Independent variable is the predictor variable which is the height and dependent variable is the response variable which is weight in this scenario.
The square of correlation coefficient gives the coefficient of determination. It is denoted by R² (R squared).
We are given:
R = 0.75
So,
R² = 0.75²
R² = 0.5625
R² = 56.25 %
The coefficient of determination tells how much of the trend of dependent data can be explained by the independent data using the linear regression model. So in the given case, Height can explain 56.25% of the trend in the weight.
<span>Let r(x,y) = (x, y, 9 - x^2 - y^2)
So, dr/dx x dr/dy = (2x, 2y, 1)
So, integral(S) F * dS
= integral(x in [0,1], y in [0,1]) (xy, y(9 - x^2 - y^2), x(9 - x^2 - y^2)) * (2x, 2y, 1) dy dx
= integral(x in [0,1], y in [0,1]) (2x^2y + 18y^2 - 2x^2y^2 - 2y^4 + 9x - x^3 - xy^2) dy dx
= integral(x in [0,1]) (x^2 + 6 - 2x^2/3 - 2/5 + 9x - x^3 - x/3) dx
= integral(x in [0,1]) (28/5 + x^2/3 + 26x/3 - x^3) dx
= 28/5 + 40/9 - 1/4
= 1763/180 </span>
$11.40 because you just divide 35.34 by 3.1
C. 2g-19.5=40 I think i'm right
Answer: I’m not sure if this is it but 16.65 maybe
Step-by-step explanation:
it’s hard to figure out
