Answer:
(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
Answer:
x = 0
y = 2
Step-by-step explanation:
The +1 in parenthesis is adding one to the x value, which shifts the graph 1 unit to the left, called a horizontal translation.
The 3 outside of the parenthesis is multiplying the X value by 3, which narrows the shape of the graph ( makes the curve 3 times sharper), which is called a vertical stretch.
