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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 03 Dec 2016 15:13:16 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/03/t1480774527lss04rnh0u1qp63.htm/, Retrieved Sun, 05 May 2024 14:11:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297628, Retrieved Sun, 05 May 2024 14:11:50 +0000

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Estimated Impact

120

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2016-12-03 14:13:16] [73c8181f60882f9827d3eab75df83592] [Current]

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4	2	4	3	5	4	13	0
5	3	3	4	5	4	16	1
4	4	5	4	5	4	17	1
3	4	3	3	4	4	NA	1
4	4	5	4	5	4	NA	0
3	4	4	4	5	5	16	1
3	4	4	3	3	4	NA	1
3	4	5	4	4	4	NA	1
4	5	4	4	5	5	NA	0
4	5	5	4	5	5	17	0
4	4	2	4	5	4	17	0
4	4	5	3	5	4	15	0
4	4	4	3	4	5	16	0
3	3	5	4	4	5	14	0
4	4	5	4	2	5	16	0
3	4	5	4	4	5	17	1
3	4	5	4	4	5	NA	0
NA	NA	5	NA	5	5	NA	1
5	5	4	3	4	4	NA	1
4	4	4	4	5	4	NA	1
3	4	5	3	4	5	16	1
4	4	4	4	5	5	NA	0
4	4	5	4	4	5	16	1
4	4	5	4	4	4	NA	1
4	4	5	4	4	5	NA	0
3	4	4	4	4	4	NA	0
3	4	4	3	5	5	16	0
4	4	4	4	4	4	15	0
2	4	5	4	5	5	16	0
5	4	4	4	4	4	16	0
4	3	5	4	4	4	13	0
4	5	5	4	5	5	15	1
5	4	5	4	4	5	17	0
4	3	5	4	NA	5	NA	0
2	3	5	4	5	4	13	1
4	5	2	4	4	4	17	0
3	4	5	4	4	4	NA	0
4	3	5	3	4	5	14	1
4	3	3	4	4	4	14	0
4	4	5	4	4	4	18	0
5	4	4	4	4	4	NA	1
4	5	5	4	5	5	17	1
3	3	4	4	4	4	13	1
5	5	5	3	5	5	16	1
5	4	5	3	4	4	15	1
4	4	4	3	4	5	15	1
4	4	4	4	4	4	NA	1
3	5	5	3	3	4	15	1
4	4	4	4	5	4	13	1
2	3	4	2	NA	4	NA	0
4	5	5	4	4	4	17	1
5	5	2	4	5	4	NA	0
5	5	5	4	4	4	NA	1
4	3	5	4	5	5	11	1
4	3	4	3	4	5	14	0
4	4	5	4	4	4	13	1
3	4	4	3	3	4	NA	0
3	4	4	4	4	3	17	0
4	4	4	3	5	4	16	1
4	4	4	4	5	4	NA	0
5	5	3	4	5	5	17	1
2	4	4	4	5	5	16	1
4	4	4	4	5	5	16	1
3	4	4	4	2	4	16	1
4	4	5	4	5	5	15	0
4	2	4	4	4	4	12	1
4	4	4	3	5	3	17	0
4	4	4	3	5	4	14	1
5	4	5	3	3	5	14	0
3	4	4	3	5	5	16	1
3	4	4	3	4	5	NA	1
4	5	5	5	5	4	NA	1
4	4	3	4	NA	4	NA	0
4	4	4	4	4	4	NA	1
4	4	4	5	5	4	NA	0
3	4	3	4	4	4	15	1
4	4	4	4	5	4	16	0
3	4	5	3	5	5	14	1
3	3	5	4	4	5	15	0
4	3	5	4	4	4	17	1
4	4	5	4	4	5	NA	1
3	3	3	4	4	4	10	0
4	4	4	4	5	4	NA	0
4	4	3	4	5	5	17	0
4	4	4	4	5	5	NA	1
5	4	4	4	4	4	20	1
5	4	3	5	4	5	17	1
4	4	5	4	5	5	18	1
3	4	5	4	4	5	NA	1
3	NA	4	4	4	4	17	0
4	2	3	3	4	4	14	1
4	4	5	4	4	3	NA	0
4	4	5	4	4	5	17	0
4	4	4	4	5	4	NA	0
4	5	4	4	5	3	17	0
3	4	4	3	5	5	NA	1
4	4	5	4	4	5	16	1
5	4	3	4	4	5	18	1
5	4	5	5	4	5	18	0
4	5	4	4	5	5	16	1
3	4	5	4	4	5	NA	1
5	3	4	4	5	5	NA	0
4	4	5	4	4	5	15	0
5	4	4	4	4	5	13	1
3	4	4	3	NA	4	NA	1
5	4	4	5	5	5	NA	1
4	4	5	3	NA	5	NA	1
4	4	3	3	4	3	NA	0
4	4	5	4	4	4	NA	0
4	4	5	4	4	4	16	0
3	4	5	4	5	3	NA	1
4	4	4	4	4	4	NA	1
4	4	4	3	4	5	NA	1
3	3	4	3	5	5	12	0
4	4	4	3	4	4	NA	0
3	4	5	4	4	4	16	1
4	4	5	4	3	4	16	0
5	4	5	1	5	5	NA	0
5	4	5	4	5	5	16	1
4	4	4	4	4	3	14	0
4	4	5	3	4	4	15	0
3	4	4	3	4	5	14	0
4	4	4	4	4	4	NA	1
4	4	4	4	5	4	15	1
4	5	3	4	4	4	NA	1
3	4	4	4	4	4	15	1
4	4	4	3	4	4	16	1
4	4	4	4	4	5	NA	0
3	4	3	3	4	4	NA	0
4	4	4	3	4	3	NA	1
3	2	4	2	4	4	11	1
4	4	4	3	5	4	NA	1
5	4	4	3	5	4	18	0
2	4	4	3	3	5	NA	1
3	3	4	4	4	4	11	0
4	4	4	3	4	4	NA	0
5	5	4	4	5	4	18	0
NA	NA	2	NA	NA	NA	NA	0
4	5	5	4	4	4	15	1
5	5	5	5	5	4	19	0
4	5	5	4	5	5	17	0
4	4	4	3	4	5	NA	0
3	4	5	4	5	4	14	0
4	4	5	4	4	4	NA	1
4	4	2	4	4	4	13	0
4	4	3	4	5	5	17	1
4	4	4	4	5	5	14	1
5	4	5	3	5	4	19	1
4	3	5	4	4	4	14	1
4	4	5	4	4	4	NA	0
3	3	2	3	4	4	NA	0
4	5	5	4	4	3	16	0
4	4	4	3	4	4	16	0
4	4	4	4	4	5	15	1
3	4	5	3	5	5	12	1
4	4	5	4	4	5	NA	1
5	4	5	4	5	4	17	1
4	4	5	4	3	4	NA	0
2	3	5	4	4	4	NA	1
4	4	4	4	4	5	18	0
4	3	4	3	5	5	15	1
4	4	4	4	4	3	18	0
4	5	5	5	4	4	15	0
5	4	3	4	4	4	NA	0
5	4	4	3	4	4	NA	0
3	3	1	4	5	5	NA	0
4	4	4	4	4	5	16	1
4	4	4	4	5	4	NA	1
2	3	4	5	5	4	16	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297628&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.02325 + 0.649713SK1[t] + 1.20144SK2[t] -0.0140394SK3[t] + 0.462548SK4[t] + 0.161402SK5[t] -0.022023SK6[t] -0.137479G[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.02325 +  0.649713SK1[t] +  1.20144SK2[t] -0.0140394SK3[t] +  0.462548SK4[t] +  0.161402SK5[t] -0.022023SK6[t] -0.137479G[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.02325 +  0.649713SK1[t] +  1.20144SK2[t] -0.0140394SK3[t] +  0.462548SK4[t] +  0.161402SK5[t] -0.022023SK6[t] -0.137479G[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.02325 + 0.649713SK1[t] + 1.20144SK2[t] -0.0140394SK3[t] + 0.462548SK4[t] + 0.161402SK5[t] -0.022023SK6[t] -0.137479G[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.023	2.149	+2.8030e+00	0.006156	0.003078
SK1	+0.6497	0.2152	+3.0190e+00	0.003265	0.001633
SK2	+1.201	0.2391	+5.0250e+00	2.392e-06	1.196e-06
SK3	-0.01404	0.2044	-6.8670e-02	0.9454	0.4727
SK4	+0.4626	0.2886	+1.6030e+00	0.1124	0.05619
SK5	+0.1614	0.2425	+6.6550e-01	0.5074	0.2537
SK6	-0.02202	0.2629	-8.3780e-02	0.9334	0.4667
G	-0.1375	0.3141	-4.3780e-01	0.6626	0.3313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +6.023 &  2.149 & +2.8030e+00 &  0.006156 &  0.003078 \tabularnewline
SK1 & +0.6497 &  0.2152 & +3.0190e+00 &  0.003265 &  0.001633 \tabularnewline
SK2 & +1.201 &  0.2391 & +5.0250e+00 &  2.392e-06 &  1.196e-06 \tabularnewline
SK3 & -0.01404 &  0.2044 & -6.8670e-02 &  0.9454 &  0.4727 \tabularnewline
SK4 & +0.4626 &  0.2886 & +1.6030e+00 &  0.1124 &  0.05619 \tabularnewline
SK5 & +0.1614 &  0.2425 & +6.6550e-01 &  0.5074 &  0.2537 \tabularnewline
SK6 & -0.02202 &  0.2629 & -8.3780e-02 &  0.9334 &  0.4667 \tabularnewline
G & -0.1375 &  0.3141 & -4.3780e-01 &  0.6626 &  0.3313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+6.023[/C][C] 2.149[/C][C]+2.8030e+00[/C][C] 0.006156[/C][C] 0.003078[/C][/ROW]
[ROW][C]SK1[/C][C]+0.6497[/C][C] 0.2152[/C][C]+3.0190e+00[/C][C] 0.003265[/C][C] 0.001633[/C][/ROW]
[ROW][C]SK2[/C][C]+1.201[/C][C] 0.2391[/C][C]+5.0250e+00[/C][C] 2.392e-06[/C][C] 1.196e-06[/C][/ROW]
[ROW][C]SK3[/C][C]-0.01404[/C][C] 0.2044[/C][C]-6.8670e-02[/C][C] 0.9454[/C][C] 0.4727[/C][/ROW]
[ROW][C]SK4[/C][C]+0.4626[/C][C] 0.2886[/C][C]+1.6030e+00[/C][C] 0.1124[/C][C] 0.05619[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1614[/C][C] 0.2425[/C][C]+6.6550e-01[/C][C] 0.5074[/C][C] 0.2537[/C][/ROW]
[ROW][C]SK6[/C][C]-0.02202[/C][C] 0.2629[/C][C]-8.3780e-02[/C][C] 0.9334[/C][C] 0.4667[/C][/ROW]
[ROW][C]G[/C][C]-0.1375[/C][C] 0.3141[/C][C]-4.3780e-01[/C][C] 0.6626[/C][C] 0.3313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.023	2.149	+2.8030e+00	0.006156	0.003078
SK1	+0.6497	0.2152	+3.0190e+00	0.003265	0.001633
SK2	+1.201	0.2391	+5.0250e+00	2.392e-06	1.196e-06
SK3	-0.01404	0.2044	-6.8670e-02	0.9454	0.4727
SK4	+0.4626	0.2886	+1.6030e+00	0.1124	0.05619
SK5	+0.1614	0.2425	+6.6550e-01	0.5074	0.2537
SK6	-0.02202	0.2629	-8.3780e-02	0.9334	0.4667
G	-0.1375	0.3141	-4.3780e-01	0.6626	0.3313

