Multiple Linear Regression - Estimated Regression Equation |
d[t] = + 0.717743972649403 + 0.267095376844774a[t] + 0.235849949421252b[t] + 0.378073882408914c[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) | 0.717743972649403 | 1.463505 | 0.4904 | 0.635566 | 0.317783 |
a | 0.267095376844774 | 0.162932 | 1.6393 | 0.135572 | 0.067786 |
b | 0.235849949421252 | 0.114061 | 2.0678 | 0.068612 | 0.034306 |
c | 0.378073882408914 | 0.663848 | 0.5695 | 0.582937 | 0.291469 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.73475078302044 |
R-squared | 0.539858713149149 |
Adjusted R-squared | 0.386478284198865 |
F-TEST (value) | 3.5197366237914 |
F-TEST (DF numerator) | 3 |
F-TEST (DF denominator) | 9 |
p-value | 0.0620257294553627 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 2.35984944866166 |
Sum Squared Residuals | 50.1200047831389 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 6 | 7.6201038923867 | -1.6201038923867 |
2 | 6 | 5.29262929392375 | 0.707370706076251 |
3 | 6 | 3.45420708343385 | 2.54579291656615 |
4 | 6 | 4.80714672262525 | 1.19285327737475 |
5 | 2 | -0.194630929286035 | 2.19463092928604 |
6 | 2 | 1.86574829233214 | 0.134251707667861 |
7 | 2 | 3.61054448638844 | -1.61054448638844 |
8 | 3 | 3.16986948329551 | -0.169869483295509 |
9 | -1 | 0.245823542132945 | -1.24582354213295 |
10 | -4 | 0.876879331419706 | -4.87687933141971 |
11 | 4 | 1.52193832483642 | 2.47806167516358 |
12 | 5 | 4.61945362641016 | 0.380546373589835 |
13 | 3 | 3.11028685010116 | -0.110286850101157 |