UGC NET Management Previous Year Solved Papers MCQ - July 2018

rame Work for Process Design The design of transformation process required answers to several questions given below:
1. What are the characteristics of the product or service being supplied or offered to customers?
2. What is the expected volume of output?
3. What kinds of equipment or machinery is available?
4. Must the equipment or machinery be custom-built?
5. What is the cost of equipment and machinery needed?
6. What types of labor skills are available, in what quantities and at what wage rates?
7. How much money can be spent on the manufacturing process?
8. Should the process be capital intensive or labor-intensive?
9. Should the components or parts be made or purchased?
10. How best to handle materials?
Systems Approach Process Planning and Design
A. Inputs (a) Product/Service Information: It includes product/service demand, prices/volumes, competitive environment, customer needs/wants and desired product characteristics.
(b) Production System Information: It includes resource availability, production economics, available technologies and technologies that can be acquired and predominant strengths and weaknesses of the firm.
(c) Operation Strategy: Includes positioning strategy, competitive strategy, the focus of factory/organization and service facilities and allocation of resources.
B. Conversion Process
(a) Selection of the type of process coordinated with open actions strategies.
(b) Vertical integration studies consist of the study of vendor capabilities, acquisitions decision, make or by decisions.
(c) Process/Product Studies: It consists of major/minor technological steps, product simplification, standardization and product design for producibility.
(d) Equipment Studies: Regarding the level of automation, linkage of machines, equipment selection and tooling.
(e) Production Procedure Studies: Includes production sequence, material specifications and personnel requirements.
(f) Facilities Studies: Regarding building designs and layout of facilities etc.
C. Output
(a) Technological Processes: Design of specific processes and linkage among processes.
(b) Facilities: Building design, the layout of facilities and selection of equipment.
(c) Personnel Estimates: Skill level requirements, number of employees, training and retaining requirements, and supervisions requirements.

Two types of errors in Hypothesis Testing It is necessary to know that when a hypothesis is tested, there are four possibilities:
1. The hypothesis is true but our test leads to its rejection.
2. The hypothesis is false but our test leads to its acceptance.
3. The hypothesis is true and our test leads to its acceptance.
4. The hypothesis is false and our test leads to its rejection. Of these four possibilities, the first two lead to an erroneous decisions. The first possibility leads to a Type I error and the second possibility leads to a Type II error. This can be shown as follows:
Table: Types of Errors in Hypothesis Testing

State of nature
Decision	H0 is true (S1)	H0 is false (S2)
Accept H0(A1)	Correct decision	Type II error (β)
Reject H0(A2)	Type I error (α)	Correct decision

Table indicates that one of the two conditions (states of nature) exists in the population, i.e., either the null hypothesis is true or it is false. Similarly, there are two decision alternatives: Accept the null hypothesis or reject the null hypothesis. Thus, two decisions and two states of nature result into four possibilities.
If any hypothesis testing the researcher runs the risk of committing Type I and Type II errors. In case we are interested in reducingthe risk of committing a Type I error, then we should reduce the size of the rejection region or level of significance, indicated in Table.

Two types of errors in Hypothesis Testing
It is necessary to know that when a hypothesis is tested, there are four possibilities:
1. The hypothesis is true but our test leads to its rejection.
2. The hypothesis is false but our test leads to its acceptance.
3. The hypothesis is true and our test leads to its acceptance.
4. The hypothesis is false and our test leads to its rejection.
Of these four possibilities, the first two lead to erroneous decisions. The first possibility leads to a Type I error and the second possibility leads to a Type II error. This can be shown as follows:
Table: Types of Errors in Hypothesis Testing

State of nature
Decision	H0 is true (S1)	H0 is false (S2)
Accept H0(A1)	Correct decision	Type II error (β)
Reject H0(A2)	Type I error (α)	Correct decision

Table indicates that one of the two conditions (states of nature) exists in the population, i.e., either the null hypothesis is true or it is false. Similarly, there are two decision alternatives: Accept the null hypothesis or reject the null hypothesis. Thus, two decisions and two states of nature result into four possibilities.
If any hypothesis testing the researcher runs the risk of committing Type I and Type II errors. In case we are interested in reducing the risk of committing a Type I error, then we should reduce the size of the rejection region or level of significance, indicated in Table.

Probability sampling refers to the sampling method in which all the members of the population has a pre-specified and an equal chance to be a part of the sample. This technique is based on the randomization principle, wherein the procedure is so designed, which guarantees that each and every individual of the population has an equal selection opportunity. This helps to reduce the possibility of bias.
Statistical inferences can be made by the researchers using this technique, i.e. the result obtained can be generalised from the surveyed sample to the target population. The methods of probability Sampling, are provided below:
> Simple Random Sampling
> Systematic Sampling
> Stratified Sampling
> Cluster Sampling
Types of Sampling
Probability Sampling
> Random Sampling: Each person in the universe has an equal probability of being chosen for the sample and every collection of persons of the same size has an equal probability of becoming the actual sample. Simple Random Sampling = Sampling without replacement
>Systematic Sampling: A sample constructed by selecting every kth element in the sampling frame. Systematic sampling is more practical in that it is less work and thus provides more information per dollar. It also may reduce error. (The more complex the method, the greater opportunity for error.)
Stratified Random Sampling: Is obtained by separating the population elements into overlapping groups, called strata, and then selecting a simple random sample from within each stratum. Example: Rank order: full professor, associate professor, etc. After this is done a random or systematic sample is drawn within each group.
>Cluster Sampling: A simple random sample-in which each sampling unit is a collection or cluster, or elements. For example, an investigator wishing to study students might first sample groups or clusters of students such as classes or dormitories, and then select the final sample of students from among clusters. Also called area sampling. Advantage: saves time and money. Disadvantage: error.
Non-Probability Sampling: when in a sampling method, all the individuals of the universe are not given an equal opportunity of becoming a part of the sample, the method is said to be Non-probability sampling. Under this technique as such, there is no probabilityattached to the unit of the population and the selection relies on the subjective judgement of the researcher. Therefore, the conclusions drawn by the sampler cannot be inferred from the sample to the whole population. The methods of non-probability sampling are listed below:
> Convenience Sampling
>Quota Sampling
>Dimensional Sampling
>Judgement or Purposive Sampling
>Snowball Sampling