Answer:
see explanation
Step-by-step explanation:
The circumference (C) of a circle is calculated as
C = πd ( d is the diameter )
(4)
Here d = 18, thus
C = 18π in
(5)
Here C = 50π, thus
πd = 50π ( divide both sides by π )
d = 50 in
The radius r is one half of the diameter , thus
r = 50 ÷ 2 = 25 in
Answer:
19.625
Step-by-step explanation:
If the square's diagonal is 5 and the circle is inscribed in it, that means that the radius is 2.5
2.5^2*3.14
=19.625
B. As the x-values increase, the y- valuebtend to decrease.
Answer:
nick's number = 91
eddie's number = 39
Step-by-step explanation:
n = nick's number
n = eddie's number
n -
n = 52
n = 52
cross-multiply: 4n = 52(7)
4n = 364
n = 364/4 = 91
eddie's number:
· 91 = 273/7 = 39
Answer:
In order to test the hypothesis if the correlation coefficient it's significant we have the following hypothesis:
Null hypothesis:
Alternative hypothesis:
The statistic to check the hypothesis is given by:
And is distributed with n-2 degreed of freedom. df=n-2=10-2=8
For this case the null hypothesis represent that we don't have association betwen the dependent variable Y and the independent variable X and that means r=0. So then the best option for this case is:
The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero
Step-by-step explanation:
Previous concepts
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
And in order to calculate the correlation coefficient we can use this formula:
Solution to the problem
In order to test the hypothesis if the correlation coefficient it's significant we have the following hypothesis:
Null hypothesis:
Alternative hypothesis:
The statistic to check the hypothesis is given by:
And is distributed with n-2 degreed of freedom. df=n-2=10-2=8
For this case the null hypothesis represent that we don't have association betwen the dependent variable Y and the independent variable X and that means r=0. So then the best option for this case is:
The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero