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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 29 Nov 2015 10:54:36 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Nov/29/t1448794489h61jknpqfq09z2b.htm/, Retrieved Wed, 15 May 2024 08:36:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284405, Retrieved Wed, 15 May 2024 08:36:17 +0000

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Estimated Impact

189

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

-       [Multiple Regression] [multiple regression] [2015-11-29 10:54:36] [60e7016130b28a0c8bd4011f80276a66] [Current]

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Dataseries X:

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2.3 6.5
1.9 6.8
0.6 6.8
0.6 6.5
-0.4 6.2
-1.1 6.2
-1.7 6.6
-0.8 6.7
-1.2 6.5
-1 6.4
-0.1 6.5
0.3 6.8
0.6 7.1
0.7 7.2
1.7 7.1
1.8 7
2.3 6.9
2.5 6.9
2.6 7.4
2.3 7.3
2.9 7
3 6.8
2.9 6.5
3.1 6.4
3.2 6.3
3.4 6
3.5 5.9
3.4 5.7
3.4 5.7
3.7 5.7
3.8 6.2
3.6 6.4
3.6 6.2
3.6 6.2
3.9 6.1
3.5 6.1
3.7 6.2
3.7 6.1
3.4 6.1
3.2 6.2
2.8 6.2
2.3 6.2
2.3 6.4
2.9 6.4
2.8 6.4
2.8 6.7
2.3 6.9
2.2 7.1
1.5 7.3
1.2 7.2
1.1 7.1
1 6.9
1.2 6.8
1.6 6.7
1.5 7.2
1 7.2
0.9 7.1
0.6 7.1
0.8 7
1 7.1
1.1 7.3
1 7.2
0.9 7.1
0.6 7
0.4 6.9
0.3 7
0.3 7.5
0 7.6
-0.1 7.5
0.1 7.3
-0.1 7.3
-0.4 7.4
-0.7 7.7
-0.4 7.8
-0.4 7.7
0.3 7.5
0.6 7.3
0.6 7.3
0.5 7.6
0.9 7.6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 12.8738 -1.67011werkloosheid[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  12.8738 -1.67011werkloosheid[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  12.8738 -1.67011werkloosheid[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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
inflatie[t] = + 12.8738 -1.67011werkloosheid[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+12.87	1.73	+7.4400e+00	1.136e-10	5.68e-11
werkloosheid	-1.67	0.2538	-6.5800e+00	4.9e-09	2.45e-09

\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) & +12.87 &  1.73 & +7.4400e+00 &  1.136e-10 &  5.68e-11 \tabularnewline
werkloosheid & -1.67 &  0.2538 & -6.5800e+00 &  4.9e-09 &  2.45e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&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]+12.87[/C][C] 1.73[/C][C]+7.4400e+00[/C][C] 1.136e-10[/C][C] 5.68e-11[/C][/ROW]
[ROW][C]werkloosheid[/C][C]-1.67[/C][C] 0.2538[/C][C]-6.5800e+00[/C][C] 4.9e-09[/C][C] 2.45e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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)	+12.87	1.73	+7.4400e+00	1.136e-10	5.68e-11
werkloosheid	-1.67	0.2538	-6.5800e+00	4.9e-09	2.45e-09

