The only thing i can remember is Y hat = a + bx.
I just dont remember how to apply any of this information to the finding of the LSRL line.
In a study of the application of a certain type of weed killer, 14 fields containing large numbers of weeds were treated. The weed killer was prepared at seven different strengths by adding 1, 1.5, 2, 2.5, 3, 3.5, or 4 teaspoons to a gallon of water. Two randomly selected fields were treated with each strength of weed killer. After a few days, the percentage of weeds killed on each filed was measured. The computer output obtained from fitting a least squares regression line to the data is shown below. A plot of the residuals is provided as well.
Dependent variable is: Percent killed
R squared = 97.2% R squared (adjusted) = 96.9%
s=4.505 with 14-2 =12 degrees of freedom
Source Sum of Squares df mean square f-ratio
Regression 8330.16 1 8330.16 410
Residual 243.589 12 20.2990
Variable Coefficient s.e. of Coeff T-ratio Prob.
Constant -20.5893 3.242 -6.35 <_ .0001
No. Teaspoons 24.3929 1.204 20.3 <_ .0001
(SEE residual picture)
1. What is the equation of the least squares regression line given by this analysis? Define any variables used in this equation.
2.) If someone uses this equation to predict the percentages of weeds killed when 2.6 teaspoons of weed killer are used, which of the following would you expect?
- The prediction will be too large
- The prediction will be too small
- A prediction cannot be made based on the information given on the computer output.
Explain your reasoning.
This is study guide for a test coming up. Havent touched this material in months. Honest to god, dont remember any of it. I know you have to find R^2 i beleive. But dont know how to go about this. Care to get me started?