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Consider the following data sets (We are using a small sample size for illustration purposes. In an exam situation, it might involve large sample sizes)
Y | X1 | X2
|--------|-------|-------|
-2 | -0.41 | -0.01
-0.11| 0.40 | -1.2
-1.68| -0.86 | -0.91
-0.36| 1.69 | 0.37
-0.08| 0.46 | -0.64
-0.74| 1.40 | -1.09
What is the estimated regression equation
Y-hat = alpha + beta_1 X1
A
Y-hat = 0.8967 + 0.9633 X1
B
Y-hat = -0.8967 + 0.9633 X1
C
Y-hat = 0.8967 - 0.9633 X1
D
Y-hat = -0.8967 - 0.9633 X1
Explanation:
goes from to , and goes from to
As increases, increases → positive slope
This immediately eliminates options C and D (both have negative )
For , with , the slope term =
The actual is , which is much lower, so must be negative to bring the prediction down
This eliminates option A
A: Positive slope (ok) but positive intercept — would overpredict for negative
C/D: Negative slope — contradicts observed positive relationship