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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 02 Dec 2016 09:20:55 +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/02/t1480666954i0bxbw5k1khq3d9.htm/, Retrieved Fri, 01 Nov 2024 03:42:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297549, Retrieved Fri, 01 Nov 2024 03:42:53 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

149

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

-       [Multiple Regression] [Voorbeeld Les Mul...] [2016-12-02 08:20:55] [bde5266f17215258f6d7c4cd7e531432] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297549&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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	6 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVD[t] = + 6.04468 + 0.647079SK1[t] + 1.20525SK2[t] -0.0162453SK3[t] + 0.475205SK4[t] + 0.152156SK5[t] -0.0439898SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVD[t] =  +  6.04468 +  0.647079SK1[t] +  1.20525SK2[t] -0.0162453SK3[t] +  0.475205SK4[t] +  0.152156SK5[t] -0.0439898SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVD[t] =  +  6.04468 +  0.647079SK1[t] +  1.20525SK2[t] -0.0162453SK3[t] +  0.475205SK4[t] +  0.152156SK5[t] -0.0439898SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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
TVD[t] = + 6.04468 + 0.647079SK1[t] + 1.20525SK2[t] -0.0162453SK3[t] + 0.475205SK4[t] + 0.152156SK5[t] -0.0439898SK6[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)	+6.045	2.139	+2.8250e+00	0.005756	0.002878
SK1	+0.6471	0.2142	+3.0210e+00	0.003239	0.00162
SK2	+1.205	0.2379	+5.0660e+00	1.993e-06	9.965e-07
SK3	-0.01624	0.2035	-7.9830e-02	0.9365	0.4683
SK4	+0.4752	0.286	+1.6620e+00	0.09985	0.04992
SK5	+0.1522	0.2406	+6.3250e-01	0.5286	0.2643
SK6	-0.04399	0.2569	-1.7120e-01	0.8644	0.4322

\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) & +6.045 &  2.139 & +2.8250e+00 &  0.005756 &  0.002878 \tabularnewline
SK1 & +0.6471 &  0.2142 & +3.0210e+00 &  0.003239 &  0.00162 \tabularnewline
SK2 & +1.205 &  0.2379 & +5.0660e+00 &  1.993e-06 &  9.965e-07 \tabularnewline
SK3 & -0.01624 &  0.2035 & -7.9830e-02 &  0.9365 &  0.4683 \tabularnewline
SK4 & +0.4752 &  0.286 & +1.6620e+00 &  0.09985 &  0.04992 \tabularnewline
SK5 & +0.1522 &  0.2406 & +6.3250e-01 &  0.5286 &  0.2643 \tabularnewline
SK6 & -0.04399 &  0.2569 & -1.7120e-01 &  0.8644 &  0.4322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&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]+6.045[/C][C] 2.139[/C][C]+2.8250e+00[/C][C] 0.005756[/C][C] 0.002878[/C][/ROW]
[ROW][C]SK1[/C][C]+0.6471[/C][C] 0.2142[/C][C]+3.0210e+00[/C][C] 0.003239[/C][C] 0.00162[/C][/ROW]
[ROW][C]SK2[/C][C]+1.205[/C][C] 0.2379[/C][C]+5.0660e+00[/C][C] 1.993e-06[/C][C] 9.965e-07[/C][/ROW]
[ROW][C]SK3[/C][C]-0.01624[/C][C] 0.2035[/C][C]-7.9830e-02[/C][C] 0.9365[/C][C] 0.4683[/C][/ROW]
[ROW][C]SK4[/C][C]+0.4752[/C][C] 0.286[/C][C]+1.6620e+00[/C][C] 0.09985[/C][C] 0.04992[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1522[/C][C] 0.2406[/C][C]+6.3250e-01[/C][C] 0.5286[/C][C] 0.2643[/C][/ROW]
[ROW][C]SK6[/C][C]-0.04399[/C][C] 0.2569[/C][C]-1.7120e-01[/C][C] 0.8644[/C][C] 0.4322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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)	+6.045	2.139	+2.8250e+00	0.005756	0.002878
SK1	+0.6471	0.2142	+3.0210e+00	0.003239	0.00162
SK2	+1.205	0.2379	+5.0660e+00	1.993e-06	9.965e-07
SK3	-0.01624	0.2035	-7.9830e-02	0.9365	0.4683
SK4	+0.4752	0.286	+1.6620e+00	0.09985	0.04992
SK5	+0.1522	0.2406	+6.3250e-01	0.5286	0.2643
SK6	-0.04399	0.2569	-1.7120e-01	0.8644	0.4322

Multiple Linear Regression - Regression Statistics
Multiple R	0.6145
R-squared	0.3776
Adjusted R-squared	0.3383
F-TEST (value)	9.605
F-TEST (DF numerator)	6
F-TEST (DF denominator)	95
p-value	3.034e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.526
Sum Squared Residuals	221.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6145 \tabularnewline
R-squared &  0.3776 \tabularnewline
Adjusted R-squared &  0.3383 \tabularnewline
F-TEST (value) &  9.605 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value &  3.034e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.526 \tabularnewline
Sum Squared Residuals &  221.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6145[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3776[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3383[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 9.605[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/C][/ROW]
[ROW][C]p-value[/C][C] 3.034e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.526[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 221.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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	0.6145
R-squared	0.3776
Adjusted R-squared	0.3383
F-TEST (value)	9.605
F-TEST (DF numerator)	6
F-TEST (DF denominator)	95
p-value	3.034e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.526
Sum Squared Residuals	221.2