Multiple Linear Regression - Regression Statistics
Multiple R	0.6155
R-squared	0.3788
Adjusted R-squared	0.3326
F-TEST (value)	8.19
F-TEST (DF numerator)	7
F-TEST (DF denominator)	94
p-value	9.169e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.532
Sum Squared Residuals	220.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6155 \tabularnewline
R-squared &  0.3788 \tabularnewline
Adjusted R-squared &  0.3326 \tabularnewline
F-TEST (value) &  8.19 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  9.169e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.532 \tabularnewline
Sum Squared Residuals &  220.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6155[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3788[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3326[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 8.19[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C] 9.169e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.532[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 220.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.6155
R-squared	0.3788
Adjusted R-squared	0.3326
F-TEST (value)	8.19
F-TEST (DF numerator)	7
F-TEST (DF denominator)	94
p-value	9.169e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.532
Sum Squared Residuals	220.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.08	-0.07539
2	16	15.27	0.7343
3	17	15.79	1.211
4	16	15.13	0.8684
5	17	17.11	-0.1062
6	17	15.97	1.031
7	15	15.46	-0.4642
8	16	15.29	0.7052
9	14	13.89	0.1078
10	16	15.42	0.5794
11	17	14.96	2.044
12	16	14.49	1.506
13	16	15.61	0.3941
14	16	14.81	1.193
15	15	15.78	-0.7794
16	16	14.61	1.395
17	16	16.43	-0.4291
18	13	14.56	-1.564
19	15	16.97	-1.969
20	17	16.39	0.6069
21	13	13.29	-0.2884
22	17	17.01	-0.008941
23	14	13.94	0.05811
24	14	14.59	-0.592
25	18	15.77	2.235
26	17	16.97	0.03128
27	13	13.79	-0.7908
28	16	17.16	-1.156
29	15	15.82	-0.8151
30	15	15.16	-0.1574
31	15	15.56	-0.5557
32	13	15.8	-2.803
33	17	16.83	0.1707
34	11	14.57	-3.566
35	14	14.09	-0.09341
36	13	15.63	-2.628
37	17	15.15	1.848
38	16	15.34	0.6592
39	17	17.65	-0.6465
40	16	14.48	1.518
41	16	15.78	0.2187
42	16	14.67	1.331
43	15	15.9	-0.9048
44	12	13.24	-1.239
45	17	15.5	1.5
46	14	15.34	-1.341
47	14	15.77	-1.769
48	16	14.67	1.331
49	15	15.01	-0.006267
50	16	15.94	0.05918
51	14	14.65	-0.655
52	15	13.89	1.108
53	17	14.43	2.574
54	10	13.94	-3.942
55	17	15.93	1.067
56	20	16.29	3.708
57	17	16.75	0.2538
58	18	15.77	2.233
59	14	12.79	1.209
60	17	15.74	1.257
61	17	17.16	-0.1643
62	16	15.61	0.3941
63	18	16.28	1.716
64	18	16.86	1.144
65	16	16.98	-0.9828
66	15	15.74	-0.7434
67	13	16.27	-3.27
68	16	15.77	0.2346
69	12	13.61	-1.605
70	16	14.98	1.022
71	16	15.6	0.396
72	16	16.42	-0.417
73	14	15.8	-1.801
74	15	15.3	-0.3028
75	14	14.65	-0.6451
76	15	15.8	-0.8033
77	15	14.99	0.007773
78	16	15.18	0.8206
79	11	11.66	-0.6642
80	18	16.13	1.872
81	11	13.93	-2.928
82	18	17.79	0.208
83	15	16.83	-1.829
84	19	18.24	0.7595
85	17	17.11	-0.1062
86	14	15.28	-1.277
87	13	15.81	-2.808
88	17	15.8	1.205
89	14	15.78	-1.781
90	19	15.98	3.024
91	14	14.43	-0.4265
92	16	16.99	-0.9888
93	16	15.32	0.6831
94	15	15.62	-0.6199
95	12	14.65	-2.655
96	17	16.44	0.561
97	18	15.76	2.243
98	15	14.12	0.8827
99	18	15.8	2.199
100	15	17.43	-2.429
101	16	15.62	0.3801
102	16	13.9	2.098

