Multiple Linear Regression - Estimated Regression Equation
f[t] = + 83.8616 + 0.691554e[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+83.86 14.83+5.6540e+00 7.954e-07 3.977e-07
e+0.6915 0.05296+1.3060e+01 1.416e-17 7.079e-18


Multiple Linear Regression - Regression Statistics
Multiple R 0.8814
R-squared 0.7768
Adjusted R-squared 0.7722
F-TEST (value) 170.5
F-TEST (DF numerator)1
F-TEST (DF denominator)49
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 23.11
Sum Squared Residuals 2.618e+04


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
1 362.8 356.7 6.131
2 362.8 349.6 13.16
3 362.8 342.5 20.25
4 353 323.6 29.34
5 343.2 314.5 28.67
6 323.6 320 3.536
7 313.8 315.5-1.731
8 304 283.4 20.54
9 284.4 253 31.36
10 274.5 250.2 24.3
11 254.9 239.9 15.07
12 254.9 231.8 23.17
13 254.9 237.3 17.65
14 254.9 277.3-22.4
15 245.1 266.2-21.07
16 245.1 249.9-4.815
17 245.1 242.7 2.467
18 245.1 237.7 7.384
19 245.1 231.3 13.82
20 245.1 222 23.17
21 225.5 227.6-2.047
22 225.5 240.6-15.03
23 225.5 231.9-6.41
24 225.5 227-1.459
25 225.5 222.4 3.168
26 214.9 214.7 0.2194
27 205.6 210.1-4.529
28 196.3 212-15.74
29 189.2 216.1-26.9
30 189.2 232.5-43.22
31 189.2 244-54.74
32 214.9 266.8-51.83
33 238.3 288.9-50.61
34 261.7 292.4-30.69
35 294.4 296.9-2.545
36 317.8 303.4 14.36
37 308.4 300.4 8.02
38 299.1 282.3 16.72
39 299.1 274.7 24.38
40 299.1 277.4 21.67
41 299.1 307.7-8.63
42 308.4 336.7-28.35
43 308.4 336.4-28.01
44 317.8 348-30.25
45 342.9 349.7-6.808
46 317.8 314.5 3.243
47 308.4 280.5 27.91
48 293.3 255.8 37.53
49 261.7 248.3 13.35
50 266.5 267.9-1.429
51 272.9 264.2 8.647


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.005009 0.01002 0.995
6 0.07744 0.1549 0.9226
7 0.1074 0.2149 0.8926
8 0.05508 0.1102 0.9449
9 0.03119 0.06239 0.9688
10 0.0157 0.03139 0.9843
11 0.01057 0.02115 0.9894
12 0.005074 0.01015 0.9949
13 0.002565 0.00513 0.9974
14 0.05283 0.1057 0.9472
15 0.131 0.262 0.869
16 0.1105 0.221 0.8895
17 0.07689 0.1538 0.9231
18 0.05 0.1 0.95
19 0.03329 0.06659 0.9667
20 0.0287 0.0574 0.9713
21 0.02052 0.04104 0.9795
22 0.02311 0.04622 0.9769
23 0.01659 0.03318 0.9834
24 0.01023 0.02046 0.9898
25 0.005906 0.01181 0.9941
26 0.003321 0.006641 0.9967
27 0.001894 0.003789 0.9981
28 0.001522 0.003045 0.9985
29 0.002412 0.004825 0.9976
30 0.01637 0.03274 0.9836
31 0.1982 0.3964 0.8018
32 0.6898 0.6204 0.3102
33 0.973 0.05402 0.02701
34 0.9956 0.00884 0.00442
35 0.9919 0.01629 0.008147
36 0.9894 0.02129 0.01064
37 0.9809 0.03815 0.01908
38 0.9683 0.06344 0.03172
39 0.9576 0.08484 0.04242
40 0.9412 0.1177 0.05884
41 0.9011 0.1978 0.09889
42 0.879 0.242 0.121
43 0.8615 0.277 0.1385
44 0.874 0.252 0.126
45 0.7684 0.4631 0.2316
46 0.6188 0.7624 0.3812


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level6 0.1429NOK
5% type I error level190.452381NOK
10% type I error level260.619048NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.29851, df1 = 2, df2 = 47, p-value = 0.7433
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.29851, df1 = 2, df2 = 47, p-value = 0.7433
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.29851, df1 = 2, df2 = 47, p-value = 0.7433