**Q.12. Define the term cluster sampling.**

**Ans.** The total population is divided into some recognizable sub-divisions which are termed clusters and a simple random sample is drawn from each cluster.

**Q.13. What is meant by non-random sampling?**

**Ans.** Non-random sampling methods are those which do not provide every item in the universe with a knowing chance of being included in the sample.

**Q.14. Write the two merits and demerits of quota sampling. **

**Ans. Merits:** The two merits are:

- A mixture of stratified and purposive sampling.
- Quite reliable results.

**Demerits:** The two demerits are:

- Use for-determined r conclusions.
- Problems of sampling error.

**Q.15. What is the meaning of extensive sampling?**

**Ans.** This sampling is almost like a census and a large number of items from the universe are included in the sample. Only those units are left about which it seems difficult or impossible to collect information.

**Q.16. Define the term F-distribution.**

**Ans.** In order to test the equality of two population variance and equality of several population mean, we use the F-test. Thus, it is necessary to know F-distribution. The F-distribution is named after the English statistician, Sir Ronald Fisher.

**Q.17. Write the three assumptions of the F-test. **

**Ans. **The three assumptions of the F-test are:

- The parent population is normal.
- The samples are selected randomly and thus the observations are independent.
- The variance ratio should be greater than or equal to one. This is the reason that the larger variance is divided by the smaller variance.

**Q.18. What do you mean by the chi-square test?**

**Ans.** The test of significance concerning large samples and small samples is based on the assumption that the sample is drawn from a normally distributed population. Since these tests are based on assumptions about the type of population or parameters, therefore, these tests are known as parametric tests. But there are many situations in which it is not possible to make any assumption about the distribution of the population from which samples are drawn. This has led to the development of non-parametric tests in which no assumption is made regarding the population from which samples are drawn.

**Q.19. Write the four characteristics of the chi-square test. **

**Ans.** The four characteristics of the chi-square test are:

- It is a non-parametric test that is based on observed and expected frequencies rather than parameters like mean and standard deviations.
- Though x? distribution is essentially a cotribuinuous distribution it can also be applied to discrete random variables whose frequencies can be counted and tabulated with or without grouping.
- This test is used for testing the hypothesis and not for estimation.
- The sum of the observed and expected frequencies is always zero Symbolically,

EO- E) = 20 – EE = N-N=0

**Q.20. What is the meaning of degrees of freedom?**

**Ans.** The degrees of freedom refer to the end whserved frequencies. In other words by degrees of freedom, the values can be assigned arbitrarily or without violating in Fefer to the number of independent constraints in a set of data.

Estimation Theory MBA 1st Year Semester Very Short Question Answer Notes