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.08 & -0.07539 \tabularnewline
2 &  16 &  15.27 &  0.7343 \tabularnewline
3 &  17 &  15.79 &  1.211 \tabularnewline
4 &  16 &  15.13 &  0.8684 \tabularnewline
5 &  17 &  17.11 & -0.1062 \tabularnewline
6 &  17 &  15.97 &  1.031 \tabularnewline
7 &  15 &  15.46 & -0.4642 \tabularnewline
8 &  16 &  15.29 &  0.7052 \tabularnewline
9 &  14 &  13.89 &  0.1078 \tabularnewline
10 &  16 &  15.42 &  0.5794 \tabularnewline
11 &  17 &  14.96 &  2.044 \tabularnewline
12 &  16 &  14.49 &  1.506 \tabularnewline
13 &  16 &  15.61 &  0.3941 \tabularnewline
14 &  16 &  14.81 &  1.193 \tabularnewline
15 &  15 &  15.78 & -0.7794 \tabularnewline
16 &  16 &  14.61 &  1.395 \tabularnewline
17 &  16 &  16.43 & -0.4291 \tabularnewline
18 &  13 &  14.56 & -1.564 \tabularnewline
19 &  15 &  16.97 & -1.969 \tabularnewline
20 &  17 &  16.39 &  0.6069 \tabularnewline
21 &  13 &  13.29 & -0.2884 \tabularnewline
22 &  17 &  17.01 & -0.008941 \tabularnewline
23 &  14 &  13.94 &  0.05811 \tabularnewline
24 &  14 &  14.59 & -0.592 \tabularnewline
25 &  18 &  15.77 &  2.235 \tabularnewline
26 &  17 &  16.97 &  0.03128 \tabularnewline
27 &  13 &  13.79 & -0.7908 \tabularnewline
28 &  16 &  17.16 & -1.156 \tabularnewline
29 &  15 &  15.82 & -0.8151 \tabularnewline
30 &  15 &  15.16 & -0.1574 \tabularnewline
31 &  15 &  15.56 & -0.5557 \tabularnewline
32 &  13 &  15.8 & -2.803 \tabularnewline
33 &  17 &  16.83 &  0.1707 \tabularnewline
34 &  11 &  14.57 & -3.566 \tabularnewline
35 &  14 &  14.09 & -0.09341 \tabularnewline
36 &  13 &  15.63 & -2.628 \tabularnewline
37 &  17 &  15.15 &  1.848 \tabularnewline
38 &  16 &  15.34 &  0.6592 \tabularnewline
39 &  17 &  17.65 & -0.6465 \tabularnewline
40 &  16 &  14.48 &  1.518 \tabularnewline
41 &  16 &  15.78 &  0.2187 \tabularnewline
42 &  16 &  14.67 &  1.331 \tabularnewline
43 &  15 &  15.9 & -0.9048 \tabularnewline
44 &  12 &  13.24 & -1.239 \tabularnewline
45 &  17 &  15.5 &  1.5 \tabularnewline
46 &  14 &  15.34 & -1.341 \tabularnewline
47 &  14 &  15.77 & -1.769 \tabularnewline
48 &  16 &  14.67 &  1.331 \tabularnewline
49 &  15 &  15.01 & -0.006267 \tabularnewline
50 &  16 &  15.94 &  0.05918 \tabularnewline
51 &  14 &  14.65 & -0.655 \tabularnewline
52 &  15 &  13.89 &  1.108 \tabularnewline
53 &  17 &  14.43 &  2.574 \tabularnewline
54 &  10 &  13.94 & -3.942 \tabularnewline
55 &  17 &  15.93 &  1.067 \tabularnewline
56 &  20 &  16.29 &  3.708 \tabularnewline
57 &  17 &  16.75 &  0.2538 \tabularnewline
58 &  18 &  15.77 &  2.233 \tabularnewline
59 &  14 &  12.79 &  1.209 \tabularnewline
60 &  17 &  15.74 &  1.257 \tabularnewline
61 &  17 &  17.16 & -0.1643 \tabularnewline
62 &  16 &  15.61 &  0.3941 \tabularnewline
63 &  18 &  16.28 &  1.716 \tabularnewline
64 &  18 &  16.86 &  1.144 \tabularnewline
65 &  16 &  16.98 & -0.9828 \tabularnewline
66 &  15 &  15.74 & -0.7434 \tabularnewline
67 &  13 &  16.27 & -3.27 \tabularnewline
68 &  16 &  15.77 &  0.2346 \tabularnewline
69 &  12 &  13.61 & -1.605 \tabularnewline
70 &  16 &  14.98 &  1.022 \tabularnewline
71 &  16 &  15.6 &  0.396 \tabularnewline
72 &  16 &  16.42 & -0.417 \tabularnewline
73 &  14 &  15.8 & -1.801 \tabularnewline
74 &  15 &  15.3 & -0.3028 \tabularnewline
75 &  14 &  14.65 & -0.6451 \tabularnewline
76 &  15 &  15.8 & -0.8033 \tabularnewline
77 &  15 &  14.99 &  0.007773 \tabularnewline
78 &  16 &  15.18 &  0.8206 \tabularnewline
79 &  11 &  11.66 & -0.6642 \tabularnewline
80 &  18 &  16.13 &  1.872 \tabularnewline
81 &  11 &  13.93 & -2.928 \tabularnewline
82 &  18 &  17.79 &  0.208 \tabularnewline
83 &  15 &  16.83 & -1.829 \tabularnewline
84 &  19 &  18.24 &  0.7595 \tabularnewline
85 &  17 &  17.11 & -0.1062 \tabularnewline
86 &  14 &  15.28 & -1.277 \tabularnewline
87 &  13 &  15.81 & -2.808 \tabularnewline
88 &  17 &  15.8 &  1.205 \tabularnewline
89 &  14 &  15.78 & -1.781 \tabularnewline
90 &  19 &  15.98 &  3.024 \tabularnewline
91 &  14 &  14.43 & -0.4265 \tabularnewline
92 &  16 &  16.99 & -0.9888 \tabularnewline
93 &  16 &  15.32 &  0.6831 \tabularnewline
94 &  15 &  15.62 & -0.6199 \tabularnewline
95 &  12 &  14.65 & -2.655 \tabularnewline
96 &  17 &  16.44 &  0.561 \tabularnewline
97 &  18 &  15.76 &  2.243 \tabularnewline
98 &  15 &  14.12 &  0.8827 \tabularnewline
99 &  18 &  15.8 &  2.199 \tabularnewline
100 &  15 &  17.43 & -2.429 \tabularnewline
101 &  16 &  15.62 &  0.3801 \tabularnewline
102 &  16 &  13.9 &  2.098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.08[/C][C]-0.07539[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.27[/C][C] 0.7343[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.79[/C][C] 1.211[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.13[/C][C] 0.8684[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.11[/C][C]-0.1062[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.97[/C][C] 1.031[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.46[/C][C]-0.4642[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.29[/C][C] 0.7052[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 13.89[/C][C] 0.1078[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.42[/C][C] 0.5794[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 14.96[/C][C] 2.044[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.49[/C][C] 1.506[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.61[/C][C] 0.3941[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.81[/C][C] 1.193[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.78[/C][C]-0.7794[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.61[/C][C] 1.395[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.43[/C][C]-0.4291[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.56[/C][C]-1.564[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 16.97[/C][C]-1.969[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.39[/C][C] 0.6069[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.29[/C][C]-0.2884[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 17.01[/C][C]-0.008941[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 13.94[/C][C] 0.05811[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.59[/C][C]-0.592[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.77[/C][C] 2.235[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.97[/C][C] 0.03128[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.79[/C][C]-0.7908[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.16[/C][C]-1.156[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.82[/C][C]-0.8151[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.16[/C][C]-0.1574[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.56[/C][C]-0.5557[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.8[/C][C]-2.803[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 16.83[/C][C] 