’s coefficient of association lies between
• If $(AB) < \frac{(A)(B)}{n}$ then the two attributes $A$ and $B$ are said to be
• If $(AB) = \frac{(A)(B)}{n}$ the attributes $A$ and $B$ are said to be
• The coefficient of contingency is measured by
• If $\chi^2_{calculated} = 0$ then
• $(\alpha \beta)$ is the frequency of the class of the order
• If $A$ and $B$ are independent attributes then the coefficient of associate is
• The value of $\chi^2$ is always
• In a Chi-Square test of independence, no expected frequencies should be
• The two attributes are said to be ———–, if for every cell of the contingency table, the observed frequency $O_{ij}$ is equal to the expected frequency $e_{ij}$

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