Hello from MrBillDoesMath!
Answer:
Yes.
Discussion:
There appears to be a functional relationship based on the data gives. In particular, each value of "x" has a defined (dependent) value of "y"
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MrB
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Answer:
.
Step-by-step explanation:
The given expression is

It can be rewritten as

![[\because \cos^{-1}(-x)=\pi-\cos^{-1}(x), x\in [-1,1]]](https://tex.z-dn.net/?f=%5B%5Cbecause%20%5Ccos%5E%7B-1%7D%28-x%29%3D%5Cpi-%5Ccos%5E%7B-1%7D%28x%29%2C%20x%5Cin%20%5B-1%2C1%5D%5D)
![2\left [\pi-\cos^{-1}\left(\cos \frac{\pi}{4}\right)\right]](https://tex.z-dn.net/?f=2%5Cleft%20%5B%5Cpi-%5Ccos%5E%7B-1%7D%5Cleft%28%5Ccos%20%5Cfrac%7B%5Cpi%7D%7B4%7D%5Cright%29%5Cright%5D)
![2\left [\pi-\frac{\pi}{4}\right]](https://tex.z-dn.net/?f=2%5Cleft%20%5B%5Cpi-%5Cfrac%7B%5Cpi%7D%7B4%7D%5Cright%5D)
![2\left [\frac{4\pi-\pi}{4}\right]](https://tex.z-dn.net/?f=2%5Cleft%20%5B%5Cfrac%7B4%5Cpi-%5Cpi%7D%7B4%7D%5Cright%5D)

Hence, the exact value of given expression is
.
Answer: a. .7
Step-by-step explanation:
Multicollinearity means the two or more explanatory variables in a multiple regression model are highly linearly related to each other.
Multicollinearity is a problem because
- it weaken the statistical significance of an independent variable.
- Larger the standard error of a regression coefficient, the less likely it is that this coefficient will be statistically significant.
Multicollinearity may cause problems if the absolute value of the sample correlation coefficient for two of the independent variables exceeds 0.7.
So, the correct option is a. .7 .
Answer:
use pythogarus therom u'll get the answer
see yaa