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.99	0.01105
2	16	15.33	0.6673
3	17	15.86	1.142
4	16	15.18	0.8164
5	17	17.02	-0.01966
6	17	15.91	1.093
7	15	15.38	-0.3832
8	16	15.2	0.7967
9	14	13.81	0.1901
10	16	15.36	0.6421
11	17	15.02	1.985
12	16	14.54	1.46
13	16	15.66	0.3377
14	16	14.71	1.292
15	15	15.72	-0.7225
16	16	14.52	1.48
17	16	16.37	-0.3696
18	13	14.5	-1.501
19	15	17.02	-2.02
20	17	16.31	0.6907
21	13	13.36	-0.359
22	17	16.96	0.03977
23	14	13.98	0.01819
24	14	14.53	-0.5335
25	18	15.71	2.294
26	17	17.02	-0.01966
27	13	13.87	-0.8702
28	16	17.19	-1.192
29	15	15.88	-0.8781
30	15	15.2	-0.2033
31	15	15.64	-0.6371
32	13	15.87	-2.875
33	17	16.91	0.0885
34	11	14.61	-3.609
35	14	14	0.001948
36	13	15.71	-2.706
37	17	15.12	1.881
38	16	15.4	0.6006
39	17	17.7	-0.6992
40	16	14.54	1.464
41	16	15.83	0.1693
42	16	14.77	1.229
43	15	15.81	-0.8144
44	12	13.31	-1.312
45	17	15.44	1.557
46	14	15.4	-1.399
47	14	15.68	-1.682
48	16	14.71	1.292
49	15	15.09	-0.09166
50	16	15.87	0.1253
51	14	14.69	-0.6921
52	15	13.81	1.19
53	17	14.5	2.499
54	10	13.89	-3.886
55	17	15.85	1.153
56	20	16.37	3.63
57	17	16.82	0.183
58	18	15.81	2.186
59	14	12.85	1.147
60	17	15.66	1.338
61	17	17.12	-0.1239
62	16	15.66	0.3377
63	18	16.34	1.658
64	18	16.78	1.215
65	16	17.04	-1.036
66	15	15.66	-0.6623
67	13	16.33	-3.326
68	16	15.71	0.2938
69	12	13.5	-1.503
70	16	15.06	0.9408
71	16	15.55	0.4459
72	16	16.46	-0.4615
73	14	15.77	-1.766
74	15	15.23	-0.231
75	14	14.56	-0.5562
76	15	15.87	-0.8747
77	15	15.08	-0.07542
78	16	15.25	0.7527
79	11	11.71	-0.7145
80	18	16.05	1.953
81	11	13.87	-2.87
82	18	17.73	0.273
83	15	16.91	-1.911
84	19	18.19	0.8141
85	17	17.02	-0.01966
86	14	15.21	-1.211
87	13	15.76	-2.755
88	17	15.85	1.153
89	14	15.83	-1.831
90	19	16.03	2.97
91	14	14.5	-0.501
92	16	16.96	-0.9555
93	16	15.25	0.7527
94	15	15.68	-0.6785
95	12	14.69	-2.692
96	17	16.51	0.4945
97	18	15.68	2.321
98	15	14.15	0.8498
99	18	15.77	2.234
100	15	17.39	-2.387
101	16	15.68	0.3215
102	16	13.85	2.15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  12.99 &  0.01105 \tabularnewline
2 &  16 &  15.33 &  0.6673 \tabularnewline
3 &  17 &  15.86 &  1.142 \tabularnewline
4 &  16 &  15.18 &  0.8164 \tabularnewline
5 &  17 &  17.02 & -0.01966 \tabularnewline
6 &  17 &  15.91 &  1.093 \tabularnewline
7 &  15 &  15.38 & -0.3832 \tabularnewline
8 &  16 &  15.2 &  0.7967 \tabularnewline
9 &  14 &  13.81 &  0.1901 \tabularnewline
10 &  16 &  15.36 &  0.6421 \tabularnewline
11 &  17 &  15.02 &  1.985 \tabularnewline
12 &  16 &  14.54 &  1.46 \tabularnewline
13 &  16 &  15.66 &  0.3377 \tabularnewline
14 &  16 &  14.71 &  1.292 \tabularnewline
15 &  15 &  15.72 & -0.7225 \tabularnewline
16 &  16 &  14.52 &  1.48 \tabularnewline
17 &  16 &  16.37 & -0.3696 \tabularnewline
18 &  13 &  14.5 & -1.501 \tabularnewline
19 &  15 &  17.02 & -2.02 \tabularnewline
20 &  17 &  16.31 &  0.6907 \tabularnewline
21 &  13 &  13.36 & -0.359 \tabularnewline
22 &  17 &  16.96 &  0.03977 \tabularnewline
23 &  14 &  13.98 &  0.01819 \tabularnewline
24 &  14 &  14.53 & -0.5335 \tabularnewline
25 &  18 &  15.71 &  2.294 \tabularnewline
26 &  17 &  17.02 & -0.01966 \tabularnewline
27 &  13 &  13.87 & -0.8702 \tabularnewline
28 &  16 &  17.19 & -1.192 \tabularnewline
29 &  15 &  15.88 & -0.8781 \tabularnewline
30 &  15 &  15.2 & -0.2033 \tabularnewline
31 &  15 &  15.64 & -0.6371 \tabularnewline
32 &  13 &  15.87 & -2.875 \tabularnewline
33 &  17 &  16.91 &  0.0885 \tabularnewline
34 &  11 &  14.61 & -3.609 \tabularnewline
35 &  14 &  14 &  0.001948 \tabularnewline
36 &  13 &  15.71 & -2.706 \tabularnewline
37 &  17 &  15.12 &  1.881 \tabularnewline
38 &  16 &  15.4 &  0.6006 \tabularnewline
39 &  17 &  17.7 & -0.6992 \tabularnewline
40 &  16 &  14.54 &  1.464 \tabularnewline
41 &  16 &  15.83 &  0.1693 \tabularnewline
42 &  16 &  14.77 &  1.229 \tabularnewline
43 &  15 &  15.81 & -0.8144 \tabularnewline
44 &  12 &  13.31 & -1.312 \tabularnewline
45 &  17 &  15.44 &  1.557 \tabularnewline
46 &  14 &  15.4 & -1.399 \tabularnewline
47 &  14 &  15.68 & -1.682 \tabularnewline
48 &  16 &  14.71 &  1.292 \tabularnewline
49 &  15 &  15.09 & -0.09166 \tabularnewline
50 &  16 &  15.87 &  0.1253 \tabularnewline
51 &  14 &  14.69 & -0.6921 \tabularnewline
52 &  15 &  13.81 &  1.19 \tabularnewline
53 &  17 &  14.5 &  2.499 \tabularnewline
54 &  10 &  13.89 & -3.886 \tabularnewline
55 &  17 &  15.85 &  1.153 \tabularnewline
56 &  20 &  16.37 &  3.63 \tabularnewline
57 &  17 &  16.82 &  0.183 \tabularnewline
58 &  18 &  15.81 &  2.186 \tabularnewline
59 &  14 &  12.85 &  1.147 \tabularnewline
60 &  17 &  15.66 &  1.338 \tabularnewline
61 &  17 &  17.12 & -0.1239 \tabularnewline
62 &  16 &  15.66 &  0.3377 \tabularnewline
63 &  18 &  16.34 &  1.658 \tabularnewline
64 &  18 &  16.78 &  1.215 \tabularnewline
65 &  16 &  17.04 & -1.036 \tabularnewline
66 &  15 &  15.66 & -0.6623 \tabularnewline
67 &  13 &  16.33 & -3.326 \tabularnewline
68 &  16 &  15.71 &  0.2938 \tabularnewline
69 &  12 &  13.5 & -1.503 \tabularnewline
70 &  16 &  15.06 &  0.9408 \tabularnewline
71 &  16 &  15.55 &  0.4459 \tabularnewline
72 &  16 &  16.46 & -0.4615 \tabularnewline
73 &  14 &  15.77 & -1.766 \tabularnewline
74 &  15 &  15.23 & -0.231 \tabularnewline
75 &  14 &  14.56 & -0.5562 \tabularnewline
76 &  15 &  15.87 & -0.8747 \tabularnewline
77 &  15 &  15.08 & -0.07542 \tabularnewline
78 &  16 &  15.25 &  0.7527 \tabularnewline
79 &  11 &  11.71 & -0.7145 \tabularnewline
80 &  18 &  16.05 &  1.953 \tabularnewline
81 &  11 &  13.87 & -2.87 \tabularnewline
82 &  18 &  17.73 &  0.273 \tabularnewline
83 &  15 &  16.91 & -1.911 \tabularnewline
84 &  19 &  18.19 &  0.8141 \tabularnewline
85 &  17 &  17.02 & -0.01966 \tabularnewline
86 &  14 &  15.21 & -1.211 \tabularnewline
87 &  13 &  15.76 & -2.755 \tabularnewline
88 &  17 &  15.85 &  1.153 \tabularnewline
89 &  14 &  15.83 & -1.831 \tabularnewline
90 &  19 &  16.03 &  2.97 \tabularnewline
91 &  14 &  14.5 & -0.501 \tabularnewline
92 &  16 &  16.96 & -0.9555 \tabularnewline
93 &  16 &  15.25 &  0.7527 \tabularnewline
94 &  15 &  15.68 & -0.6785 \tabularnewline
95 &  12 &  14.69 & -2.692 \tabularnewline
96 &  17 &  16.51 &  0.4945 \tabularnewline
97 &  18 &  15.68 &  2.321 \tabularnewline
98 &  15 &  14.15 &  0.8498 \tabularnewline
99 &  18 &  15.77 &  2.234 \tabularnewline
100 &  15 &  17.39 & -2.387 \tabularnewline
101 &  16 &  15.68 &  0.3215 \tabularnewline
102 &  16 &  13.85 &  2.15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&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] 12.99[/C][C] 0.01105[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.33[/C][C] 0.6673[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.86[/C][C] 1.142[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.18[/C][C] 0.8164[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.02[/C][C]-0.01966[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.91[/C][C] 1.093[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.38[/C][C]-0.3832[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.2[/C][C] 0.7967[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 13.81[/C][C] 0.1901[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.36[/C][C] 0.6421[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.02[/C][C] 1.985[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.54[/C][C] 1.46[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.66[/C][C] 0.3377[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.71[/C][C] 1.292[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.72[/C][C]-0.7225[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.52[/C][C] 1.48[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.37[/C][C]-0.3696[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.5[/C][C]-1.501[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 17.02[/C][C]-2.02[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.31[/C][C] 