Table 2.
Linear regression Number of obs = 368 F(21, 346) = 29.16 Prob > F = 0.0000 R-squared = 0.5959 Root MSE = 115.83 Robust | |||||
---|---|---|---|---|---|
y | Standard Error | t | P > t | (95% Confidence Interval) | |
var1 | 0.0436 | 0.0080 | 5.50 | 0.000 *** | 0.0280–0.0592 |
var2 | 0.0356 | 0.0203 | 1.75 | 0.081 * | −0.0044–0.0755 |
var3 | 0.0042 | 0.0008 | 5.39 | 0.000 *** | 0.0027–0.0058 |
var4 = 1 | 105.3848 | 32.6609 | 3.23 | 0.001 *** | 41.1459–169.6237 |
var5 = | |||||
2 | −105.664 | 18.3378 | −5.76 | 0.000 *** | −141.7317–−69.5964 |
3 | −142.515 | 40.0669 | −3.56 | 0.000 *** | −221.3204–−63.7096 |
var6 = 1 | 44.8862 | 16.5621 | 2.71 | 0.007 *** | 12.3111–77.4613 |
var12 = 1 | 171.0338 | 59.9637 | 2.85 | 0.005 *** | 53.0946–288.9731 |
var9 = 1 | 23.8974 | 32.1457 | 0.74 | 0.458 | −39.3281–87.1230 |
var10 = 1 | 13.3541 | 38.2681 | 0.35 | 0.727 | −61.9132–88.6215 |
var8 * var7 | |||||
0 1 | −94.1936 | ||||
1 0 | 60.0584 | 34.6160 | −2.72 | 0.007 *** | −162.278–−26.1093 |
1 1 | 0 (omitted) | 38.9043 | 1.54 | 0.124 | −16.4603–136.577 |
var11 * var7 | |||||
0 1 | −67.3168 | ||||
1 0 | 159.3906 | 32.0474 | −2.10 | 0.036 * | −130.3491–−4.2845 |
1 1 | 0 (omitted) | 39.2444 | 4.06 | 0.000 *** | 82.2029–236.5783 |
var13 * var7 | |||||
1 1 | 257.8345 | 64.8942 | 3.97 | 0.000 *** | 130.1978–385.4712 |
2 0 | 59.5823 | 59.4744 | 1.00 | 0.317 | −57.3946–176.5593 |
2 1 | 283.7637 | 72.0074 | 3.94 | 0.000 *** | 142.1364–425.391 |
3 0 | −24.1865 | 66.7344 | −0.36 | 0.717 | −155.4427–107.0697 |
3 1 | 300.5646 | 96.5215 | 3.11 | 0.002 *** | 110.7219–490.4072 |
4 0 | −25.5099 | 65.3923 | −0.39 | 0.697 | −154.1263–103.1066 |
4 1 | 270.0733 | 80.1628 | 3.37 | 0.001 *** | 112.4057–427.741 |
_cons | 63.0924 | 77.3488 | 0.82 | 0.415 | −89.0406–215.2255 |
Note: The subscripts *, **, and *** refer to significance levels for two-tailed tests at p < 0.1, p < 0.05, and p < 0.01, respectively. For dummy and categorical variables, we notice the confidence intervals are somewhat wide in the result, which is mainly caused by the small and imbalanced sample size (especially for categorical variables). This can only be improved with a larger sample size or the inclusion of other key variables once additional relevant data and information becomes available.