Answer:
Jeff went over by 53.33 grams or 
grams.
Step-by-step explanation:
Jeff is baking a cake. The recipe says that he has to mix 32 grams of vanilla powder to the flour.
Jeff knows that 1 cup of that particular vanilla powder has a mass of 128 grams.
He added 
of a cup of vanilla powder to the flour.
So, he added a mass of 
grams of vanilla.
As the recipe says, it needs 32 grams of vanilla so Jeff went over by 
grams.
Jeff went over by 53.33 grams or grams.
The mistake is made on line 3, after calculating 35/7, he should have started with the multiplication 6(2)
Answer:
32
Step-by-step explanation:
40 = 100%
how many are 80% ?
well, 80% = 80× 1%
1% = 100% / 100
so, in our case
80% = 80×40/100 = 8×4 = 32
as a shortcut you consider a percentage x as a ratio factor of x/100 you multiply the 100% number with.
in our case
80% = 40 × 80/100 = 32
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.