0.6907[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.36[/C][C]-0.359[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 16.96[/C][C] 0.03977[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 13.98[/C][C] 0.01819[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.53[/C][C]-0.5335[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.71[/C][C] 2.294[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 17.02[/C][C]-0.01966[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.87[/C][C]-0.8702[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.19[/C][C]-1.192[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.88[/C][C]-0.8781[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.2[/C][C]-0.2033[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.64[/C][C]-0.6371[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.87[/C][C]-2.875[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 16.91[/C][C] 0.0885[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.61[/C][C]-3.609[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 14[/C][C] 0.001948[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.71[/C][C]-2.706[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.12[/C][C] 1.881[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.4[/C][C] 0.6006[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.7[/C][C]-0.6992[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.54[/C][C] 1.464[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.83[/C][C] 0.1693[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 14.77[/C][C] 1.229[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.81[/C][C]-0.8144[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.31[/C][C]-1.312[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.44[/C][C] 1.557[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.4[/C][C]-1.399[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.68[/C][C]-1.682[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.71[/C][C] 1.292[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.09[/C][C]-0.09166[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.87[/C][C] 0.1253[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.69[/C][C]-0.6921[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.81[/C][C] 1.19[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 14.5[/C][C] 2.499[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 13.89[/C][C]-3.886[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.85[/C][C] 1.153[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.37[/C][C] 3.63[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.82[/C][C] 0.183[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.81[/C][C] 2.186[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 12.85[/C][C] 1.147[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 15.66[/C][C] 1.338[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 17.12[/C][C]-0.1239[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.66[/C][C] 0.3377[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.34[/C][C] 1.658[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.78[/C][C] 1.215[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 17.04[/C][C]-1.036[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 15.66[/C][C]-0.6623[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.33[/C][C]-3.326[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.71[/C][C] 0.2938[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.5[/C][C]-1.503[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 15.06[/C][C] 0.9408[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.55[/C][C] 0.4459[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 16.46[/C][C]-0.4615[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.77[/C][C]-1.766[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.23[/C][C]-0.231[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 14.56[/C][C]-0.5562[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 15.87[/C][C]-0.8747[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.08[/C][C]-0.07542[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.25[/C][C] 0.7527[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 11.71[/C][C]-0.7145[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 16.05[/C][C] 1.953[/C][/ROW]
[ROW][C]81[/C][C] 11[/C][C] 13.87[/C][C]-2.87[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.73[/C][C] 0.273[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.91[/C][C]-1.911[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 18.19[/C][C] 0.8141[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 17.02[/C][C]-0.01966[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.21[/C][C]-1.211[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 15.76[/C][C]-2.755[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.85[/C][C] 1.153[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.83[/C][C]-1.831[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 16.03[/C][C] 2.97[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 14.5[/C][C]-0.501[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.96[/C][C]-0.9555[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.25[/C][C] 0.7527[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.68[/C][C]-0.6785[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 14.69[/C][C]-2.692[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.51[/C][C] 0.4945[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.68[/C][C] 2.321[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 14.15[/C][C] 0.8498[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 15.77[/C][C] 2.234[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 17.39[/C][C]-2.387[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.68[/C][C] 0.3215[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 13.85[/C][C] 2.15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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	12.99	0.01105
2	16	15.33	0.6673
3	17	15.86	1.142
4	16	15.18	0.8164
5	17	17.02	-0.01966
6	17	15.91	1.093
7	15	15.38	-0.3832
8	16	15.2	0.7967
9	14	13.81	0.1901
10	16	15.36	0.6421
11	17	15.02	1.985
12	16	14.54	1.46
13	16	15.66	0.3377
14	16	14.71	1.292
15	15	15.72	-0.7225
16	16	14.52	1.48
17	16	16.37	-0.3696
18	13	14.5	-1.501
19	15	17.02	-2.02
20	17	16.31	0.6907
21	13	13.36	-0.359
22	17	16.96	0.03977
23	14	13.98	0.01819
24	14	14.53	-0.5335
25	18	15.71	2.294
26	17	17.02	-0.01966
27	13	13.87	-0.8702
28	16	17.19	-1.192
29	15	15.88	-0.8781
30	15	15.2	-0.2033
31	15	15.64	-0.6371
32	13	15.87	-2.875
33	17	16.91	0.0885
34	11	14.61	-3.609
35	14	14	0.001948
36	13	15.71	-2.706
37	17	15.12	1.881
38	16	15.4	0.6006
39	17	17.7	-0.6992
40	16	14.54	1.464
41	16	15.83	0.1693
42	16	14.77	1.229
43	15	15.81	-0.8144
44	12	13.31	-1.312
45	17	15.44	1.557
46	14	15.4	-1.399
47	14	15.68	-1.682
48	16	14.71	1.292
49	15	15.09	-0.09166
50	16	15.87	0.1253
51	14	14.69	-0.6921
52	15	13.81	1.19
53	17	14.5	2.499
54	10	13.89	-3.886
55	17	15.85	1.153
56	20	16.37	3.63
57	17	16.82	0.183
58	18	15.81	2.186
59	14	12.85	1.147
60	17	15.66	1.338
61	17	17.12	-0.1239
62	16	15.66	0.3377
63	18	16.34	1.658
64	18	16.78	1.215
65	16	17.04	-1.036
66	15	15.66	-0.6623
67	13	16.33	-3.326
68	16	15.71	0.2938
69	12	13.5	-1.503
70	16	15.06	0.9408
71	16	15.55	0.4459
72	16	16.46	-0.4615
73	14	15.77	-1.766
74	15	15.23	-0.231
75	14	14.56	-0.5562
76	15	15.87	-0.8747
77	15	15.08	-0.07542
78	16	15.25	0.7527
79	11	11.71	-0.7145
80	18	16.05	1.953
81	11	13.87	-2.87
82	18	17.73	0.273
83	15	16.91	-1.911
84	19	18.19	0.8141
85	17	17.02	-0.01966
86	14	15.21	-1.211
87	13	15.76	-2.755
88	17	15.85	1.153
89	14	15.83	-1.831
90	19	16.03	2.97
91	14	14.5	-0.501
92	16	16.96	-0.9555
93	16	15.25	0.7527
94	15	15.68	-0.6785
95	12	14.69	-2.692
96	17	16.51	0.4945
97	18	15.68	2.321
98	15	14.15	0.8498
99	18	15.77	2.234
100	15	17.39	-2.387
101	16	15.68	0.3215
102	16	13.85	2.15

