# Important MCQs on Chi-Square Test Quiz – 3

The post is about Online MCQs on Chi-Square Test Quiz with Answers. The Quiz MCQs on Chi-Square Test cover the topics of attributes, Chi-Square Distribution, Coefficient of Association, Contingency Table, and Hypothesis Testing on Association between attributes, etc. Let us start with MCQs on Chi-Square Test Quiz.

The quiz about Chi-Square Association between attributes.

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

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

3. The coefficient of association $Q$ lies between

4. Association measures the strength of the relationship between

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

6. The eye colour of 100 men is

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

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

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

10. The presence of an attribute is denoted by

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

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

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

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

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

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

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

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

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

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

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

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

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

### MCQs on Chi-Square Test Quiz with Answers

• A characteristic which varies in quality from one individual to another is called
• The eye colour of 100 men is
• Association measures the strength of the relationship between
• The presence of an attribute is denoted by
• The process of dividing the objects into two mutually exclusive classes is called
• There are ———– parameters of Chi-Square distribution.
• The parameter of the Chi-Square distribution is ———–.
• The value of $\chi^2$ cannot be ———.
• The range of $\chi^2$ is
• Two attributes $A$ and $B$ are said to be independent if
• Two attributes $A$ and $B$ are said to be positively associated if
• If $(AB) > \frac{(A)(B)}{n}$ then association is
• If $(AB) < \frac{(A)(B)}{n}$ then association between two attributes $A$ and $B$ is
• The coefficient of association $Q$ lies between
• If $\chi^2_c=5.8$ and $df=1$, we make the following decision ———-.
• A contingency table with $r$ rows and $c$ columns is called
• A $4 \times 5$ contingency table consists of ———.
• If for a contingency table, $df=12$ and the number of rows is 4 then the number of columns will be
• For $r\times c$ contingency table, the Chi-Square test has $df=$ ———-.
• For the $3\times 3$ contingency table, the degrees of freedom is

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.

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