0.1707[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.57[/C][C]-3.566[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 14.09[/C][C]-0.09341[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.63[/C][C]-2.628[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.15[/C][C] 1.848[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.34[/C][C] 0.6592[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.65[/C][C]-0.6465[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.48[/C][C] 1.518[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.78[/C][C] 0.2187[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 14.67[/C][C] 1.331[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.9[/C][C]-0.9048[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.24[/C][C]-1.239[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.5[/C][C] 1.5[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.34[/C][C]-1.341[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.77[/C][C]-1.769[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.67[/C][C] 1.331[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.01[/C][C]-0.006267[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.94[/C][C] 0.05918[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.65[/C][C]-0.655[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.89[/C][C] 1.108[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 14.43[/C][C] 2.574[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 13.94[/C][C]-3.942[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.93[/C][C] 1.067[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.29[/C][C] 3.708[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.75[/C][C] 0.2538[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.77[/C][C] 2.233[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 12.79[/C][C] 1.209[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 15.74[/C][C] 1.257[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 17.16[/C][C]-0.1643[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.61[/C][C] 0.3941[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.28[/C][C] 1.716[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.86[/C][C] 1.144[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 16.98[/C][C]-0.9828[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 15.74[/C][C]-0.7434[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.27[/C][C]-3.27[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.77[/C][C] 0.2346[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.61[/C][C]-1.605[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 14.98[/C][C] 1.022[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.6[/C][C] 0.396[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 16.42[/C][C]-0.417[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.8[/C][C]-1.801[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.3[/C][C]-0.3028[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 14.65[/C][C]-0.6451[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 15.8[/C][C]-0.8033[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.99[/C][C] 0.007773[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.18[/C][C] 0.8206[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 11.66[/C][C]-0.6642[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 16.13[/C][C] 1.872[/C][/ROW]
[ROW][C]81[/C][C] 11[/C][C] 13.93[/C][C]-2.928[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.79[/C][C] 0.208[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.83[/C][C]-1.829[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 18.24[/C][C] 0.7595[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 17.11[/C][C]-0.1062[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.28[/C][C]-1.277[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 15.81[/C][C]-2.808[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.8[/C][C] 1.205[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.78[/C][C]-1.781[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 15.98[/C][C] 3.024[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 14.43[/C][C]-0.4265[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.99[/C][C]-0.9888[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.32[/C][C] 0.6831[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.62[/C][C]-0.6199[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 14.65[/C][C]-2.655[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.44[/C][C] 0.561[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.76[/C][C] 2.243[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 14.12[/C][C] 0.8827[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 15.8[/C][C] 2.199[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 17.43[/C][C]-2.429[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.62[/C][C] 0.3801[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 13.9[/C][C] 2.098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.08	-0.07539
2	16	15.27	0.7343
3	17	15.79	1.211
4	16	15.13	0.8684
5	17	17.11	-0.1062
6	17	15.97	1.031
7	15	15.46	-0.4642
8	16	15.29	0.7052
9	14	13.89	0.1078
10	16	15.42	0.5794
11	17	14.96	2.044
12	16	14.49	1.506
13	16	15.61	0.3941
14	16	14.81	1.193
15	15	15.78	-0.7794
16	16	14.61	1.395
17	16	16.43	-0.4291
18	13	14.56	-1.564
19	15	16.97	-1.969
20	17	16.39	0.6069
21	13	13.29	-0.2884
22	17	17.01	-0.008941
23	14	13.94	0.05811
24	14	14.59	-0.592
25	18	15.77	2.235
26	17	16.97	0.03128
27	13	13.79	-0.7908
28	16	17.16	-1.156
29	15	15.82	-0.8151
30	15	15.16	-0.1574
31	15	15.56	-0.5557
32	13	15.8	-2.803
33	17	16.83	0.1707
34	11	14.57	-3.566
35	14	14.09	-0.09341
36	13	15.63	-2.628
37	17	15.15	1.848
38	16	15.34	0.6592
39	17	17.65	-0.6465
40	16	14.48	1.518
41	16	15.78	0.2187
42	16	14.67	1.331
43	15	15.9	-0.9048
44	12	13.24	-1.239
45	17	15.5	1.5
46	14	15.34	-1.341
47	14	15.77	-1.769
48	16	14.67	1.331
49	15	15.01	-0.006267
50	16	15.94	0.05918
51	14	14.65	-0.655
52	15	13.89	1.108
53	17	14.43	2.574
54	10	13.94	-3.942
55	17	15.93	1.067
56	20	16.29	3.708
57	17	16.75	0.2538
58	18	15.77	2.233
59	14	12.79	1.209
60	17	15.74	1.257
61	17	17.16	-0.1643
62	16	15.61	0.3941
63	18	16.28	1.716
64	18	16.86	1.144
65	16	16.98	-0.9828
66	15	15.74	-0.7434
67	13	16.27	-3.27
68	16	15.77	0.2346
69	12	13.61	-1.605
70	16	14.98	1.022
71	16	15.6	0.396
72	16	16.42	-0.417
73	14	15.8	-1.801
74	15	15.3	-0.3028
75	14	14.65	-0.6451
76	15	15.8	-0.8033
77	15	14.99	0.007773
78	16	15.18	0.8206
79	11	11.66	-0.6642
80	18	16.13	1.872
81	11	13.93	-2.928
82	18	17.79	0.208
83	15	16.83	-1.829
84	19	18.24	0.7595
85	17	17.11	-0.1062
86	14	15.28	-1.277
87	13	15.81	-2.808
88	17	15.8	1.205
89	14	15.78	-1.781
90	19	15.98	3.024
91	14	14.43	-0.4265
92	16	16.99	-0.9888
93	16	15.32	0.6831
94	15	15.62	-0.6199
95	12	14.65	-2.655
96	17	16.44	0.561
97	18	15.76	2.243
98	15	14.12	0.8827
99	18	15.8	2.199
100	15	17.43	-2.429
101	16	15.62	0.3801
102	16	13.9	2.098