Multiple Linear Regression - Regression Statistics
Multiple R	0.5975
R-squared	0.357
Adjusted R-squared	0.3487
F-TEST (value)	43.3
F-TEST (DF numerator)	1
F-TEST (DF denominator)	78
p-value	4.9e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.197
Sum Squared Residuals	111.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5975 \tabularnewline
R-squared &  0.357 \tabularnewline
Adjusted R-squared &  0.3487 \tabularnewline
F-TEST (value) &  43.3 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 78 \tabularnewline
p-value &  4.9e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.197 \tabularnewline
Sum Squared Residuals &  111.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5975[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.357[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3487[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 43.3[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]78[/C][/ROW]
[ROW][C]p-value[/C][C] 4.9e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.197[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 111.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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	0.5975
R-squared	0.357
Adjusted R-squared	0.3487
F-TEST (value)	43.3
F-TEST (DF numerator)	1
F-TEST (DF denominator)	78
p-value	4.9e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.197
Sum Squared Residuals	111.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.3	2.018	0.2819
2	1.9	1.517	0.3829
3	0.6	1.517	-0.9171
4	0.6	2.018	-1.418
5	-0.4	2.519	-2.919
6	-1.1	2.519	-3.619
7	-1.7	1.851	-3.551
8	-0.8	1.684	-2.484
9	-1.2	2.018	-3.218
10	-1	2.185	-3.185
11	-0.1	2.018	-2.118
12	0.3	1.517	-1.217
13	0.6	1.016	-0.416
14	0.7	0.849	-0.149
15	1.7	1.016	0.684
16	1.8	1.183	0.6169
17	2.3	1.35	0.9499
18	2.5	1.35	1.15
19	2.6	0.515	2.085
20	2.3	0.682	1.618
21	2.9	1.183	1.717
22	3	1.517	1.483
23	2.9	2.018	0.8819
24	3.1	2.185	0.9149
25	3.2	2.352	0.8479
26	3.4	2.853	0.5468
27	3.5	3.02	0.4798
28	3.4	3.354	0.04581
29	3.4	3.354	0.04581
30	3.7	3.354	0.3458
31	3.8	2.519	1.281
32	3.6	2.185	1.415
33	3.6	2.519	1.081
34	3.6	2.519	1.081
35	3.9	2.686	1.214
36	3.5	2.686	0.8138
37	3.7	2.519	1.181
38	3.7	2.686	1.014
39	3.4	2.686	0.7138
40	3.2	2.519	0.6809
41	2.8	2.519	0.2809
42	2.3	2.519	-0.2191
43	2.3	2.185	0.1149
44	2.9	2.185	0.7149
45	2.8	2.185	0.6149
46	2.8	1.684	1.116
47	2.3	1.35	0.9499
48	2.2	1.016	1.184
49	1.5	0.682	0.818
50	1.2	0.849	0.351
51	1.1	1.016	0.08396
52	1	1.35	-0.3501
53	1.2	1.517	-0.3171
54	1.6	1.684	-0.08409
55	1.5	0.849	0.651
56	1	0.849	0.151
57	0.9	1.016	-0.116
58	0.6	1.016	-0.416
59	0.8	1.183	-0.3831
60	1	1.016	-0.01604
61	1.1	0.682	0.418
62	1	0.849	0.151
63	0.9	1.016	-0.116
64	0.6	1.183	-0.5831
65	0.4	1.35	-0.9501
66	0.3	1.183	-0.8831
67	0.3	0.348	-0.048
68	0	0.181	-0.181
69	-0.1	0.348	-0.448
70	0.1	0.682	-0.582
71	-0.1	0.682	-0.782
72	-0.4	0.515	-0.915
73	-0.7	0.01398	-0.714
74	-0.4	-0.153	-0.247
75	-0.4	0.01398	-0.414
76	0.3	0.348	-0.048
77	0.6	0.682	-0.08202
78	0.6	0.682	-0.08202
79	0.5	0.181	0.319
80	0.9	0.181	0.719

