Systematic Random Sampling
Stratified Sampling
Cluster Sampling
Point-Estimate
Central Limit Theorem
100
What is the main idea of systematic random sampling?
you are sampling every m^th member of the population
100
What are the non-overlapping sub-populations called that the population is first divided up into?
Strata
100
How is cluster sampling similar to stratified sampling?
You break the population up into non-overlapping groups.
100
To estimate the value of a population parameter, we compute a corresponding characteristic of the sample, referred to as a _______
Sample statistic
100
What are we assuming large means?
n > or = 30
200
What do you divide the population size (N) by in order to get your m for this sampling technique?
Sample size (n)
200
What is the process when the proportion of the population that falls under each stratum will be the same as the proportion of sample members taken from the stratum?
proportional allocation
200
What are the groups called?
Clusters
200
How do you compute a sample proportion?
x/n
200
When computing the standard error what does the Central Limit Theorem require?
The sample to be large enough
300
If a sample size is of 50 is desired from a population containing 5000 elements, we will sample one element from every _____ elements in the population
5000/50 = 100
Every 100 elements
300
If you had a population made up of 3 categories, one making up 25%, one making up 35%, and one making up 40% of the population, and you wanted a sample of 500. How many of each category would you need?
25% = .25 * 500 = 125
35% = .35 * 500 = 175
40% = .40 * 500 = 200
300
When is cluster sampling useful?
When the members of a population are widely scattered geographically.
This saves time and money.
300
We refer to the sample mean as the _____
Point estimator
x bar
300
What is the Central Limit Theorem helpful in identifying?
The shape of the sampling distribution of x bar
400
What does k indicate in this sampling method?
The first item randomly selected in our first block
400
What is the advantage of this sampling method?
Your sample is representative of the population.
400
The main idea behind cluster sampling is ______
you will divide your population up into groups (clusters) and randomly choose some of these clusters to obtain the desired sample size.
400
The numerical value obtained for x bar , s, or p bar is called the ____
point estimate
400
When does the approximation to the normal distribution improve?
as n increases
500
How do you obtain your k that is between one and m?
Hint: there are 2 ways
use a random number table or some other random number generator
500
When does stratified sampling work best?
When the variance among elements in each stratum are relatively small.
500
What are the 3 steps
1. Divide the population into clusters of approx the same size
2. Obtain a simple random sample of the clusters
3. Use all the members of the clusters obtained in step 2 as the sample
500
What is the standard deviation for a sample size called and how is it denoted
standard error
500
Describe in your own words, the Central Limit Theorem
In selecting random samples of size n from a population, the sampling distribution of the sample mean (x bar) can be approximated by a normal distribution as the sample becomes large