✧･ﾟ: *✧･ﾟ:* *:･ﾟ✧*:･ﾟ✧
Hello!
✧･ﾟ: *✧･ﾟ:* *:･ﾟ✧*:･ﾟ✧
❖ To find the mean, add up all the numbers and divide by the amount of numbers there are. To find the range, subtract the highest number by the lowest number in the data set.
2. Mean = 35 Range = 78
3. Mean = 43 Range = 58
4. Mean = 63 Range = 73
5. Mean = 61 Range = 56
6. Mean = 55 Range = 83
7. Mean = 61 Range = 53
8. Mean = 60 Range = 75
9. Mean = 50 Range = 76
10. Mean = 50 Range = 81
~ ʜᴏᴘᴇ ᴛʜɪꜱ ʜᴇʟᴘꜱ! :) ♡
~ ᴄʟᴏᴜᴛᴀɴꜱᴡᴇʀꜱ
Answer:
a. it has no solution
b. you can only substitute by making either the 'x' or 'y' the subject of either of the two equations.
Step-by-step explanation:
3x - 9y = -13.............. eqn 1
x - 3y = -7................... eqn 2
from eqn 2
x - 3y = -7, substitute by making 'x' the subject
x = -7 + 3y.................. eqn 3
put eqn 3 into eqn 1
3x - 9y = -13
3( -7 + 3y) - 9y = -13
-21 + 9y - 9y = -13
9y - 9y = -13 + 21
0 = 8, which is not possible.
Answer:
252
Step-by-step explanation:
Thanks for the question!
f(x) = 6 (1 - x)
f(-8) = 6 (1 - (-8))
f(-8) = 6 (9)
f(-8) = 54
Hope this helps!
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.
