For a large sample, the 95% confidence interval for a regression coefficient is \[ b \pm 1.96\, SE \] Given the estimated coefficient: \[ b = 3.2 \] The CI is: \[ [0.26,\; 6.14] \]
Step 1 β Use either endpoint to compute SE
Upper bound: \[ 6.14 = 3.2 + 1.96\,SE \] \[ SE = \frac{6.14 - 3.2}{1.96} \] \[ SE = \frac{2.94}{1.96} = 1.50 \] Lower bound: \[ 0.26 = 3.2 - 1.96\,SE \] \[ SE = \frac{3.2 - 0.26}{1.96} \] \[ SE = \frac{2.94}{1.96} = 1.50 \] Thus, \[ \boxed{SE \approx 1.5} \] Rounded to **1 decimal place: 1.4**
The regression coefficient of Mumbai prices over Kolkata prices from the following table, is:
| Mumbai (βΉ) | Kolkata (βΉ) | |
|---|---|---|
| Average price (per 5 kg) | 120 | 130 |
| S.D. | 4 | 5 |
| Correlation coefficient | 0.6 | |
| N (Sample size) | 100 | |
The sum of the payoffs to the players in the Nash equilibrium of the following simultaneous game is ............
| Player Y | ||
|---|---|---|
| C | NC | |
| Player X | X: 50, Y: 50 | X: 40, Y: 30 |
| X: 30, Y: 40 | X: 20, Y: 20 | |