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:
2/5
Step-by-step explanation:
2/5=4/10
Answer: It is only the 3rd equation that is a good example to Jeremy's argument. Others are counter examples to Jeremy's argument.
Step-by-step explanation:
Let us consider the general linear equation
Y = MX + C
On a coordinate plane, a line goes through points (0, negative 1) and (2, 0).
Slope = ( 0 - -1)/( 2- 0) = 1/2
When x = 0, Y = -1
Substitutes both into general linear equation
-1 = 1/2(0) + C
C = -1
The equations for the coordinate is therefore
Y = 1/2X - 1
Let's check the equations one after the other
y = negative one-half x minus 1
Y = -1/2X - 1
y = negative one-half x + 1
Y = -1/2X + 1
y = one-half x minus 1
Y = 1/2X - 1
y = one-half x + 1
Y = 1/2X + 1
It is only the 3rd equation that is a good example to Jeremy's argument. Others are counter examples to Jeremy's argument.
