
Answer-first summary for fast verification
Answer: Y-hat = -0.8967 + 0.9633 X1
# Why B is correct (quick reasoning) ## Check slope (\( \beta_1 \)) sign: - \( X_1 \) goes from \(-0.41\) to \(0.40\), and \( Y \) goes from \(-2\) to \(-0.11\) - As \( X_1 \) increases, \( Y \) increases → **positive slope** - This immediately eliminates options **C** and **D** (both have negative \( \beta_1 \)) ## Check intercept (\( \alpha \)) sign: - For \( X_1 = -0.41 \), with \( \beta_1 \approx 0.96 \), the slope term = \( 0.96 \times -0.41 \approx -0.39 \) - The actual \( Y \) is \(-2\), which is much lower, so \( \alpha \) must be **negative** to bring the prediction down - This eliminates option **A** ## Only option B fits: - \( \alpha = -0.8967 \) (negative), \( \beta_1 = +0.9633 \) (positive) ✅ --- # Why the others are wrong - **A**: Positive slope (ok) but positive intercept — would overpredict for negative \( X_1 \) - **C/D**: Negative slope — contradicts observed positive relationship
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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
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