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  2.3 &  2.018 &  0.2819 \tabularnewline
2 &  1.9 &  1.517 &  0.3829 \tabularnewline
3 &  0.6 &  1.517 & -0.9171 \tabularnewline
4 &  0.6 &  2.018 & -1.418 \tabularnewline
5 & -0.4 &  2.519 & -2.919 \tabularnewline
6 & -1.1 &  2.519 & -3.619 \tabularnewline
7 & -1.7 &  1.851 & -3.551 \tabularnewline
8 & -0.8 &  1.684 & -2.484 \tabularnewline
9 & -1.2 &  2.018 & -3.218 \tabularnewline
10 & -1 &  2.185 & -3.185 \tabularnewline
11 & -0.1 &  2.018 & -2.118 \tabularnewline
12 &  0.3 &  1.517 & -1.217 \tabularnewline
13 &  0.6 &  1.016 & -0.416 \tabularnewline
14 &  0.7 &  0.849 & -0.149 \tabularnewline
15 &  1.7 &  1.016 &  0.684 \tabularnewline
16 &  1.8 &  1.183 &  0.6169 \tabularnewline
17 &  2.3 &  1.35 &  0.9499 \tabularnewline
18 &  2.5 &  1.35 &  1.15 \tabularnewline
19 &  2.6 &  0.515 &  2.085 \tabularnewline
20 &  2.3 &  0.682 &  1.618 \tabularnewline
21 &  2.9 &  1.183 &  1.717 \tabularnewline
22 &  3 &  1.517 &  1.483 \tabularnewline
23 &  2.9 &  2.018 &  0.8819 \tabularnewline
24 &  3.1 &  2.185 &  0.9149 \tabularnewline
25 &  3.2 &  2.352 &  0.8479 \tabularnewline
26 &  3.4 &  2.853 &  0.5468 \tabularnewline
27 &  3.5 &  3.02 &  0.4798 \tabularnewline
28 &  3.4 &  3.354 &  0.04581 \tabularnewline
29 &  3.4 &  3.354 &  0.04581 \tabularnewline
30 &  3.7 &  3.354 &  0.3458 \tabularnewline
31 &  3.8 &  2.519 &  1.281 \tabularnewline
32 &  3.6 &  2.185 &  1.415 \tabularnewline
33 &  3.6 &  2.519 &  1.081 \tabularnewline
34 &  3.6 &  2.519 &  1.081 \tabularnewline
35 &  3.9 &  2.686 &  1.214 \tabularnewline
36 &  3.5 &  2.686 &  0.8138 \tabularnewline
37 &  3.7 &  2.519 &  1.181 \tabularnewline
38 &  3.7 &  2.686 &  1.014 \tabularnewline
39 &  3.4 &  2.686 &  0.7138 \tabularnewline
40 &  3.2 &  2.519 &  0.6809 \tabularnewline
41 &  2.8 &  2.519 &  0.2809 \tabularnewline
42 &  2.3 &  2.519 & -0.2191 \tabularnewline
43 &  2.3 &  2.185 &  0.1149 \tabularnewline
44 &  2.9 &  2.185 &  0.7149 \tabularnewline
45 &  2.8 &  2.185 &  0.6149 \tabularnewline
46 &  2.8 &  1.684 &  1.116 \tabularnewline
47 &  2.3 &  1.35 &  0.9499 \tabularnewline
48 &  2.2 &  1.016 &  1.184 \tabularnewline
49 &  1.5 &  0.682 &  0.818 \tabularnewline
50 &  1.2 &  0.849 &  0.351 \tabularnewline
51 &  1.1 &  1.016 &  0.08396 \tabularnewline
52 &  1 &  1.35 & -0.3501 \tabularnewline
53 &  1.2 &  1.517 & -0.3171 \tabularnewline
54 &  1.6 &  1.684 & -0.08409 \tabularnewline
55 &  1.5 &  0.849 &  0.651 \tabularnewline
56 &  1 &  0.849 &  0.151 \tabularnewline
57 &  0.9 &  1.016 & -0.116 \tabularnewline
58 &  0.6 &  1.016 & -0.416 \tabularnewline
59 &  0.8 &  1.183 & -0.3831 \tabularnewline
60 &  1 &  1.016 & -0.01604 \tabularnewline
61 &  1.1 &  0.682 &  0.418 \tabularnewline
62 &  1 &  0.849 &  0.151 \tabularnewline
63 &  0.9 &  1.016 & -0.116 \tabularnewline
64 &  0.6 &  1.183 & -0.5831 \tabularnewline
65 &  0.4 &  1.35 & -0.9501 \tabularnewline
