Multiple Linear Regression - Estimated Regression Equation |
cons[t] = + 0.197315 + 0.00330776income[t] + 0.00345843temp[t] -1.04441price[t] + e[t] |
Multiple Linear Regression - Ordinary Least Squares | |||||
Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
(Intercept) | +0.1973 | 0.2702 | +7.3020e-01 | 0.4718 | 0.2359 |
income | +0.003308 | 0.001171 | +2.8240e+00 | 0.008989 | 0.004494 |
temp | +0.003458 | 0.0004456 | +7.7620e+00 | 3.1e-08 | 1.55e-08 |
price | -1.044 | 0.8344 | -1.2520e+00 | 0.2218 | 0.1109 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.8479 |
R-squared | 0.719 |
Adjusted R-squared | 0.6866 |
F-TEST (value) | 22.17 |
F-TEST (DF numerator) | 3 |
F-TEST (DF denominator) | 26 |
p-value | 2.45e-07 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.03683 |
Sum Squared Residuals | 0.03527 |
Menu of Residual Diagnostics | |
Description | Link |
Histogram | Compute |
Central Tendency | Compute |
QQ Plot | Compute |
Kernel Density Plot | Compute |
Skewness/Kurtosis Test | Compute |
Skewness-Kurtosis Plot | Compute |
Harrell-Davis Plot | Compute |
Bootstrap Plot -- Central Tendency | Compute |
Blocked Bootstrap Plot -- Central Tendency | Compute |
(Partial) Autocorrelation Plot | Compute |
Spectral Analysis | Compute |
Tukey lambda PPCC Plot | Compute |
Box-Cox Normality Plot | Compute |
Summary Statistics | Compute |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 0.386 | 0.3151 | 0.07088 |
2 | 0.374 | 0.3578 | 0.01622 |
3 | 0.393 | 0.3938 | -0.0008221 |
4 | 0.425 | 0.4047 | 0.02033 |
5 | 0.406 | 0.4033 | 0.002744 |
6 | 0.344 | 0.4065 | -0.06248 |
7 | 0.327 | 0.3923 | -0.0653 |
8 | 0.288 | 0.3423 | -0.05432 |
9 | 0.269 | 0.2826 | -0.0136 |
10 | 0.256 | 0.2523 | 0.003672 |
11 | 0.286 | 0.2709 | 0.01514 |
12 | 0.298 | 0.2864 | 0.0116 |
13 | 0.329 | 0.3084 | 0.02063 |
14 | 0.318 | 0.3105 | 0.00755 |
15 | 0.381 | 0.3761 | 0.004922 |
16 | 0.381 | 0.3867 | -0.005686 |
17 | 0.47 | 0.4185 | 0.05149 |
18 | 0.443 | 0.415 | 0.02798 |
19 | 0.386 | 0.4176 | -0.03158 |
20 | 0.342 | 0.4 | -0.05799 |
21 | 0.319 | 0.3257 | -0.006677 |
22 | 0.307 | 0.3237 | -0.01668 |
23 | 0.284 | 0.3296 | -0.04561 |
24 | 0.326 | 0.2973 | 0.02865 |
25 | 0.309 | 0.3139 | -0.004864 |
26 | 0.359 | 0.3522 | 0.006781 |
27 | 0.376 | 0.3733 | 0.00273 |
28 | 0.416 | 0.4179 | -0.001929 |
29 | 0.437 | 0.4398 | -0.002758 |
30 | 0.548 | 0.469 | 0.07899 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
7 | 0.6175 | 0.7651 | 0.3825 |
8 | 0.7796 | 0.4408 | 0.2204 |
9 | 0.8127 | 0.3745 | 0.1873 |
10 | 0.773 | 0.454 | 0.227 |
11 | 0.6615 | 0.6771 | 0.3385 |
12 | 0.7229 | 0.5543 | 0.2771 |
13 | 0.6636 | 0.6727 | 0.3364 |
14 | 0.5698 | 0.8603 | 0.4302 |
15 | 0.4578 | 0.9156 | 0.5422 |
16 | 0.347 | 0.694 | 0.653 |
17 | 0.4523 | 0.9046 | 0.5477 |
18 | 0.3911 | 0.7821 | 0.6089 |
19 | 0.316 | 0.6321 | 0.684 |
20 | 0.5439 | 0.9122 | 0.4561 |
21 | 0.4265 | 0.8531 | 0.5735 |
22 | 0.4157 | 0.8314 | 0.5843 |
23 | 0.5318 | 0.9365 | 0.4682 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 0 | 0 | OK |
5% type I error level | 0 | 0 | OK |
10% type I error level | 0 | 0 | OK |
Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 4.8233, df1 = 2, df2 = 24, p-value = 0.01735 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 1.4272, df1 = 6, df2 = 20, p-value = 0.2533 |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 3.7184, df1 = 2, df2 = 24, p-value = 0.0392 |
Variance Inflation Factors (Multicollinearity) |
> vif income temp price 1.144186 1.144367 1.035673 |