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.

Online Multiple Choice Questions about Chi-square Association

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 $e_{ij}$