Important Chi-Square Test MCQs with Answers 4

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.

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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