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.
Answer:
a) Var[z] = 1600
D[z] = 40
b) Var[z] = 2304
D[z] = 48
c) Var[z] = 80
D[z] = 8.94
d) Var[z] = 80
D[z] = 8.94
e) Var[z] = 320
D[z] = 17.88
Step-by-step explanation:
In general
V([x+y] = V[x] + V[y] +2Cov[xy]
how in this problem Cov[XY] = 0, then
V[x+y] = V[x] + V[y]
Also we must use this properti of the variance
V[ax+b] = V[x]
and remember that
standard desviation =
a) z = 35-10x
Var[z] = Var[x] = 100*16 = 1600
D[z] = = 40
b) z = 12x -5
Var[z] = Var[x] = 144*16 = 2304
D[z] = = 48
c) z = x + y
Var[z] = Var[x+y] = Var[x] + Var[y] = 16 + 64 = 80
D[z] = = 8.94
d) z = x - y
Var[z] = Var[x-y] = Var[x] + Var[y] = 16 + 64 = 80
D[z] = = 8.94
e) z = -2x + 2y
Var[z] = 4Var[x] + 4Var[y] = 4*16 + 4*64 = 320
D[z] = = 17.88
Answer:
3.1415967382
Step-by-step explanation: