Classify each sample as random, systematic, stratified, or cluster 1. In a large school district, all teachers from two buildings are interviewed to determine whether they believe the students have less homework to do now than in previous years.
2. Every seventh customer entering a shopping mall is asked to select his or her favorite store.
3. Nursing supervisors are selected using random numbers in order to determine annual salaries.
4. Every hundredth hamburger manufactured is checked to determine its fat content.
5. Mail carriers of a large city are divided into four groups according to gender (male or female) and according to whether they walk or ride on their routes. Then 10 are selected from each group and interviewed to determine whether they have been bitten by a dog in the last year.

Using sampling concepts, it is found that the classifications are:

1. Cluster

2. Systematic

3. Random

4. Systematic

5. Stratified

Samples are classified as:

Item 1:

Teachers divided into groups according to their school, and all teachers are interview, hence Cluster sampling is used.

Item 2:

Every 7th customer, that is, every kth customer, hence Systematic sampling is used.

Item 3:

Random numbers, hence, Random sampling is used.

Item 4:

Every 100th customer, that is, every kth customer, hence Systematic sampling is used.

Item 5:

Four groups, then 10 for each group are selected, that is, a few members, hence Stratified sampling it used.

A similar problem is given at link

(1) Cluster sample

(2) Systematic sample

(3) Random sample

(4) Systematic sample

(5) Stratified sample

Step-by-step explanation:

A simple random sample is a part of a statistical population in which every individual of the population has an equal probability of being selected.

Assigning each individual of the population a unique number and using a computer or random number generator for selection is a procedure to select a simple random sample.

Stratified sampling is a kind of sampling in which whole-population is distributed into homogeneous subgroups before one takes a sample. These subgroups are called strata which is mutually exclusive or related.

In this process the population members cannot be excluded.

Cluster Sampling is a method to randomly select samples from a population that is too enormous for simple random sampling.  

Using cluster sampling, the experimenter distributes the entire-population into distinct groups, called clusters. Then, a simple random sample of clusters is chosen from the population. Then the experimenter performs the analysis on data from the sampled clusters.

Systematic sampling is a kind of probability sampling method in which individuals from a larger population are nominated according to a random initial point and a static, periodic interval.

Consider all the definitions of different types of samples.

(1) Cluster sample

(2) Systematic sample

(3) Random sample

(4) Systematic sample

(5) Stratified sample

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