Answer:
mello yello hehe hope this helps
Step-by-step explanation:
Answer: 19.9248588452
Step-by-step explanation:
Answer:
A1)
Stratified sampling: Here, sampling is performed on element within each stratum( the European store and the United States store. Therefore, The technique of randomly selecting 1,000 employees from a list of all employees at the United States stores and 600 employees form a list of all employees at the European stores is a stratified sampling.
A 11)
Cluster sampling :
It involves dividing total population into groups, which should be a representation of the total population and each identified cluster is treated as a sampling unit, by randomly selecting 8 stores from 800, we have chosen 8 clusters, and all samples in the 8 clusters being sampled signals a single stage cluster design.
B) Advantage : stratified sampling usually saves cost as it encourages a small subset of the population while also encougung representativeness.
Disadvantage : It requires that the experimenter has prior knowledge of the population before being able to group into stratums
Step-by-step explanation:
Answer:
237 ⁹/23% change
Step-by-step explanation:
115=100%
337-155=273 which represents the increase
115=100
273=?
273×100÷115= 237 ⁹/23%
Answer =237⁹/23%
Step-by-step explanation:
Note: Question does not indicate if probability required is for weight to exceed or below 3000 lbs. So choose appropriate answer accordingly (near the end)
Using the usual notations and formulas,
mean, mu = 3550
standard deviation, sigma = 870
Observed value, X = 3000
We calculate
Z = (X-mu)/sigma = (3000-3550)/870 = -0.6321839
Probability of weight below 3000 lbs
= P(X<3000) = P(z<Z) = P(z<-0.6321839) = 0.2636334
Answer:
Probability that a car randomly selected is less than 3000
= P(X<3000) = 0.2636 (to 4 decimals)
Probability that a car randomly selected is greater than 3000
= 1 - P(X<3000) = 1 - 0.2636 (to 4 decimals) = 0.7364 (to 4 decimals)