66 &  0.3 &  1.183 & -0.8831 \tabularnewline
67 &  0.3 &  0.348 & -0.048 \tabularnewline
68 &  0 &  0.181 & -0.181 \tabularnewline
69 & -0.1 &  0.348 & -0.448 \tabularnewline
70 &  0.1 &  0.682 & -0.582 \tabularnewline
71 & -0.1 &  0.682 & -0.782 \tabularnewline
72 & -0.4 &  0.515 & -0.915 \tabularnewline
73 & -0.7 &  0.01398 & -0.714 \tabularnewline
74 & -0.4 & -0.153 & -0.247 \tabularnewline
75 & -0.4 &  0.01398 & -0.414 \tabularnewline
76 &  0.3 &  0.348 & -0.048 \tabularnewline
77 &  0.6 &  0.682 & -0.08202 \tabularnewline
78 &  0.6 &  0.682 & -0.08202 \tabularnewline
79 &  0.5 &  0.181 &  0.319 \tabularnewline
80 &  0.9 &  0.181 &  0.719 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&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] 2.3[/C][C] 2.018[/C][C] 0.2819[/C][/ROW]
[ROW][C]2[/C][C] 1.9[/C][C] 1.517[/C][C] 0.3829[/C][/ROW]
[ROW][C]3[/C][C] 0.6[/C][C] 1.517[/C][C]-0.9171[/C][/ROW]
[ROW][C]4[/C][C] 0.6[/C][C] 2.018[/C][C]-1.418[/C][/ROW]
[ROW][C]5[/C][C]-0.4[/C][C] 2.519[/C][C]-2.919[/C][/ROW]
[ROW][C]6[/C][C]-1.1[/C][C] 2.519[/C][C]-3.619[/C][/ROW]
[ROW][C]7[/C][C]-1.7[/C][C] 1.851[/C][C]-3.551[/C][/ROW]
[ROW][C]8[/C][C]-0.8[/C][C] 1.684[/C][C]-2.484[/C][/ROW]
[ROW][C]9[/C][C]-1.2[/C][C] 2.018[/C][C]-3.218[/C][/ROW]
[ROW][C]10[/C][C]-1[/C][C] 2.185[/C][C]-3.185[/C][/ROW]
[ROW][C]11[/C][C]-0.1[/C][C] 2.018[/C][C]-2.118[/C][/ROW]
[ROW][C]12[/C][C] 0.3[/C][C] 1.517[/C][C]-1.217[/C][/ROW]
[ROW][C]13[/C][C] 0.6[/C][C] 1.016[/C][C]-0.416[/C][/ROW]
[ROW][C]14[/C][C] 0.7[/C][C] 0.849[/C][C]-0.149[/C][/ROW]
[ROW][C]15[/C][C] 1.7[/C][C] 1.016[/C][C] 0.684[/C][/ROW]
[ROW][C]16[/C][C] 1.8[/C][C] 1.183[/C][C] 0.6169[/C][/ROW]
[ROW][C]17[/C][C] 2.3[/C][C] 1.35[/C][C] 0.9499[/C][/ROW]
[ROW][C]18[/C][C] 2.5[/C][C] 1.35[/C][C] 1.15[/C][/ROW]
[ROW][C]19[/C][C] 2.6[/C][C] 0.515[/C][C] 2.085[/C][/ROW]
[ROW][C]20[/C][C] 2.3[/C][C] 0.682[/C][C] 1.618[/C][/ROW]
[ROW][C]21[/C][C] 2.9[/C][C] 1.183[/C][C] 1.717[/C][/ROW]
[ROW][C]22[/C][C] 3[/C][C] 1.517[/C][C] 1.483[/C][/ROW]
[ROW][C]23[/C][C] 2.9[/C][C] 2.018[/C][C] 0.8819[/C][/ROW]
[ROW][C]24[/C][C] 3.1[/C][C] 2.185[/C][C] 0.9149[/C][/ROW]
[ROW][C]25[/C][C] 3.2[/C][C] 2.352[/C][C] 0.8479[/C][/ROW]
[ROW][C]26[/C][C] 3.4[/C][C] 2.853[/C][C] 0.5468[/C][/ROW]
[ROW][C]27[/C][C] 3.5[/C][C] 3.02[/C][C] 0.4798[/C][/ROW]
[ROW][C]28[/C][C] 3.4[/C][C] 3.354[/C][C] 0.04581[/C][/ROW]
[ROW][C]29[/C][C] 3.4[/C][C] 3.354[/C][C] 0.04581[/C][/ROW]
[ROW][C]30[/C][C] 3.7[/C][C] 3.354[/C][C] 0.3458[/C][/ROW]
[ROW][C]31[/C][C] 3.8[/C][C] 2.519[/C][C] 1.281[/C][/ROW]
[ROW][C]32[/C][C] 3.6[/C][C] 2.185[/C][C] 1.415[/C][/ROW]
[ROW][C]33[/C][C] 3.6[/C][C] 2.519[/C][C] 1.081[/C][/ROW]
[ROW][C]34[/C][C] 3.6[/C][C] 2.519[/C][C] 1.081[/C][/ROW]
[ROW][C]35[/C][C] 3.9[/C][C] 2.686[/C][C] 1.214[/C][/ROW]
[ROW][C]36[/C][C] 3.5[/C][C] 2.686[/C][C] 0.8138[/C][/ROW]
[ROW][C]37[/C][C] 3.7[/C][C] 2.519[/C][C] 1.181[/C][/ROW]
