If you add the two terms on the right, you will get 5^14. 14 is your answer.
Answer:
Step-by-step explanation:
Let 
be the number of bags with 8 onions and let 
be the number of bags with 3 onions. We have the following system of equations:
Subtracting from both sides of the first equation, we get 
. Substitute this into the second equation:
Therefore, the number of 8-onion bags is:
Thus, the chef got 4 8-onion bags and 3 3-onion bags.
Answer:
C. with 3000 successes of 5000 cases sample
Step-by-step explanation:
Given that we need to test if the proportion of success is greater than 0.5.
From the given options, we can see that they all have the same proportion which equals to;
Proportion p = 30/50 = 600/1000 = 0.6
p = 0.6
But we can notice that the number of samples in each case is different.
Test statistic z score can be calculated with the formula below;
z = (p^−po)/√{po(1−po)/n}
Where,
z= Test statistics
n = Sample size
po = Null hypothesized value
p^ = Observed proportion
Since all other variables are the same for all the cases except sample size, from the formula for the test statistics we can see that the higher the value of sample size (n) the higher the test statistics (z) and the highest z gives the strongest evidence for the alternative hypothesis. So the option with the highest sample size gives the strongest evidence for the alternative hypothesis.
Therefore, option C with sample size 5000 and proportion 0.6 has the highest sample size. Hence, option C gives the strongest evidence for the alternative hypothesis
Answer:
12
Step-by-step explanation:
3x + y = 6
Multiply both sides by 2
6x + 2y = 12
Answer:
The alternative hypothesis is 
.
The critical value is 
Step-by-step explanation:
It is claimed that proportion in favor of proportion A is greater than 60%.
This means that at the null hypothesis, we test if the proportion is of at most 60%, that is:
At the alternative hypothesis, we test if the proportion is more than 60%, that is:
What is (are) the critical value?
The critical value is the value of Z with a p-value 1 subtracted by the standard significance level of 0.05, since we are testing if the mean is more than a value, so, looking at the z-table,
