Answer:
- In a cluster sample, every sample of size n has an equal chance of being included.
- In a stratified sample, random samples from each strata are included.
- In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included.
- In a stratified sample, every sample of size n has an equal chance of being included
Step-by-step explanation:
In a stratified sample the population is divided into different segments and then we take random elements from each segment.
In a cluster sample, the sample is divided into segments (or clusters) and then the sample is taken by selecting different clusters.
Therefore, in the cluster sample we take ALL elements from different clusters while in a stratified sample we take SOME elements from the different sections.
Now let's take a look at the options given:
- In a cluster sample, the only samples possible are those including every kth item from the random starting position: FALSE. In the cluster sample we select all items from the cluster.
- In a cluster sample, every sample of size n has an equal chance of being included: TRUE. if we divide the sample into clusters of size n then every cluster has an equal chance of being selected (since we select them at random).
- In a stratified sample, random samples from each strata are included: TRUE. We already said that we take a random sample from each segment (strata).
- In a stratified sample, the only samples possible are those including every kth item from the random starting position: FALSE. We can apply different methods to select our sample from each strata.
- In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included. TRUE. This is the definition of cluster sample we wrote at first.
- In a stratified sample, every sample of size n has an equal chance of being included: TRUE, we take samples from elements, not from stratas.
- In a cluster sample, random samples from each strata are included: FALSE. This is the definition of stratified sample.
- In a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included: FALSE. This is the definition of cluster sample.
Answer:
a. (3x² - 2x - 5) units²
b. (x² - x) units²
c. (2x² - x - 5) units²
Step-by-step explanation:
a. Area of the frame = L*W
L = (x + 1) units
W = (3x - 5) units
Area of the frame = (x + 1)(3x - 5)
Apply distributive property
x(3x - 5) +1(3x - 5)
3x² - 5x + 3x - 5
Area of the frame = (3x² - 2x - 5) units²
b. Area of the picture = L*W
L = x units
W = (x - 1) units
Area of the picture = x(x - 1)
Apply distributive property
= x² - x
Area of picture = (x² - x) units²
c. Area of the frame that surrounds the picture = area of frame - area of picture
= (3x² - 2x - 5) - (x² - x)
= 3x² - 2x - 5 - x² + x (distributive property)
Add like terms
= 3x² - x² - 2x + x - 5
= 2x² - x - 5
Area of the frame that surrounds the picture = (2x² - x - 5) units²
Answer:
1/3
Step-by-step explanation:
1/3 because 8/12 + 1/3 is 1 whole
Step-by-step explanation:
<u>The two cameras will have total view of:</u>
<u>The angle to be viewed is:</u>
This is not sufficient to cover the needed area as 220° < 255°
<u>The cameras should have a minimum view of:</u>
Answer:
Her claims are wrong as per my studied
Step-by-step explanation:
She says that 60% of the students are more enriched.
total number of students = 64
if 34 people feel more enriched, the ratio of the students that feel enriched to the total number of students then is ? \frac{34}{64} = 0.53125.
converting .53125 to percentage we'd multiply by 100%.
and hence have .53125*100% =53.125%
since 53.125% is less that the 60% claimed by the instructor, then i infer that her claims are wrong
