Multiple Linear Regression - Estimated Regression Equation |
y[t] = + 1.96779661016949 + 0.932203389830508x[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) | 1.96779661016949 | 0.070915 | 27.7488 | 0 | 0 |
x | 0.932203389830508 | 0.549302 | 1.6971 | 0.095044 | 0.047522 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.217501426841003 |
R-squared | 0.0473068706778723 |
Adjusted R-squared | 0.0308811270688701 |
F-TEST (value) | 2.88004438666299 |
F-TEST (DF numerator) | 1 |
F-TEST (DF denominator) | 58 |
p-value | 0.0950442637767794 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.544705133129179 |
Sum Squared Residuals | 17.2088135593220 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 1.3 | 1.96779661016949 | -0.667796610169493 |
2 | 1.2 | 1.96779661016949 | -0.767796610169491 |
3 | 1.6 | 1.96779661016949 | -0.367796610169491 |
4 | 1.7 | 1.96779661016949 | -0.267796610169491 |
5 | 1.5 | 1.96779661016949 | -0.467796610169491 |
6 | 0.9 | 1.96779661016949 | -1.06779661016949 |
7 | 1.5 | 1.96779661016949 | -0.467796610169491 |
8 | 1.4 | 1.96779661016949 | -0.567796610169491 |
9 | 1.6 | 1.96779661016949 | -0.367796610169491 |
10 | 1.7 | 1.96779661016949 | -0.267796610169491 |
11 | 1.4 | 1.96779661016949 | -0.567796610169491 |
12 | 1.8 | 1.96779661016949 | -0.167796610169491 |
13 | 1.7 | 1.96779661016949 | -0.267796610169491 |
14 | 1.4 | 1.96779661016949 | -0.567796610169491 |
15 | 1.2 | 1.96779661016949 | -0.767796610169492 |
16 | 1 | 1.96779661016949 | -0.967796610169491 |
17 | 1.7 | 1.96779661016949 | -0.267796610169491 |
18 | 2.4 | 1.96779661016949 | 0.432203389830508 |
19 | 2 | 1.96779661016949 | 0.0322033898305086 |
20 | 2.1 | 1.96779661016949 | 0.132203389830509 |
21 | 2 | 1.96779661016949 | 0.0322033898305086 |
22 | 1.8 | 1.96779661016949 | -0.167796610169491 |
23 | 2.7 | 1.96779661016949 | 0.732203389830509 |
24 | 2.3 | 1.96779661016949 | 0.332203389830508 |
25 | 1.9 | 1.96779661016949 | -0.0677966101694915 |
26 | 2 | 1.96779661016949 | 0.0322033898305086 |
27 | 2.3 | 1.96779661016949 | 0.332203389830508 |
28 | 2.8 | 1.96779661016949 | 0.832203389830508 |
29 | 2.4 | 1.96779661016949 | 0.432203389830508 |
30 | 2.3 | 1.96779661016949 | 0.332203389830508 |
31 | 2.7 | 1.96779661016949 | 0.732203389830509 |
32 | 2.7 | 1.96779661016949 | 0.732203389830509 |
33 | 2.9 | 1.96779661016949 | 0.932203389830509 |
34 | 3 | 1.96779661016949 | 1.03220338983051 |
35 | 2.2 | 1.96779661016949 | 0.232203389830509 |
36 | 2.3 | 1.96779661016949 | 0.332203389830508 |
37 | 2.8 | 1.96779661016949 | 0.832203389830508 |
38 | 2.8 | 1.96779661016949 | 0.832203389830508 |
39 | 2.8 | 1.96779661016949 | 0.832203389830508 |
40 | 2.2 | 1.96779661016949 | 0.232203389830509 |
41 | 2.6 | 1.96779661016949 | 0.632203389830509 |
42 | 2.8 | 1.96779661016949 | 0.832203389830508 |
43 | 2.5 | 1.96779661016949 | 0.532203389830509 |
44 | 2.4 | 1.96779661016949 | 0.432203389830508 |
45 | 2.3 | 1.96779661016949 | 0.332203389830508 |
46 | 1.9 | 1.96779661016949 | -0.0677966101694915 |
47 | 1.7 | 1.96779661016949 | -0.267796610169491 |
48 | 2 | 1.96779661016949 | 0.0322033898305086 |
49 | 2.1 | 1.96779661016949 | 0.132203389830509 |
50 | 1.7 | 1.96779661016949 | -0.267796610169491 |
51 | 1.8 | 1.96779661016949 | -0.167796610169491 |
52 | 1.8 | 1.96779661016949 | -0.167796610169491 |
53 | 1.8 | 1.96779661016949 | -0.167796610169491 |
54 | 1.3 | 1.96779661016949 | -0.667796610169491 |
55 | 1.3 | 1.96779661016949 | -0.667796610169491 |
56 | 1.3 | 1.96779661016949 | -0.667796610169491 |
57 | 1.2 | 1.96779661016949 | -0.767796610169492 |
58 | 1.4 | 1.96779661016949 | -0.567796610169491 |
59 | 2.2 | 1.96779661016949 | 0.232203389830509 |
60 | 2.9 | 2.9 | -6.50521303491303e-17 |