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.06445	0.1289	0.9355
12	0.02019	0.04038	0.9798
13	0.009389	0.01878	0.9906
14	0.004536	0.009073	0.9955
15	0.007748	0.0155	0.9923
16	0.004115	0.008231	0.9959
17	0.001465	0.00293	0.9985
18	0.001613	0.003226	0.9984
19	0.02516	0.05033	0.9748
20	0.03425	0.0685	0.9658
21	0.02479	0.04959	0.9752
22	0.01585	0.0317	0.9841
23	0.01155	0.0231	0.9885
24	0.008174	0.01635	0.9918
25	0.03833	0.07666	0.9617
26	0.02447	0.04894	0.9755
27	0.02147	0.04293	0.9785
28	0.01894	0.03788	0.9811
29	0.01207	0.02414	0.9879
30	0.007696	0.01539	0.9923
31	0.00474	0.00948	0.9953
32	0.01601	0.03202	0.984
33	0.01147	0.02294	0.9885
34	0.07345	0.1469	0.9265
35	0.05384	0.1077	0.9462
36	0.08348	0.167	0.9165
37	0.09137	0.1827	0.9086
38	0.07602	0.152	0.924
39	0.05637	0.1127	0.9436
40	0.05169	0.1034	0.9483
41	0.03824	0.07649	0.9618
42	0.03692	0.07385	0.9631
43	0.02961	0.05923	0.9704
44	0.02724	0.05448	0.9728
45	0.02545	0.0509	0.9746
46	0.02437	0.04875	0.9756
47	0.02709	0.05418	0.9729
48	0.02597	0.05194	0.974
49	0.02146	0.04292	0.9785
50	0.01462	0.02923	0.9854
51	0.01105	0.02211	0.9889
52	0.008848	0.0177	0.9912
53	0.02908	0.05816	0.9709
54	0.1932	0.3864	0.8068
55	0.175	0.3501	0.825
56	0.4311	0.8621	0.5689
57	0.3742	0.7485	0.6258
58	0.4402	0.8804	0.5598
59	0.407	0.814	0.593
60	0.3898	0.7797	0.6102
61	0.332	0.6641	0.668
62	0.2869	0.5739	0.7131
63	0.307	0.6141	0.693
64	0.2791	0.5582	0.7209
65	0.2403	0.4806	0.7597
66	0.1997	0.3993	0.8003
67	0.369	0.738	0.631
68	0.3096	0.6191	0.6904
69	0.3098	0.6195	0.6902
70	0.334	0.6679	0.666
71	0.2928	0.5857	0.7072
72	0.2656	0.5312	0.7344
73	0.281	0.562	0.719
74	0.2259	0.4518	0.7741
75	0.1836	0.3671	0.8164
76	0.1554	0.3109	0.8446
77	0.1458	0.2915	0.8542
78	0.1451	0.2902	0.8549
79	0.1097	0.2194	0.8903
80	0.09164	0.1833	0.9084
81	0.2464	0.4928	0.7536
82	0.186	0.3721	0.814
83	0.1598	0.3196	0.8402
84	0.1144	0.2288	0.8856
85	0.07585	0.1517	0.9242
86	0.07106	0.1421	0.9289
87	0.4654	0.9309	0.5346
88	0.3566	0.7132	0.6434
89	0.485	0.9699	0.515
90	0.7895	0.4211	0.2105
91	0.6334	0.7332	0.3666

