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Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center
R Engine
error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted


Multiple Linear Regression - Estimated Regression Equation
^CPI[t] = + 0.10473 + 0.0186338`^M`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)+0.1047 0.01982+5.2850e+00 1.617e-06 8.083e-07
`^M`+0.01863 0.01366+1.3650e+00 0.1772 0.08858


Multiple Linear Regression - Regression Statistics
Multiple R 0.1681
R-squared 0.02827
Adjusted R-squared 0.01309
F-TEST (value) 1.862
F-TEST (DF numerator)1
F-TEST (DF denominator)64
p-value 0.1772
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.1099
Sum Squared Residuals 0.7734


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 0.1 0.1351-0.0351
2 0.07 0.1217-0.05169
3 0.18 0.1278 0.05216
4 0.03 0.1306-0.1006
5-0.077 0.114-0.191
6 0.1 0.1187-0.01871
7 0.123 0.109 0.01398
8 0.047 0.1083-0.06127
9 0.063 0.1122-0.04918
10-0.043 0.1075-0.1505
11-0.07 0.128-0.198
12-0.083 0.1312-0.2142
13 0.036 0.1329-0.09687
14-0.036 0.1181-0.1541
15 0.02 0.1157-0.09572
16 0.08 0.1107-0.03069
17 0.003 0.1148-0.1118
18 0.177 0.1088 0.06817
19 0.28 0.1058 0.1742
20 0.233 0.1161 0.1169
21 0.227 0.1105 0.1165
22 0.236 0.1053 0.1307
23 0.25 0.1045 0.1455
24 0.137 0.09299 0.04401
25 0.337 0.101 0.236
26 0.193 0.1321 0.06088
27-0.017 0.1319-0.1489
28 0.03 0.1379-0.1079
29 0.05 0.1394-0.08939
30 0.05 0.1271-0.07709
31 0.15 0.1234 0.02664
32 0.177 0.08125 0.09575
33 0.027 0.09784-0.07084
34 0.176 0.09914 0.07686
35 0.017 0.1295-0.1125
36 0.19 0.1029 0.08713
37 0.06 0.1178-0.05777
38-0.01 0.1239-0.1339
39 0.117 0.1204-0.003383
40 0.043 0.129-0.08595
41 0.117 0.1221-0.00506
42 0.113 0.1228-0.009805
43 0.087 0.1023-0.01531
44 0.073 0.1196-0.04664
45 0.097 0.1332-0.03624
46 0.056 0.1303-0.07426
47 0.187 0.1332 0.05376
48 0.083 0.1321-0.04912
49 0.127 0.1234 0.003636
50 0.05 0.1245-0.07448
51 0.07 0.1521-0.08206
52 0.143 0.1543-0.0113
53 0.097 0.1271-0.03009
54 0.2 0.1222 0.07775
55 0.093 0.1394-0.04639
56 0.167 0.1606 0.006368
57 0.297 0.1588 0.1382
58 0.29 0.139 0.151
59 0.28 0.09467 0.1853
60 0.266 0.114 0.152
61 0.084 0.1364-0.05241
62 0.2 0.1495 0.05055
63 0.333 0.1768 0.1562
64 0.367 0.1593 0.2077
65 0.333 0.1495 0.1835
66 0.333 0.1638 0.1692


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.3536 0.7072 0.6464
6 0.2912 0.5825 0.7088
7 0.2978 0.5956 0.7022
8 0.1885 0.377 0.8115
9 0.1123 0.2246 0.8877
10 0.1043 0.2087 0.8957
11 0.2059 0.4117 0.7941
12 0.312 0.6241 0.688
13 0.2412 0.4824 0.7588
14 0.2335 0.4669 0.7665
15 0.1821 0.3643 0.8179
16 0.1425 0.285 0.8575
17 0.1169 0.2338 0.8831
18 0.1581 0.3161 0.8419
19 0.3678 0.7355 0.6322
20 0.4664 0.9327 0.5336
21 0.5023 0.9954 0.4977
22 0.5149 0.9703 0.4851
23 0.5347 0.9305 0.4653
24 0.4761 0.9522 0.5239
25 0.6724 0.6551 0.3276
26 0.7169 0.5661 0.2831
27 0.7277 0.5447 0.2723
28 0.7134 0.5732 0.2866
29 0.6968 0.6064 0.3032
30 0.6606 0.6788 0.3394
31 0.6137 0.7727 0.3863
32 0.6082 0.7836 0.3918
33 0.608 0.7841 0.392
34 0.5728 0.8543 0.4272
35 0.573 0.854 0.427
36 0.5567 0.8867 0.4433
37 0.5007 0.9986 0.4993
38 0.5451 0.9098 0.4549
39 0.4772 0.9544 0.5228
40 0.4647 0.9295 0.5353
41 0.4004 0.8007 0.5996
42 0.3396 0.6791 0.6604
43 0.2827 0.5653 0.7173
44 0.2431 0.4861 0.7569
45 0.2179 0.4358 0.7821
46 0.2201 0.4402 0.7799
47 0.2047 0.4093 0.7953
48 0.1945 0.3889 0.8055
49 0.1571 0.3141 0.8429
50 0.1866 0.3732 0.8134
51 0.2755 0.5511 0.7245
52 0.311 0.622 0.689
53 0.3523 0.7046 0.6477
54 0.2958 0.5916 0.7042
55 0.4493 0.8986 0.5507
56 0.5527 0.8946 0.4473
57 0.5261 0.9478 0.4739
58 0.4591 0.9182 0.5409
59 0.4594 0.9188 0.5406
60 0.6237 0.7525 0.3763
61 0.712 0.576 0.288


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 = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05












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