How many permutations can be formed from all the letters in the word engineering?
1 answer:
Answer:
277,200
Step-by-step explanation:
To find the number of permutation we can form from the letters of the word "engineering", we first need to find the frequencies of the different letters present.
E = 3
G = 2
N= 3
I = 2
R = 1
Now that we have the frequencies, we count the number of letters in the word "engineering".
E N G I N E E R I N G
11 letters
Now we take the factorial of total number of letters and divide it by the number of repeats and their factorial
So we get:
We remove the 1! because it will just yield 1.
So the total number of permutations from the letters of the word "engineering" will be:
Total number of permutations =
Total number of permutations = 277,200
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Answer:
So the p value obtained was a very high value and using the significance level assumed we have
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% of significance the proportion of respondents that trust these labels is not significanlty less than 0.5 or 50%.
Step-by-step explanation:
1) Data given and notation
n=1000 represent the random sample taken
X=498 represent the adults that trust these labels
estimated proportion of respondents that trust these labels
is the value that we want to test
represent the significance level
z would represent the statistic (variable of interest)
represent the p value (variable of interest)
2) Concepts and formulas to use
We need to conduct a hypothesis in order to test the claim that the proportion of respondents that trust these labels is at least 50%:
Null hypothesis:
Alternative hypothesis:
When we conduct a proportion test we need to use the z statisitc, and the is given by:
(1)
The One-Sample Proportion Test is used to assess whether a population proportion is significantly different from a hypothesized value
.
3) Calculate the statistic
Since we have all the info requires we can replace in formula (1) like this:
4) Statistical decision
It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.
The significance level is not provided but we can assume it as . The next step would be calculate the p value for this test.
Since is a left tailed test the p value would be:
So the p value obtained was a very high value and using the significance level assumed we have
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% of significance the proportion of respondents that trust these labels is not significanlty less than 0.5 or 50%.