Answer:
a) No.
b) Yes.
c) Yes.
Step-by-step explanation:
a) No.
As being without replacement, the probabilities of each color in each draw change depending on the previous draws.
This is best modeled by an hypergeometric distribution.
b) Yes.
As being with replacement, the probabilities for each color is constant.
Also, there are only two colors, so the "success", with probability p, can be associated with the color red, and the "failure", with probability (1-p), with the color blue, for example.
(With more than two colors, it should be "red" and "not red", allowing only two possibilities).
c) Yes.
The answer is binary (Yes or No) and the probabilities are constant, so it can be represented as a binomial experiment.
Answer:
0.8 of a cake
Step-by-step explanation:
all you do is divide 4 by 5
Answer:
crxr utcug UFC tvuyvuyv tvuycrcfgvugvihtc
Step-by-step explanation:
TT good uvjobihcygc r
15c-25
15c=25
your answer is c=13.5
Answer:
a)Null hypothesis:
Alternative hypothesis:
b) A Type of error I is reject the hypothesis that
is equal to 40 when is fact
, is different from 40 hours and wish to do a statistical test. We select a random sample of college graduates employed full-time and find that the mean of the sample is 43 hours and that the standard deviation is 4 hours. Based on this information, answer the questions below"
Data given
represent the sample mean
population mean (variable of interest)
s=4 represent the sample standard deviation
n represent the sample size
Part a: System of hypothesis
We need to conduct a hypothesis in order to determine if actual mean is different from 40 , the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
Part b
In th context of this tes, what is a Type I error?
A Type of error I is reject the hypothesis that
is equal to 40 when is fact [tex]\mu is equal to 40
Part c
Suppose that we decide not to reject the null hypothesis. What sort of error might we be making.
We can commit a Type II Error, since by definition "A type II error is the non-rejection of a false null hypothesis and is known as "false negative" conclusion"