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.06445 &  0.1289 &  0.9355 \tabularnewline
12 &  0.02019 &  0.04038 &  0.9798 \tabularnewline
13 &  0.009389 &  0.01878 &  0.9906 \tabularnewline
14 &  0.004536 &  0.009073 &  0.9955 \tabularnewline
15 &  0.007748 &  0.0155 &  0.9923 \tabularnewline
16 &  0.004115 &  0.008231 &  0.9959 \tabularnewline
17 &  0.001465 &  0.00293 &  0.9985 \tabularnewline
18 &  0.001613 &  0.003226 &  0.9984 \tabularnewline
19 &  0.02516 &  0.05033 &  0.9748 \tabularnewline
20 &  0.03425 &  0.0685 &  0.9658 \tabularnewline
21 &  0.02479 &  0.04959 &  0.9752 \tabularnewline
22 &  0.01585 &  0.0317 &  0.9841 \tabularnewline
23 &  0.01155 &  0.0231 &  0.9885 \tabularnewline
24 &  0.008174 &  0.01635 &  0.9918 \tabularnewline
25 &  0.03833 &  0.07666 &  0.9617 \tabularnewline
26 &  0.02447 &  0.04894 &  0.9755 \tabularnewline
27 &  0.02147 &  0.04293 &  0.9785 \tabularnewline
28 &  0.01894 &  0.03788 &  0.9811 \tabularnewline
29 &  0.01207 &  0.02414 &  0.9879 \tabularnewline
30 &  0.007696 &  0.01539 &  0.9923 \tabularnewline
31 &  0.00474 &  0.00948 &  0.9953 \tabularnewline
32 &  0.01601 &  0.03202 &  0.984 \tabularnewline
33 &  0.01147 &  0.02294 &  0.9885 \tabularnewline
34 &  0.07345 &  0.1469 &  0.9265 \tabularnewline
35 &  0.05384 &  0.1077 &  0.9462 \tabularnewline
36 &  0.08348 &  0.167 &  0.9165 \tabularnewline
37 &  0.09137 &  0.1827 &  0.9086 \tabularnewline
38 &  0.07602 &  0.152 &  0.924 \tabularnewline
39 &  0.05637 &  0.1127 &  0.9436 \tabularnewline
40 &  0.05169 &  0.1034 &  0.9483 \tabularnewline
41 &  0.03824 &  0.07649 &  0.9618 \tabularnewline
42 &  0.03692 &  0.07385 &  0.9631 \tabularnewline
43 &  0.02961 &  0.05923 &  0.9704 \tabularnewline
44 &  0.02724 &  0.05448 &  0.9728 \tabularnewline
45 &  0.02545 &  0.0509 &  0.9746 \tabularnewline
46 &  0.02437 &  0.04875 &  0.9756 \tabularnewline
47 &  0.02709 &  0.05418 &  0.9729 \tabularnewline
48 &  0.02597 &  0.05194 &  0.974 \tabularnewline
49 &  0.02146 &  0.04292 &  0.9785 \tabularnewline
50 &  0.01462 &  0.02923 &  0.9854 \tabularnewline
51 &  0.01105 &  0.02211 &  0.9889 \tabularnewline
52 &  0.008848 &  0.0177 &  0.9912 \tabularnewline
53 &  0.02908 &  0.05816 &  0.9709 \tabularnewline
54 &  0.1932 &  0.3864 &  0.8068 \tabularnewline
55 &  0.175 &  0.3501 &  0.825 \tabularnewline
56 &  0.4311 &  0.8621 &  0.5689 \tabularnewline
57 &  0.3742 &  0.7485 &  0.6258 \tabularnewline
58 &  0.4402 &  0.8804 &  0.5598 \tabularnewline
59 &  0.407 &  0.814 &  0.593 \tabularnewline
60 &  0.3898 &  0.7797 &  0.6102 \tabularnewline
61 &  0.332 &  0.6641 &  0.668 \tabularnewline
62 &  0.2869 &  0.5739 &  0.7131 \tabularnewline
63 &  0.307 &  0.6141 &  0.693 \tabularnewline
64 &  0.2791 &  0.5582 &  0.7209 \tabularnewline
65 &  0.2403 &  0.4806 &  0.7597 \tabularnewline
66 &  0.1997 &  0.3993 &  0.8003 \tabularnewline
67 &  0.369 &  0.738 &  0.631 \tabularnewline
68 &  0.3096 &  0.6191 &  0.6904 \tabularnewline
69 &  0.3098 &  0.6195 &  0.6902 \tabularnewline
70 &  0.334 &  0.6679 &  0.666 \tabularnewline
71 &  0.2928 &  0.5857 &  0.7072 \tabularnewline
72 &  0.2656 &  0.5312 &  0.7344 \tabularnewline
73 &  0.281 &  0.562 &  0.719 \tabularnewline
74 &  0.2259 &  0.4518 &  0.7741 \tabularnewline
75 &  0.1836 &  0.3671 &  0.8164 \tabularnewline
76 &  0.1554 &  0.3109 &  0.8446 \tabularnewline
77 &  0.1458 &  0.2915 &  0.8542 \tabularnewline
78 &  0.1451 &  0.2902 &  0.8549 \tabularnewline
79 &  0.1097 &  0.2194 &  0.8903 \tabularnewline
80 &  0.09164 &  0.1833 &  0.9084 \tabularnewline
81 &  0.2464 &  0.4928 &  0.7536 \tabularnewline
82 &  0.186 &  0.3721 &  0.814 \tabularnewline
83 &  0.1598 &  0.3196 &  0.8402 \tabularnewline
84 &  0.1144 &  0.2288 &  0.8856 \tabularnewline
85 &  0.07585 &  0.1517 &  0.9242 \tabularnewline
86 &  0.07106 &  0.1421 &  0.9289 \tabularnewline
87 &  0.4654 &  0.9309 &  0.5346 \tabularnewline
88 &  0.3566 &  0.7132 &  0.6434 \tabularnewline
89 &  0.485 &  0.9699 &  0.515 \tabularnewline
90 &  0.7895 &  0.4211 &  0.2105 \tabularnewline
91 &  0.6334 &  0.7332 &  0.3666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C] 0.06445[/C][C] 0.1289[/C][C] 0.9355[/C][/ROW]
[ROW][C]12[/C][C] 0.02019[/C][C] 0.04038[/C][C] 0.9798[/C][/ROW]
[ROW][C]13[/C][C] 0.009389[/C][C] 0.01878[/C][C] 0.9906[/C][/ROW]
[ROW][C]14[/C][C] 0.004536[/C][C] 0.009073[/C][C] 0.9955[/C][/ROW]
[ROW][C]15[/C][C] 0.007748[/C][C] 0.0155[/C][C] 0.9923[/C][/ROW]
[ROW][C]16[/C][C] 0.004115[/C][C] 0.008231[/C][C] 0.9959[/C][/ROW]
[ROW][C]17[/C][C] 0.001465[/C][C] 0.00293[/C][C] 0.9985[/C][/ROW]
[ROW][C]18[/C][C] 0.001613[/C][C] 0.003226[/C][C] 0.9984[/C][/ROW]
[ROW][C]19[/C][C] 0.02516[/C][C] 0.05033[/C][C] 0.9748[/C][/ROW]
[ROW][C]20[/C][C] 0.03425[/C][C] 0.0685[/C][C] 0.9658[/C][/ROW]
[ROW][C]21[/C][C] 0.02479[/C][C] 0.04959[/C][C] 0.9752[/C][/ROW]
[ROW][C]22[/C][C] 0.01585[/C][C] 0.0317[/C][C] 0.9841[/C][/ROW]
[ROW][C]23[/C][C] 0.01155[/C][C] 0.0231[/C][C] 0.9885[/C][/ROW]
[ROW][C]24[/C][C] 0.008174[/C][C] 0.01635[/C][C] 0.9918[/C][/ROW]
[ROW][C]25[/C][C] 0.03833[/C][C] 0.07666[/C][C] 0.9617[/C][/ROW]
[ROW][C]26[/C][C] 0.02447[/C][C] 0.04894[/C][C] 0.9755[/C][/ROW]
[ROW][C]27[/C][C] 0.02147[/C][C] 0.04293[/C][C] 0.9785[/C][/ROW]
[ROW][C]28[/C][C] 0.01894[/C][C] 0.03788[/C][C] 0.9811[/C][/ROW]
[ROW][C]29[/C][C] 0.01207[/C][C] 0.02414[/C][C] 0.9879[/C][/ROW]
[ROW][C]30[/C][C] 0.007696[/C][C] 0.01539[/C][C] 0.9923[/C][/ROW]
[ROW][C]31[/C][C] 0.00474[/C][C] 0.00948[/C][C] 0.9953[/C][/ROW]
[ROW][C]32[/C][C] 0.01601[/C][C] 0.03202[/C][C] 0.984[/C][/ROW]
[ROW][C]33[/C][C] 0.01147[/C][C] 0.02294[/C][C] 0.9885[/C][/ROW]
[ROW][C]34[/C][C] 0.07345[/C][C] 0.1469[/C][C] 0.9265[/C][/ROW]