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.07607	0.1521	0.9239
11	0.1008	0.2015	0.8992
12	0.06374	0.1275	0.9363
13	0.02743	0.05487	0.9726
14	0.01223	0.02445	0.9878
15	0.02194	0.04389	0.9781
16	0.01057	0.02114	0.9894
17	0.004766	0.009531	0.9952
18	0.006497	0.01299	0.9935
19	0.03048	0.06097	0.9695
20	0.02816	0.05632	0.9718
21	0.01856	0.03713	0.9814
22	0.01191	0.02382	0.9881
23	0.007733	0.01547	0.9923
24	0.005608	0.01122	0.9944
25	0.02773	0.05546	0.9723
26	0.01737	0.03474	0.9826
27	0.01549	0.03098	0.9845
28	0.01449	0.02898	0.9855
29	0.0094	0.0188	0.9906
30	0.006283	0.01257	0.9937
31	0.004054	0.008107	0.9959
32	0.0176	0.03519	0.9824
33	0.01211	0.02422	0.9879
34	0.09127	0.1825	0.9087
35	0.06695	0.1339	0.9331
36	0.1103	0.2205	0.8897
37	0.1326	0.2651	0.8674
38	0.1067	0.2134	0.8933
39	0.083	0.166	0.917
40	0.07611	0.1522	0.9239
41	0.05637	0.1127	0.9436
42	0.05396	0.1079	0.946
43	0.0419	0.0838	0.9581
44	0.04137	0.08273	0.9586
45	0.04288	0.08577	0.9571
46	0.04508	0.09016	0.9549
47	0.04524	0.09048	0.9548
48	0.0426	0.08521	0.9574
49	0.03823	0.07646	0.9618
50	0.02758	0.05517	0.9724
51	0.02219	0.04438	0.9778
52	0.02005	0.0401	0.98
53	0.04313	0.08625	0.9569
54	0.2149	0.4298	0.7851
55	0.2036	0.4071	0.7964
56	0.4409	0.8818	0.5591
57	0.3828	0.7656	0.6172
58	0.4399	0.8799	0.5601
59	0.4027	0.8053	0.5973
60	0.3974	0.7947	0.6026
61	0.3394	0.6787	0.6606
62	0.2916	0.5833	0.7084
63	0.3041	0.6081	0.6959
64	0.2859	0.5717	0.7141
65	0.2496	0.4993	0.7504
66	0.2071	0.4143	0.7929
67	0.393	0.786	0.607
68	0.3344	0.6687	0.6656
69	0.3205	0.6411	0.6795
70	0.3193	0.6385	0.6807
71	0.2903	0.5805	0.7097
72	0.264	0.528	0.736
73	0.2758	0.5516	0.7242
74	0.2208	0.4416	0.7792
75	0.1883	0.3766	0.8117
76	0.1721	0.3443	0.8279
77	0.1478	0.2955	0.8522
78	0.1306	0.2611	0.8694
79	0.1	0.2	0.9
80	0.08733	0.1747	0.9127
81	0.1701	0.3401	0.8299
82	0.1236	0.2473	0.8764
83	0.1011	0.2022	0.8989
84	0.07092	0.1418	0.9291
85	0.05931	0.1186	0.9407
86	0.04256	0.08513	0.9574
87	0.3308	0.6616	0.6692
88	0.258	0.516	0.742
89	0.4709	0.9417	0.5291
90	0.7531	0.4939	0.2469
91	0.6148	0.7705	0.3852
92	0.4561	0.9123	0.5439

