# Chi-Square Association Quiz – 2

The relationship/ dependency between the attributes is called association and the measure of degrees of relationship between the 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 following is the MCQs Association Test

The quiz about Chi-Square Association between attributes.

1. The parameter of the Chi-Square distribution is ———–.

2. A characteristic which varies in quality from one individual to another is called

3. The eye colour of 100 men is

4. The value of $\chi^2$ cannot be ———.

5. A $4 \times 5$ contingency table consists of ———.

6. The coefficient of association $Q$ lies between

7. For $r\times c$ contingency table, the Chi-Square test has $df=$ ———-.

8. Two attributes $A$ and $B$ are said to be positively associated if

9. If $\chi^2_c=5.8$ and $df=1$, we make the following decision ———-.

10. There are ———– parameters of Chi-Square distribution.

11. Association measures the strength of the relationship between

12. If $(AB) < \frac{(A)(B)}{n}$ then association between two attributes $A$ and $B$ is

13. The range of $\chi^2$ is

14. The presence of an attribute is denoted by

15. The process of dividing the objects into two mutually exclusive classes is called

16. If for a contingency table $df=12$ and number of rows is 4 then the number of columns will be

17. For $3\times 3$ contingency table the degrees of freedom is

18. A contingency table with $r$ rows and $c$ columns is called

19. If $(AB) > \frac{(A)(B)}{n}$ then association is

20. Two attributes $A$ and $B$ are said to be independent if

A population can be divided into two or more mutually exclusive and exhaustive classes according to their characteristics. It is called dichotomy or twofold division if it is divided into two mutually exclusive classes. A contingency table is a two-way table in which the data is classified according to two attributes, each having two or more levels. A measure of the degree of association between attributes expressed in a contingency table is known as the coefficient of contingency. Pearson’s mean square coefficient of contingency is

$C=\sqrt{\frac{\chi^2}{n+\chi^2}}$

Attributes are said to be independent if there is no association between them. Independence means the presence or absence of one attribute does not affect the other. The association is positive if the observed frequency of attributes is greater than the expected frequency and negative association or disassociation (negative association) is if the observed frequency is less than the expected frequency.

Perform another Non-Parametric Test: MCQs Non-Parametric 