[ROW][C]35[/C][C] 0.05384[/C][C] 0.1077[/C][C] 0.9462[/C][/ROW]
[ROW][C]36[/C][C] 0.08348[/C][C] 0.167[/C][C] 0.9165[/C][/ROW]
[ROW][C]37[/C][C] 0.09137[/C][C] 0.1827[/C][C] 0.9086[/C][/ROW]
[ROW][C]38[/C][C] 0.07602[/C][C] 0.152[/C][C] 0.924[/C][/ROW]
[ROW][C]39[/C][C] 0.05637[/C][C] 0.1127[/C][C] 0.9436[/C][/ROW]
[ROW][C]40[/C][C] 0.05169[/C][C] 0.1034[/C][C] 0.9483[/C][/ROW]
[ROW][C]41[/C][C] 0.03824[/C][C] 0.07649[/C][C] 0.9618[/C][/ROW]
[ROW][C]42[/C][C] 0.03692[/C][C] 0.07385[/C][C] 0.9631[/C][/ROW]
[ROW][C]43[/C][C] 0.02961[/C][C] 0.05923[/C][C] 0.9704[/C][/ROW]
[ROW][C]44[/C][C] 0.02724[/C][C] 0.05448[/C][C] 0.9728[/C][/ROW]
[ROW][C]45[/C][C] 0.02545[/C][C] 0.0509[/C][C] 0.9746[/C][/ROW]
[ROW][C]46[/C][C] 0.02437[/C][C] 0.04875[/C][C] 0.9756[/C][/ROW]
[ROW][C]47[/C][C] 0.02709[/C][C] 0.05418[/C][C] 0.9729[/C][/ROW]
[ROW][C]48[/C][C] 0.02597[/C][C] 0.05194[/C][C] 0.974[/C][/ROW]
[ROW][C]49[/C][C] 0.02146[/C][C] 0.04292[/C][C] 0.9785[/C][/ROW]
[ROW][C]50[/C][C] 0.01462[/C][C] 0.02923[/C][C] 0.9854[/C][/ROW]
[ROW][C]51[/C][C] 0.01105[/C][C] 0.02211[/C][C] 0.9889[/C][/ROW]
[ROW][C]52[/C][C] 0.008848[/C][C] 0.0177[/C][C] 0.9912[/C][/ROW]
[ROW][C]53[/C][C] 0.02908[/C][C] 0.05816[/C][C] 0.9709[/C][/ROW]
[ROW][C]54[/C][C] 0.1932[/C][C] 0.3864[/C][C] 0.8068[/C][/ROW]
[ROW][C]55[/C][C] 0.175[/C][C] 0.3501[/C][C] 0.825[/C][/ROW]
[ROW][C]56[/C][C] 0.4311[/C][C] 0.8621[/C][C] 0.5689[/C][/ROW]
[ROW][C]57[/C][C] 0.3742[/C][C] 0.7485[/C][C] 0.6258[/C][/ROW]
[ROW][C]58[/C][C] 0.4402[/C][C] 0.8804[/C][C] 0.5598[/C][/ROW]
[ROW][C]59[/C][C] 0.407[/C][C] 0.814[/C][C] 0.593[/C][/ROW]
[ROW][C]60[/C][C] 0.3898[/C][C] 0.7797[/C][C] 0.6102[/C][/ROW]
[ROW][C]61[/C][C] 0.332[/C][C] 0.6641[/C][C] 0.668[/C][/ROW]
[ROW][C]62[/C][C] 0.2869[/C][C] 0.5739[/C][C] 0.7131[/C][/ROW]
[ROW][C]63[/C][C] 0.307[/C][C] 0.6141[/C][C] 0.693[/C][/ROW]
[ROW][C]64[/C][C] 0.2791[/C][C] 0.5582[/C][C] 0.7209[/C][/ROW]
[ROW][C]65[/C][C] 0.2403[/C][C] 0.4806[/C][C] 0.7597[/C][/ROW]
[ROW][C]66[/C][C] 0.1997[/C][C] 0.3993[/C][C] 0.8003[/C][/ROW]
[ROW][C]67[/C][C] 0.369[/C][C] 0.738[/C][C] 0.631[/C][/ROW]
[ROW][C]68[/C][C] 0.3096[/C][C] 0.6191[/C][C] 0.6904[/C][/ROW]
[ROW][C]69[/C][C] 0.3098[/C][C] 0.6195[/C][C] 0.6902[/C][/ROW]
[ROW][C]70[/C][C] 0.334[/C][C] 0.6679[/C][C] 0.666[/C][/ROW]
[ROW][C]71[/C][C] 0.2928[/C][C] 0.5857[/C][C] 0.7072[/C][/ROW]
[ROW][C]72[/C][C] 0.2656[/C][C] 0.5312[/C][C] 0.7344[/C][/ROW]
[ROW][C]73[/C][C] 0.281[/C][C] 0.562[/C][C] 0.719[/C][/ROW]
[ROW][C]74[/C][C] 0.2259[/C][C] 0.4518[/C][C] 0.7741[/C][/ROW]
[ROW][C]75[/C][C] 0.1836[/C][C] 0.3671[/C][C] 0.8164[/C][/ROW]
[ROW][C]76[/C][C] 0.1554[/C][C] 0.3109[/C][C] 0.8446[/C][/ROW]
[ROW][C]77[/C][C] 0.1458[/C][C] 0.2915[/C][C] 0.8542[/C][/ROW]
[ROW][C]78[/C][C] 0.1451[/C][C] 0.2902[/C][C] 0.8549[/C][/ROW]
[ROW][C]79[/C][C] 0.1097[/C][C] 0.2194[/C][C] 0.8903[/C][/ROW]
[ROW][C]80[/C][C] 0.09164[/C][C] 0.1833[/C][C] 0.9084[/C][/ROW]
[ROW][C]81[/C][C] 0.2464[/C][C] 0.4928[/C][C] 0.7536[/C][/ROW]
[ROW][C]82[/C][C] 0.186[/C][C] 0.3721[/C][C] 0.814[/C][/ROW]
[ROW][C]83[/C][C] 0.1598[/C][C] 0.3196[/C][C] 0.8402[/C][/ROW]
[ROW][C]84[/C][C] 0.1144[/C][C] 0.2288[/C][C] 0.8856[/C][/ROW]
[ROW][C]85[/C][C] 0.07585[/C][C] 0.1517[/C][C] 0.9242[/C][/ROW]
[ROW][C]86[/C][C] 0.07106[/C][C] 0.1421[/C][C] 0.9289[/C][/ROW]
[ROW][C]87[/C][C] 0.4654[/C][C] 0.9309[/C][C] 0.5346[/C][/ROW]
[ROW][C]88[/C][C] 0.3566[/C][C] 0.7132[/C][C] 0.6434[/C][/ROW]
[ROW][C]89[/C][C] 0.485[/C][C] 0.9699[/C][C] 0.515[/C][/ROW]
[ROW][C]90[/C][C] 0.7895[/C][C] 0.4211[/C][C] 0.2105[/C][/ROW]
[ROW][C]91[/C][C] 0.6334[/C][C] 0.7332[/C][C] 0.3666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.06445	0.1289	0.9355
12	0.02019	0.04038	0.9798
13	0.009389	0.01878	0.9906
14	0.004536	0.009073	0.9955
15	0.007748	0.0155	0.9923
16	0.004115	0.008231	0.9959
17	0.001465	0.00293	0.9985
18	0.001613	0.003226	0.9984
19	0.02516	0.05033	0.9748
20	0.03425	0.0685	0.9658
21	0.02479	0.04959	0.9752
22	0.01585	0.0317	0.9841
23	0.01155	0.0231	0.9885
24	0.008174	0.01635	0.9918
25	0.03833	0.07666	0.9617
26	0.02447	0.04894	0.9755
27	0.02147	0.04293	0.9785
28	0.01894	0.03788	0.9811
29	0.01207	0.02414	0.9879
30	0.007696	0.01539	0.9923
31	0.00474	0.00948	0.9953
32	0.01601	0.03202	0.984
33	0.01147	0.02294	0.9885
34	0.07345	0.1469	0.9265
35	0.05384	0.1077	0.9462
36	0.08348	0.167	0.9165
37	0.09137	0.1827	0.9086
38	0.07602	0.152	0.924
39	0.05637	0.1127	0.9436
40	0.05169	0.1034	0.9483
41	0.03824	0.07649	0.9618
42	0.03692	0.07385	0.9631
43	0.02961	0.05923	0.9704
44	0.02724	0.05448	0.9728
45	0.02545	0.0509	0.9746
46	0.02437	0.04875	0.9756
47	0.02709	0.05418	0.9729
48	0.02597	0.05194	0.974
49	0.02146	0.04292	0.9785
50	0.01462	0.02923	0.9854
51	0.01105	0.02211	0.9889
52	0.008848	0.0177	0.9912
53	0.02908	0.05816	0.9709
54	0.1932	0.3864	0.8068
55	0.175	0.3501	0.825
56	0.4311	0.8621	0.5689
57	0.3742	0.7485	0.6258
58	0.4402	0.8804	0.5598
59	0.407	0.814	0.593
60	0.3898	0.7797	0.6102
61	0.332	0.6641	0.668
62	0.2869	0.5739	0.7131
63	0.307	0.6141	0.693
64	0.2791	0.5582	0.7209
65	0.2403	0.4806	0.7597
66	0.1997	0.3993	0.8003
67	0.369	0.738	0.631
68	0.3096	0.6191	0.6904
69	0.3098	0.6195	0.6902
70	0.334	0.6679	0.666
71	0.2928	0.5857	0.7072
72	0.2656	0.5312	0.7344
73	0.281	0.562	0.719
74	0.2259	0.4518	0.7741
75	0.1836	0.3671	0.8164
76	0.1554	0.3109	0.8446
77	0.1458	0.2915	0.8542
78	0.1451	0.2902	0.8549
79	0.1097	0.2194	0.8903
80	0.09164	0.1833	0.9084
81	0.2464	0.4928	0.7536
82	0.186	0.3721	0.814
83	0.1598	0.3196	0.8402
84	0.1144	0.2288	0.8856
85	0.07585	0.1517	0.9242
86	0.07106	0.1421	0.9289
87	0.4654	0.9309	0.5346
88	0.3566	0.7132	0.6434
89	0.485	0.9699	0.515
90	0.7895	0.4211	0.2105
91	0.6334	0.7332	0.3666