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.07607 &  0.1521 &  0.9239 \tabularnewline
11 &  0.1008 &  0.2015 &  0.8992 \tabularnewline
12 &  0.06374 &  0.1275 &  0.9363 \tabularnewline
13 &  0.02743 &  0.05487 &  0.9726 \tabularnewline
14 &  0.01223 &  0.02445 &  0.9878 \tabularnewline
15 &  0.02194 &  0.04389 &  0.9781 \tabularnewline
16 &  0.01057 &  0.02114 &  0.9894 \tabularnewline
17 &  0.004766 &  0.009531 &  0.9952 \tabularnewline
18 &  0.006497 &  0.01299 &  0.9935 \tabularnewline
19 &  0.03048 &  0.06097 &  0.9695 \tabularnewline
20 &  0.02816 &  0.05632 &  0.9718 \tabularnewline
21 &  0.01856 &  0.03713 &  0.9814 \tabularnewline
22 &  0.01191 &  0.02382 &  0.9881 \tabularnewline
23 &  0.007733 &  0.01547 &  0.9923 \tabularnewline
24 &  0.005608 &  0.01122 &  0.9944 \tabularnewline
25 &  0.02773 &  0.05546 &  0.9723 \tabularnewline
26 &  0.01737 &  0.03474 &  0.9826 \tabularnewline
27 &  0.01549 &  0.03098 &  0.9845 \tabularnewline
28 &  0.01449 &  0.02898 &  0.9855 \tabularnewline
29 &  0.0094 &  0.0188 &  0.9906 \tabularnewline
30 &  0.006283 &  0.01257 &  0.9937 \tabularnewline
31 &  0.004054 &  0.008107 &  0.9959 \tabularnewline
32 &  0.0176 &  0.03519 &  0.9824 \tabularnewline
33 &  0.01211 &  0.02422 &  0.9879 \tabularnewline
34 &  0.09127 &  0.1825 &  0.9087 \tabularnewline
35 &  0.06695 &  0.1339 &  0.9331 \tabularnewline
36 &  0.1103 &  0.2205 &  0.8897 \tabularnewline
37 &  0.1326 &  0.2651 &  0.8674 \tabularnewline
38 &  0.1067 &  0.2134 &  0.8933 \tabularnewline
39 &  0.083 &  0.166 &  0.917 \tabularnewline
40 &  0.07611 &  0.1522 &  0.9239 \tabularnewline
41 &  0.05637 &  0.1127 &  0.9436 \tabularnewline
42 &  0.05396 &  0.1079 &  0.946 \tabularnewline
43 &  0.0419 &  0.0838 &  0.9581 \tabularnewline
44 &  0.04137 &  0.08273 &  0.9586 \tabularnewline
45 &  0.04288 &  0.08577 &  0.9571 \tabularnewline
46 &  0.04508 &  0.09016 &  0.9549 \tabularnewline
47 &  0.04524 &  0.09048 &  0.9548 \tabularnewline
48 &  0.0426 &  0.08521 &  0.9574 \tabularnewline
49 &  0.03823 &  0.07646 &  0.9618 \tabularnewline
50 &  0.02758 &  0.05517 &  0.9724 \tabularnewline
51 &  0.02219 &  0.04438 &  0.9778 \tabularnewline
52 &  0.02005 &  0.0401 &  0.98 \tabularnewline
53 &  0.04313 &  0.08625 &  0.9569 \tabularnewline
54 &  0.2149 &  0.4298 &  0.7851 \tabularnewline
55 &  0.2036 &  0.4071 &  0.7964 \tabularnewline
56 &  0.4409 &  0.8818 &  0.5591 \tabularnewline
57 &  0.3828 &  0.7656 &  0.6172 \tabularnewline
58 &  0.4399 &  0.8799 &  0.5601 \tabularnewline
59 &  0.4027 &  0.8053 &  0.5973 \tabularnewline
60 &  0.3974 &  0.7947 &  0.6026 \tabularnewline
61 &  0.3394 &  0.6787 &  0.6606 \tabularnewline
62 &  0.2916 &  0.5833 &  0.7084 \tabularnewline
63 &  0.3041 &  0.6081 &  0.6959 \tabularnewline
64 &  0.2859 &  0.5717 &  0.7141 \tabularnewline
65 &  0.2496 &  0.4993 &  0.7504 \tabularnewline
66 &  0.2071 &  0.4143 &  0.7929 \tabularnewline
67 &  0.393 &  0.786 &  0.607 \tabularnewline
68 &  0.3344 &  0.6687 &  0.6656 \tabularnewline
69 &  0.3205 &  0.6411 &  0.6795 \tabularnewline
70 &  0.3193 &  0.6385 &  0.6807 \tabularnewline
71 &  0.2903 &  0.5805 &  0.7097 \tabularnewline
72 &  0.264 &  0.528 &  0.736 \tabularnewline
73 &  0.2758 &  0.5516 &  0.7242 \tabularnewline
74 &  0.2208 &  0.4416 &  0.7792 \tabularnewline
75 &  0.1883 &  0.3766 &  0.8117 \tabularnewline
76 &  0.1721 &  0.3443 &  0.8279 \tabularnewline
77 &  0.1478 &  0.2955 &  0.8522 \tabularnewline
78 &  0.1306 &  0.2611 &  0.8694 \tabularnewline
79 &  0.1 &  0.2 &  0.9 \tabularnewline
80 &  0.08733 &  0.1747 &  0.9127 \tabularnewline
81 &  0.1701 &  0.3401 &  0.8299 \tabularnewline
82 &  0.1236 &  0.2473 &  0.8764 \tabularnewline
83 &  0.1011 &  0.2022 &  0.8989 \tabularnewline
84 &  0.07092 &  0.1418 &  0.9291 \tabularnewline
85 &  0.05931 &  0.1186 &  0.9407 \tabularnewline
86 &  0.04256 &  0.08513 &  0.9574 \tabularnewline
