For the same set of observations on a specified dependent variable, two different independent variables were used to develop two separate simple linear regression models. A portion of the results is presented below. Based on the results given above, we can conclude that:.
A. A prediction based on Model 1 is better than a prediction based on Model 2.
B. A prediction based on Model 2 is better than a prediction based on Model 1.
C. There is no difference in the predictive ability between Model 1 and Model 2.
D. There is not sufficient information to determine which of two models is superior for prediction purposes.

A. A prediction based on Model 1 is better than a prediction based on Model 2.

Step-by-step explanation:

Given :

Model 1 :

R² = 0.92

s = 1.65

Model 2 :

R² = 0.85

s = 1.91

The Coefficient of determination of the first model is 0.92 which is greater than the coefficient of determination of the Second model, the coefficient of determination gives the proportion of variation in the dependent variable which is caused by the regression line. Hence, we can say a prediction based on Model 1 is better than a prediction based on Model 2 because a larger proportion of the variation in the dependent variable is predictable from the independent variable.

step-by-step explanation:

step 1: solve one of the equations for one of the variables. let's solve the first equation for y:

step 2: substitute that equation into the other equation, and solve for x.

step 3: substitute x = 4 x = 4 x=4 into one of the original equations, and solve for y.

6(h+1)/2=a

a=area

step-by-step explanation:

its the same thing as a square or rectangle but after multiplying two sides you need to divide it by two.