x = liters of 20% solution
Mixing the 20% solution with the 60 L of 90% solution gives a new mixture with volume (x + 60) L. Each L of the 20% solution contributes 0.2 L of alcohol and thus a total of 0.2x L of alcohol, while the 60 L of 90% solution contributes 54 L of alcohol.
We want the concentration of the new mixture to be 80%, so we require

Solve for x:



So you will need 30 L of the 20% solution.
the answer is 34.83 I believe
Answer:
It is 152
Step-by-step explanation:
I hope this helped
Answer:
![r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%28%5Csum%20xy%29-%28%5Csum%20x%29%28%5Csum%20y%29%7D%7B%5Csqrt%7B%5Bn%5Csum%20x%5E2%20-%28%5Csum%20x%29%5E2%5D%5Bn%5Csum%20y%5E2%20-%28%5Csum%20y%29%5E2%5D%7D%7D)
The value of r is always between 
And we have another measure related to the correlation coefficient called the R square and this value measures the % of variance explained between the two variables of interest, and for this case we have:

So then the best conclusion for this case would be:
c. the fraction of variation in weights explained by the least-squares regression line of weight on height is 0.64.
Step-by-step explanation:
For this case we know that the correlation between the height and weight of children aged 6 to 9 is found to be about r = 0.8. And we know that we use the height x of a child to predict the weight y of the child
We need to rememeber that the correlation is a measure of dispersion of the data and is given by this formula:
![r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%28%5Csum%20xy%29-%28%5Csum%20x%29%28%5Csum%20y%29%7D%7B%5Csqrt%7B%5Bn%5Csum%20x%5E2%20-%28%5Csum%20x%29%5E2%5D%5Bn%5Csum%20y%5E2%20-%28%5Csum%20y%29%5E2%5D%7D%7D)
The value of r is always between 
And we have another measure related to the correlation coefficient called the R square and this value measures the % of variance explained between the two variables of interest, and for this case we have:

So then the best conclusion for this case would be:
c. the fraction of variation in weights explained by the least-squares regression line of weight on height is 0.64.