87 &  0.3308 &  0.6616 &  0.6692 \tabularnewline
88 &  0.258 &  0.516 &  0.742 \tabularnewline
89 &  0.4709 &  0.9417 &  0.5291 \tabularnewline
90 &  0.7531 &  0.4939 &  0.2469 \tabularnewline
91 &  0.6148 &  0.7705 &  0.3852 \tabularnewline
92 &  0.4561 &  0.9123 &  0.5439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&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]10[/C][C] 0.07607[/C][C] 0.1521[/C][C] 0.9239[/C][/ROW]
[ROW][C]11[/C][C] 0.1008[/C][C] 0.2015[/C][C] 0.8992[/C][/ROW]
[ROW][C]12[/C][C] 0.06374[/C][C] 0.1275[/C][C] 0.9363[/C][/ROW]
[ROW][C]13[/C][C] 0.02743[/C][C] 0.05487[/C][C] 0.9726[/C][/ROW]
[ROW][C]14[/C][C] 0.01223[/C][C] 0.02445[/C][C] 0.9878[/C][/ROW]
[ROW][C]15[/C][C] 0.02194[/C][C] 0.04389[/C][C] 0.9781[/C][/ROW]
[ROW][C]16[/C][C] 0.01057[/C][C] 0.02114[/C][C] 0.9894[/C][/ROW]
[ROW][C]17[/C][C] 0.004766[/C][C] 0.009531[/C][C] 0.9952[/C][/ROW]
[ROW][C]18[/C][C] 0.006497[/C][C] 0.01299[/C][C] 0.9935[/C][/ROW]
[ROW][C]19[/C][C] 0.03048[/C][C] 0.06097[/C][C] 0.9695[/C][/ROW]
[ROW][C]20[/C][C] 0.02816[/C][C] 0.05632[/C][C] 0.9718[/C][/ROW]
[ROW][C]21[/C][C] 0.01856[/C][C] 0.03713[/C][C] 0.9814[/C][/ROW]
[ROW][C]22[/C][C] 0.01191[/C][C] 0.02382[/C][C] 0.9881[/C][/ROW]
[ROW][C]23[/C][C] 0.007733[/C][C] 0.01547[/C][C] 0.9923[/C][/ROW]
[ROW][C]24[/C][C] 0.005608[/C][C] 0.01122[/C][C] 0.9944[/C][/ROW]
[ROW][C]25[/C][C] 0.02773[/C][C] 0.05546[/C][C] 0.9723[/C][/ROW]
[ROW][C]26[/C][C] 0.01737[/C][C] 0.03474[/C][C] 0.9826[/C][/ROW]
[ROW][C]27[/C][C] 0.01549[/C][C] 0.03098[/C][C] 0.9845[/C][/ROW]
[ROW][C]28[/C][C] 0.01449[/C][C] 0.02898[/C][C] 0.9855[/C][/ROW]
[ROW][C]29[/C][C] 0.0094[/C][C] 0.0188[/C][C] 0.9906[/C][/ROW]
[ROW][C]30[/C][C] 0.006283[/C][C] 0.01257[/C][C] 0.9937[/C][/ROW]
[ROW][C]31[/C][C] 0.004054[/C][C] 0.008107[/C][C] 0.9959[/C][/ROW]
[ROW][C]32[/C][C] 0.0176[/C][C] 0.03519[/C][C] 0.9824[/C][/ROW]
[ROW][C]33[/C][C] 0.01211[/C][C] 0.02422[/C][C] 0.9879[/C][/ROW]
[ROW][C]34[/C][C] 0.09127[/C][C] 0.1825[/C][C] 0.9087[/C][/ROW]
[ROW][C]35[/C][C] 0.06695[/C][C] 0.1339[/C][C] 0.9331[/C][/ROW]
[ROW][C]36[/C][C] 0.1103[/C][C] 0.2205[/C][C] 0.8897[/C][/ROW]
[ROW][C]37[/C][C] 0.1326[/C][C] 0.2651[/C][C] 0.8674[/C][/ROW]
[ROW][C]38[/C][C] 0.1067[/C][C] 0.2134[/C][C] 0.8933[/C][/ROW]
[ROW][C]39[/C][C] 0.083[/C][C] 0.166[/C][C] 0.917[/C][/ROW]
[ROW][C]40[/C][C] 0.07611[/C][C] 0.1522[/C][C] 0.9239[/C][/ROW]
[ROW][C]41[/C][C] 0.05637[/C][C] 0.1127[/C][C] 0.9436[/C][/ROW]
[ROW][C]42[/C][C] 0.05396[/C][C] 0.1079[/C][C] 0.946[/C][/ROW]
[ROW][C]43[/C][C] 0.0419[/C][C] 0.0838[/C][C] 0.9581[/C][/ROW]
[ROW][C]44[/C][C] 0.04137[/C][C] 0.08273[/C][C] 0.9586[/C][/ROW]
[ROW][C]45[/C][C] 0.04288[/C][C] 0.08577[/C][C] 0.9571[/C][/ROW]
[ROW][C]46[/C][C] 0.04508[/C][C] 0.09016[/C][C] 0.9549[/C][/ROW]
[ROW][C]47[/C][C] 0.04524[/C][C] 0.09048[/C][C] 0.9548[/C][/ROW]
[ROW][C]48[/C][C] 0.0426[/C][C] 0.08521[/C][C] 0.9574[/C][/ROW]
[ROW][C]49[/C][C] 0.03823[/C][C] 0.07646[/C][C] 0.9618[/C][/ROW]
[ROW][C]50[/C][C] 0.02758[/C][C] 0.05517[/C][C] 0.9724[/C][/ROW]
[ROW][C]51[/C][C] 0.02219[/C][C] 0.04438[/C][C] 0.9778[/C][/ROW]
[ROW][C]52[/C][C] 0.02005[/C][C] 0.0401[/C][C] 0.98[/C][/ROW]
[ROW][C]53[/C][C] 0.04313[/C][C] 0.08625[/C][C] 0.9569[/C][/ROW]
[ROW][C]54[/C][C] 0.2149[/C][C] 0.4298[/C][C] 0.7851[/C][/ROW]
[ROW][C]55[/C][C] 0.2036[/C][C] 0.4071[/C][C] 0.7964[/C][/ROW]
[ROW][C]56[/C][C] 0.4409[/C][C] 0.8818[/C][C] 0.5591[/C][/ROW]
[ROW][C]57[/C][C] 0.3828[/C][C] 0.7656[/C][C] 0.6172[/C][/ROW]
[ROW][C]58[/C][C] 0.4399[/C][C] 0.8799[/C][C] 0.5601[/C][/ROW]
[ROW][C]59[/C][C] 0.4027[/C][C] 0.8053[/C][C] 0.5973[/C][/ROW]
[ROW][C]60[/C][C] 0.3974[/C][C] 0.7947[/C][C] 0.6026[/C][/ROW]
[ROW][C]61[/C][C] 0.3394[/C][C] 0.6787[/C][C] 0.6606[/C][/ROW]
[ROW][C]62[/C][C] 0.2916[/C][C] 0.5833[/C][C] 0.7084[/C][/ROW]
[ROW][C]63[/C][C] 0.3041[/C][C] 0.6081[/C][C] 0.6959[/C][/ROW]
[ROW][C]64[/C][C] 0.2859[/C][C] 0.5717[/C][C] 0.7141[/C][/ROW]
