According to law of cosines the length of RQ can be written as 
.
Given the length PR is 6 , the length of RQ is p, the length of PQ is 8 and the angle RPQ is 39 degrees.
A length of the triangle can be written as according to law of cosines if sides are given and one angle is 
We have to just put the values in the above equation.
as .
p is the side opposite to angle given , the length of other sides are 6 and 8 and angle is 39 degrees.
Hence the side can be written as according to law of cosines if the angle is 39 degrees is as .
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Answer:
There is no significant difference between the two averages at 5% level
Step-by-step explanation:
Given that a a college student is interested in testing whether business majors or liberal arts majors are better at trivia.
The student gives a trivia quiz to a random sample of 30 business school majors and finds the sample’s average test score is 86. He gives the same quiz to 30 randomly selected liberal arts majors and finds the sample’s average quiz score is 89
Thus he has done a hypothesis testing for comparison of two means of different subjects. n =30
Since which is better is not claimed we use two tailed test here
We find that p value 
our alpha
Since p >alpha, we find that there is no significant difference between the averages of these two groups and null hypothesis is accepted
1.) (-2,3)
2.) a=175/43 b= -70/43
3.)(7,3)
I believe i am right
I haven't one this in a long time
If i am can i get Brainliest
3/2+3/2=3 That is a good equation because all of the characters are bigger than one 3/2 is equal to 1.5 and 3 is greater than 1
Answer:
b. type II
Step-by-step explanation:
given that food inspectors inspect samples of food products to see if they are safe. This can be thought of as a hypothesis test with the following hypotheses.
H0: the food is safe
Ha: the food is not safe
It was concluded from the hypothesis test that the food is safe while it was not actually safe.
This is a case of false acceptance of null hypothesis when it is false.
In hypothesis test, there are two errors. a type I error is the rejection of a true null hypothesis while a type II error is the non-rejection of a false null hypothesis
So this is type II error because we did not reject a false null hypothesis.