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	5	0.06173	NOK
5% type I error level	24	0.296296	NOK
10% type I error level	35	0.432099	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 5 &  0.06173 & NOK \tabularnewline
5% type I error level & 24 & 0.296296 & NOK \tabularnewline
10% type I error level & 35 & 0.432099 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]5[/C][C] 0.06173[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.296296[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.432099[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	5	0.06173	NOK
5% type I error level	24	0.296296	NOK
10% type I error level	35	0.432099	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.74779, df1 = 2, df2 = 92, p-value = 0.4763
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.97753, df1 = 14, df2 = 80, p-value = 0.4835
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1855, df1 = 2, df2 = 92, p-value = 0.3102

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.74779, df1 = 2, df2 = 92, p-value = 0.4763
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.97753, df1 = 14, df2 = 80, p-value = 0.4835
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1855, df1 = 2, df2 = 92, p-value = 0.3102
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.74779, df1 = 2, df2 = 92, p-value = 0.4763
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.97753, df1 = 14, df2 = 80, p-value = 0.4835
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1855, df1 = 2, df2 = 92, p-value = 0.3102
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=7

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.74779, df1 = 2, df2 = 92, p-value = 0.4763
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.97753, df1 = 14, df2 = 80, p-value = 0.4835
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1855, df1 = 2, df2 = 92, p-value = 0.3102

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 G 1.076742 1.173460 1.060510 1.076882 1.041520 1.074715 1.067413

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6        G 
1.076742 1.173460 1.060510 1.076882 1.041520 1.074715 1.067413 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297628&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6        G 
1.076742 1.173460 1.060510 1.076882 1.041520 1.074715 1.067413 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297628&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297628&T=8

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 G 1.076742 1.173460 1.060510 1.076882 1.041520 1.074715 1.067413

Figure 1

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Parameters (Session):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code