[ROW][C]65[/C][C] 0.2496[/C][C] 0.4993[/C][C] 0.7504[/C][/ROW]
[ROW][C]66[/C][C] 0.2071[/C][C] 0.4143[/C][C] 0.7929[/C][/ROW]
[ROW][C]67[/C][C] 0.393[/C][C] 0.786[/C][C] 0.607[/C][/ROW]
[ROW][C]68[/C][C] 0.3344[/C][C] 0.6687[/C][C] 0.6656[/C][/ROW]
[ROW][C]69[/C][C] 0.3205[/C][C] 0.6411[/C][C] 0.6795[/C][/ROW]
[ROW][C]70[/C][C] 0.3193[/C][C] 0.6385[/C][C] 0.6807[/C][/ROW]
[ROW][C]71[/C][C] 0.2903[/C][C] 0.5805[/C][C] 0.7097[/C][/ROW]
[ROW][C]72[/C][C] 0.264[/C][C] 0.528[/C][C] 0.736[/C][/ROW]
[ROW][C]73[/C][C] 0.2758[/C][C] 0.5516[/C][C] 0.7242[/C][/ROW]
[ROW][C]74[/C][C] 0.2208[/C][C] 0.4416[/C][C] 0.7792[/C][/ROW]
[ROW][C]75[/C][C] 0.1883[/C][C] 0.3766[/C][C] 0.8117[/C][/ROW]
[ROW][C]76[/C][C] 0.1721[/C][C] 0.3443[/C][C] 0.8279[/C][/ROW]
[ROW][C]77[/C][C] 0.1478[/C][C] 0.2955[/C][C] 0.8522[/C][/ROW]
[ROW][C]78[/C][C] 0.1306[/C][C] 0.2611[/C][C] 0.8694[/C][/ROW]
[ROW][C]79[/C][C] 0.1[/C][C] 0.2[/C][C] 0.9[/C][/ROW]
[ROW][C]80[/C][C] 0.08733[/C][C] 0.1747[/C][C] 0.9127[/C][/ROW]
[ROW][C]81[/C][C] 0.1701[/C][C] 0.3401[/C][C] 0.8299[/C][/ROW]
[ROW][C]82[/C][C] 0.1236[/C][C] 0.2473[/C][C] 0.8764[/C][/ROW]
[ROW][C]83[/C][C] 0.1011[/C][C] 0.2022[/C][C] 0.8989[/C][/ROW]
[ROW][C]84[/C][C] 0.07092[/C][C] 0.1418[/C][C] 0.9291[/C][/ROW]
[ROW][C]85[/C][C] 0.05931[/C][C] 0.1186[/C][C] 0.9407[/C][/ROW]
[ROW][C]86[/C][C] 0.04256[/C][C] 0.08513[/C][C] 0.9574[/C][/ROW]
[ROW][C]87[/C][C] 0.3308[/C][C] 0.6616[/C][C] 0.6692[/C][/ROW]
[ROW][C]88[/C][C] 0.258[/C][C] 0.516[/C][C] 0.742[/C][/ROW]
[ROW][C]89[/C][C] 0.4709[/C][C] 0.9417[/C][C] 0.5291[/C][/ROW]
[ROW][C]90[/C][C] 0.7531[/C][C] 0.4939[/C][C] 0.2469[/C][/ROW]
[ROW][C]91[/C][C] 0.6148[/C][C] 0.7705[/C][C] 0.3852[/C][/ROW]
[ROW][C]92[/C][C] 0.4561[/C][C] 0.9123[/C][C] 0.5439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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
10	0.07607	0.1521	0.9239
11	0.1008	0.2015	0.8992
12	0.06374	0.1275	0.9363
13	0.02743	0.05487	0.9726
14	0.01223	0.02445	0.9878
15	0.02194	0.04389	0.9781
16	0.01057	0.02114	0.9894
17	0.004766	0.009531	0.9952
18	0.006497	0.01299	0.9935
19	0.03048	0.06097	0.9695
20	0.02816	0.05632	0.9718
21	0.01856	0.03713	0.9814
22	0.01191	0.02382	0.9881
23	0.007733	0.01547	0.9923
24	0.005608	0.01122	0.9944
25	0.02773	0.05546	0.9723
26	0.01737	0.03474	0.9826
27	0.01549	0.03098	0.9845
28	0.01449	0.02898	0.9855
29	0.0094	0.0188	0.9906
30	0.006283	0.01257	0.9937
31	0.004054	0.008107	0.9959
32	0.0176	0.03519	0.9824
33	0.01211	0.02422	0.9879
34	0.09127	0.1825	0.9087
35	0.06695	0.1339	0.9331
36	0.1103	0.2205	0.8897
37	0.1326	0.2651	0.8674
38	0.1067	0.2134	0.8933
39	0.083	0.166	0.917
40	0.07611	0.1522	0.9239
41	0.05637	0.1127	0.9436
42	0.05396	0.1079	0.946
43	0.0419	0.0838	0.9581
44	0.04137	0.08273	0.9586
45	0.04288	0.08577	0.9571
46	0.04508	0.09016	0.9549
47	0.04524	0.09048	0.9548
48	0.0426	0.08521	0.9574
49	0.03823	0.07646	0.9618
50	0.02758	0.05517	0.9724
51	0.02219	0.04438	0.9778
52	0.02005	0.0401	0.98
53	0.04313	0.08625	0.9569
54	0.2149	0.4298	0.7851
55	0.2036	0.4071	0.7964
56	0.4409	0.8818	0.5591
57	0.3828	0.7656	0.6172
58	0.4399	0.8799	0.5601
59	0.4027	0.8053	0.5973
60	0.3974	0.7947	0.6026
61	0.3394	0.6787	0.6606
62	0.2916	0.5833	0.7084
63	0.3041	0.6081	0.6959
64	0.2859	0.5717	0.7141
65	0.2496	0.4993	0.7504
66	0.2071	0.4143	0.7929
67	0.393	0.786	0.607
68	0.3344	0.6687	0.6656
69	0.3205	0.6411	0.6795
70	0.3193	0.6385	0.6807
71	0.2903	0.5805	0.7097
72	0.264	0.528	0.736
73	0.2758	0.5516	0.7242
74	0.2208	0.4416	0.7792
75	0.1883	0.3766	0.8117
76	0.1721	0.3443	0.8279
77	0.1478	0.2955	0.8522
78	0.1306	0.2611	0.8694
79	0.1	0.2	0.9
80	0.08733	0.1747	0.9127
81	0.1701	0.3401	0.8299
82	0.1236	0.2473	0.8764
83	0.1011	0.2022	0.8989
84	0.07092	0.1418	0.9291
85	0.05931	0.1186	0.9407
86	0.04256	0.08513	0.9574
87	0.3308	0.6616	0.6692
88	0.258	0.516	0.742
89	0.4709	0.9417	0.5291
90	0.7531	0.4939	0.2469
91	0.6148	0.7705	0.3852
92	0.4561	0.9123	0.5439

