Multiple Linear Regression - Estimated Regression Equation
Rate[t] = + 4.675 -1.9StatusDummy[t] + 2.4CurryDummy[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+4.675 0.3532+1.3240e+01 1.574e-21 7.868e-22
StatusDummy-1.9 0.4078-4.6590e+00 1.308e-05 6.539e-06
CurryDummy+2.4 0.4078+5.8850e+00 9.78e-08 4.89e-08


Multiple Linear Regression - Regression Statistics
Multiple R 0.65
R-squared 0.4225
Adjusted R-squared 0.4075
F-TEST (value) 28.17
F-TEST (DF numerator)2
F-TEST (DF denominator)77
p-value 6.603e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.824
Sum Squared Residuals 256.1


Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 4 5.175-1.175
2 5 5.175-0.175
3 3 5.175-2.175
4 4 5.175-1.175
5 5 5.175-0.175
6 3 5.175-2.175
7 7 5.175 1.825
8 5 5.175-0.175
9 6 5.175 0.825
10 3 5.175-2.175
11 2 5.175-3.175
12 4 5.175-1.175
13 5 5.175-0.175
14 2 5.175-3.175
15 3 5.175-2.175
16 6 5.175 0.825
17 4 5.175-1.175
18 4 5.175-1.175
19 6 5.175 0.825
20 2 5.175-3.175
21 3 2.775 0.225
22 5 2.775 2.225
23 4 2.775 1.225
24 2 2.775-0.775
25 7 2.775 4.225
26 1 2.775-1.775
27 4 2.775 1.225
28 4 2.775 1.225
29 7 2.775 4.225
30 4 2.775 1.225
31 3 2.775 0.225
32 3 2.775 0.225
33 3 2.775 0.225
34 3 2.775 0.225
35 2 2.775-0.775
36 5 2.775 2.225
37 5 2.775 2.225
38 3 2.775 0.225
39 6 2.775 3.225
40 2 2.775-0.775
41 8 7.075 0.925
42 9 7.075 1.925
43 10 7.075 2.925
44 7 7.075-0.075
45 8 7.075 0.925
46 9 7.075 1.925
47 10 7.075 2.925
48 6 7.075-1.075
49 6 7.075-1.075
50 7 7.075-0.075
51 8 7.075 0.925
52 9 7.075 1.925
53 8 7.075 0.925
54 7 7.075-0.075
55 5 7.075-2.075
56 11 7.075 3.925
57 7 7.075-0.075
58 8 7.075 0.925
59 10 7.075 2.925
60 9 7.075 1.925
61 3 4.675-1.675
62 5 4.675 0.325
63 4 4.675-0.675
64 2 4.675-2.675
65 6 4.675 1.325
66 1 4.675-3.675
67 4 4.675-0.675
68 4 4.675-0.675
69 5 4.675 0.325
70 4 4.675-0.675
71 3 4.675-1.675
72 3 4.675-1.675
73 4 4.675-0.675
74 3 4.675-1.675
75 2 4.675-2.675
76 5 4.675 0.325
77 4 4.675-0.675
78 3 4.675-1.675
79 6 4.675 1.325
80 2 4.675-2.675


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.2409 0.4818 0.7591
7 0.5059 0.9881 0.4941
8 0.3696 0.7392 0.6304
9 0.3218 0.6437 0.6782
10 0.3114 0.6228 0.6886
11 0.4262 0.8524 0.5738
12 0.3349 0.6699 0.6651
13 0.2611 0.5223 0.7389
14 0.3659 0.7317 0.6341
15 0.3586 0.7171 0.6414
16 0.3666 0.7332 0.6334
17 0.3208 0.6416 0.6792
18 0.2898 0.5795 0.7102
19 0.3008 0.6017 0.6992
20 0.5615 0.877 0.4385
21 0.4885 0.9769 0.5115
22 0.4723 0.9446 0.5277
23 0.3977 0.7954 0.6023
24 0.4218 0.8436 0.5782
25 0.6521 0.6957 0.3479
26 0.792 0.4159 0.208
27 0.7395 0.521 0.2605
28 0.681 0.638 0.319
29 0.8441 0.3118 0.1559
30 0.8016 0.3967 0.1984
31 0.7672 0.4656 0.2328
32 0.7281 0.5438 0.2719
33 0.6853 0.6294 0.3147
34 0.6401 0.7197 0.3599
35 0.6683 0.6634 0.3317
36 0.6393 0.7213 0.3607
37 0.6136 0.7729 0.3864
38 0.5667 0.8666 0.4333
39 0.6746 0.6508 0.3254
40 0.6557 0.6887 0.3443
41 0.5928 0.8145 0.4072
42 0.5464 0.9073 0.4536
43 0.5611 0.8778 0.4389
44 0.5473 0.9054 0.4527
45 0.483 0.966 0.517
46 0.4372 0.8744 0.5628
47 0.4701 0.9403 0.5299
48 0.536 0.9279 0.464
49 0.5946 0.8109 0.4054
50 0.5627 0.8746 0.4373
51 0.4944 0.9889 0.5056
52 0.4472 0.8943 0.5528
53 0.379 0.758 0.621
54 0.3501 0.7002 0.6499
55 0.6393 0.7214 0.3607
56 0.754 0.492 0.246
57 0.7637 0.4726 0.2363
58 0.7359 0.5283 0.2641
59 0.7066 0.5868 0.2934
60 0.64 0.72 0.36
61 0.6938 0.6124 0.3062
62 0.6857 0.6287 0.3143
63 0.6488 0.7023 0.3512
64 0.7212 0.5575 0.2788
65 0.7883 0.4235 0.2117
66 0.9234 0.1531 0.07656
67 0.8842 0.2317 0.1158
68 0.8293 0.3414 0.1707
69 0.8078 0.3845 0.1922
70 0.7277 0.5446 0.2723
71 0.6374 0.7251 0.3626
72 0.5303 0.9395 0.4697
73 0.3931 0.7863 0.6069
74 0.2673 0.5346 0.7327


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 18.314, df1 = 2, df2 = 75, p-value = 3.336e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 4, df2 = 73, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 75, p-value = 1


Variance Inflation Factors (Multicollinearity)
> vif
StatusDummy  CurryDummy 
          1           1