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Estimation Theory MBA 1st Year Semester Short Question Answer Notes:- In this Post, you will know about Estimation Theory MBA 1st Year Semester Short Question Answer Notes. This Post has 3 Mock Papers For Self-Assessment Unit-Wise Division Of The Content Knowledge Boosters To Illuminate The Learning Solved Case Studies For Practice Theory Of Estimation, Point Estimation, Interval Estimation. Hypothesis Testing Null And Alternative Hypothesis; Type I And Type Errors; Testing Of Hypothesis; Large Sample Test, Small Sample Test, (T, F, Z Test, And Chi-Square Test) Notes.

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Estimation Theory MBA 1st Year Semester Short Question Answer Notes: Page.1

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## Estimation Theory MBA 1st Year Semester Short Question Answer Notes | Index

Q.1. Distinguish between sample and population.

Ans. In every branch of science, there is a lack of resources to study more than a fragment of the phenomena that might advance our knowledge. In this, the fragment is termed as a sample, and phenomena are termed as samples and phenomena that might advance our knowledge. In this, the fragment is termed as a sample, and phenomena are termed as the population. Sample aims to obtain information about the population under the course of study.

The sample is a portion of the population that is examined to estimate the characteristics of the population. The selected portion of any consignment is called a sample.

The population represents the entire group of units that is the focus of the study. It consists of all the persons in the country or those in a particular geographical location or economic group depending on the purpose and coverage of the study. It is a set of studies concerning which statistical inferences are to be drawn based on a random sample that is taken from the population.

It is an aggregate of measurable quantities or a set of numbers.

Q.2. Write a short note on estimation theory.

Ans. Estimation Theory: It refers to the technique and method by which population parameters are estimated from sample studies. It is essential when a sample study has been conducted.

When one makes an estimate of a population parameter, a sample statistic is used. This sample statistic is an estimator, i.e. a random variable.

Estimation theory was developed by Prof. R.A. Fisher in 1930 and has been grouped in two classes:

1. Point Estimation: It is a specific value of a sample statistic that is used to estimate a population parameter. This estimation deals with the task of selecting a specific sample value as an estimate for a population parameter.

For a statistical point estimate, the sampling distribution of the estimator provides information about the best estimator. The notations used in point estimation are:

• 0 = Population parameter of interest is estimated.
• 0 = Point estimator of e.

2 Interval Estimation: It establishes an interval consisting of a lower limit and an upper limit in which the true value of the population parameter is expected to fall. This interval is the confidence interval. It is a particular kind of interval estimate of a population parameter. It is used to indicate the reliability of an estimate An interval estimate of a population means can be developed either by the population standard deviation ‘o’ or the sample standard deviation to compute the margin of error.

Q.3. Define null hypothesis, critical region, and two-sided test used in the testing of statistical hypothical hypothesis.

Similar Questions:-
Explain the term ‘Null hypothesis’.

Ans. Null Hypothesis: A statistical hypothesis that is stated for the purpose of acceptance is called the null hypothesis. It is usually denoted by the symbol H,, the null hypothesis expressed symbolically as:

• Hou = 162 cms
• ‘Null hypothesis is the hypothesis which is tested for possible rejection under the assumption it is true.’

The following may be borne in mind in setting the null hypothesis:

1. If we want to test the significance of the difference between a statistic and the parameter or between two sample statistics then we set up a null hypothesis that’s the difference is significant. This means that the difference is just due to the fluctuation of sampling.

Ho:u=X

2. If we want to test any statement about the population, we set up the null hypothesis that it is true. For example, if we want to find the population mean which has a specified valueu0, then we set up the null hypothesis.

• Ho.l=u0

Critical Region: It is a rejection region that is associated with a statistical test in a subset of the sample space such that one rejects the null hypothesis in favor of the alternative if and only if the sample proved wrong by the that is observed falls within this set.

In concurrent programming, a critical region is a piece of code that accesses a shared resource that must not be concurrently accessed by more than one thread of execution. It will terminate in a fixed time and a thread, task or process will have to wait a fixed time to enter it.

Two-Sided Testing: It is a hypothesis test that looks for both sides-either increase or decrease in the parameter. This change in test is used to test the null hypothesis that predicts the direction of the null hypothesis.

Q.4. What are the steps in the test of significance problem? (2005-06)

Ans. As distinguished from variables where quantitative measurement of a phenomenon is possible in the case of attributes, we can only find out the presence or absence of particular characteristic samples from a population whose members possess the attribute. For example, in the study of attribute literacy, a sample may be taken and people are classified as literates and illiterates. Thus, out of 1,000 people selected for the sample, 100 are found literates and 900 illiterates.

Steps involved in the test of significance problem:

1. Set up the null hypothesis Ho.
2. Set up the alternative hypothesis. If His two-tailed, use two-tailed test, and if H, is right-tailed, then use right-tailed test.
3. Choose the appropriate level of significance(a). This is to be decided before the sample is drawn.
4. Choose the test statistic.

Tests for a number of successes, the sampling distribution of the number of successes follows a binomial probability distribution.

• S.E. number of successes = Inpq