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0241	NOK
5% type I error level	19	0.228916	NOK
10% type I error level	33	0.39759	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 & 2 &  0.0241 & NOK \tabularnewline
5% type I error level & 19 & 0.228916 & NOK \tabularnewline
10% type I error level & 33 & 0.39759 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&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]2[/C][C] 0.0241[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.228916[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.39759[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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	2	0.0241	NOK
5% type I error level	19	0.228916	NOK
10% type I error level	33	0.39759	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.87167, df1 = 2, df2 = 93, p-value = 0.4216
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1819, df1 = 12, df2 = 83, p-value = 0.3097
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.145, df1 = 2, df2 = 93, p-value = 0.3227

\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 = 0.87167, df1 = 2, df2 = 93, p-value = 0.4216
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1819, df1 = 12, df2 = 83, p-value = 0.3097
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.145, df1 = 2, df2 = 93, p-value = 0.3227
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&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 = 0.87167, df1 = 2, df2 = 93, p-value = 0.4216
[/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 = 1.1819, df1 = 12, df2 = 83, p-value = 0.3097
[/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 = 1.145, df1 = 2, df2 = 93, p-value = 0.3227
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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 = 0.87167, df1 = 2, df2 = 93, p-value = 0.4216
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1819, df1 = 12, df2 = 83, p-value = 0.3097
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.145, df1 = 2, df2 = 93, p-value = 0.3227

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.075900 1.171908 1.059866 1.066075 1.033619 1.035552

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.075900 1.171908 1.059866 1.066075 1.033619 1.035552 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297549&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.075900 1.171908 1.059866 1.066075 1.033619 1.035552 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297549&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297549&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 1.075900 1.171908 1.059866 1.066075 1.033619 1.035552

Figure 1

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Figure 10

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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):

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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