[ROW][C]38[/C][C] 3.7[/C][C] 2.686[/C][C] 1.014[/C][/ROW]
[ROW][C]39[/C][C] 3.4[/C][C] 2.686[/C][C] 0.7138[/C][/ROW]
[ROW][C]40[/C][C] 3.2[/C][C] 2.519[/C][C] 0.6809[/C][/ROW]
[ROW][C]41[/C][C] 2.8[/C][C] 2.519[/C][C] 0.2809[/C][/ROW]
[ROW][C]42[/C][C] 2.3[/C][C] 2.519[/C][C]-0.2191[/C][/ROW]
[ROW][C]43[/C][C] 2.3[/C][C] 2.185[/C][C] 0.1149[/C][/ROW]
[ROW][C]44[/C][C] 2.9[/C][C] 2.185[/C][C] 0.7149[/C][/ROW]
[ROW][C]45[/C][C] 2.8[/C][C] 2.185[/C][C] 0.6149[/C][/ROW]
[ROW][C]46[/C][C] 2.8[/C][C] 1.684[/C][C] 1.116[/C][/ROW]
[ROW][C]47[/C][C] 2.3[/C][C] 1.35[/C][C] 0.9499[/C][/ROW]
[ROW][C]48[/C][C] 2.2[/C][C] 1.016[/C][C] 1.184[/C][/ROW]
[ROW][C]49[/C][C] 1.5[/C][C] 0.682[/C][C] 0.818[/C][/ROW]
[ROW][C]50[/C][C] 1.2[/C][C] 0.849[/C][C] 0.351[/C][/ROW]
[ROW][C]51[/C][C] 1.1[/C][C] 1.016[/C][C] 0.08396[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 1.35[/C][C]-0.3501[/C][/ROW]
[ROW][C]53[/C][C] 1.2[/C][C] 1.517[/C][C]-0.3171[/C][/ROW]
[ROW][C]54[/C][C] 1.6[/C][C] 1.684[/C][C]-0.08409[/C][/ROW]
[ROW][C]55[/C][C] 1.5[/C][C] 0.849[/C][C] 0.651[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C] 0.849[/C][C] 0.151[/C][/ROW]
[ROW][C]57[/C][C] 0.9[/C][C] 1.016[/C][C]-0.116[/C][/ROW]
[ROW][C]58[/C][C] 0.6[/C][C] 1.016[/C][C]-0.416[/C][/ROW]
[ROW][C]59[/C][C] 0.8[/C][C] 1.183[/C][C]-0.3831[/C][/ROW]
[ROW][C]60[/C][C] 1[/C][C] 1.016[/C][C]-0.01604[/C][/ROW]
[ROW][C]61[/C][C] 1.1[/C][C] 0.682[/C][C] 0.418[/C][/ROW]
[ROW][C]62[/C][C] 1[/C][C] 0.849[/C][C] 0.151[/C][/ROW]
[ROW][C]63[/C][C] 0.9[/C][C] 1.016[/C][C]-0.116[/C][/ROW]
[ROW][C]64[/C][C] 0.6[/C][C] 1.183[/C][C]-0.5831[/C][/ROW]
[ROW][C]65[/C][C] 0.4[/C][C] 1.35[/C][C]-0.9501[/C][/ROW]
[ROW][C]66[/C][C] 0.3[/C][C] 1.183[/C][C]-0.8831[/C][/ROW]
[ROW][C]67[/C][C] 0.3[/C][C] 0.348[/C][C]-0.048[/C][/ROW]
[ROW][C]68[/C][C] 0[/C][C] 0.181[/C][C]-0.181[/C][/ROW]
[ROW][C]69[/C][C]-0.1[/C][C] 0.348[/C][C]-0.448[/C][/ROW]
[ROW][C]70[/C][C] 0.1[/C][C] 0.682[/C][C]-0.582[/C][/ROW]
[ROW][C]71[/C][C]-0.1[/C][C] 0.682[/C][C]-0.782[/C][/ROW]
[ROW][C]72[/C][C]-0.4[/C][C] 0.515[/C][C]-0.915[/C][/ROW]
[ROW][C]73[/C][C]-0.7[/C][C] 0.01398[/C][C]-0.714[/C][/ROW]
[ROW][C]74[/C][C]-0.4[/C][C]-0.153[/C][C]-0.247[/C][/ROW]
[ROW][C]75[/C][C]-0.4[/C][C] 0.01398[/C][C]-0.414[/C][/ROW]
[ROW][C]76[/C][C] 0.3[/C][C] 0.348[/C][C]-0.048[/C][/ROW]
[ROW][C]77[/C][C] 0.6[/C][C] 0.682[/C][C]-0.08202[/C][/ROW]
[ROW][C]78[/C][C] 0.6[/C][C] 0.682[/C][C]-0.08202[/C][/ROW]
[ROW][C]79[/C][C] 0.5[/C][C] 0.181[/C][C] 0.319[/C][/ROW]
[ROW][C]80[/C][C] 0.9[/C][C] 0.181[/C][C] 0.719[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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	2.3	2.018	0.2819
2	1.9	1.517	0.3829
3	0.6	1.517	-0.9171
4	0.6	2.018	-1.418
5	-0.4	2.519	-2.919
6	-1.1	2.519	-3.619
7	-1.7	1.851	-3.551
8	-0.8	1.684	-2.484
9	-1.2	2.018	-3.218
10	-1	2.185	-3.185
11	-0.1	2.018	-2.118
12	0.3	1.517	-1.217
13	0.6	1.016	-0.416
14	0.7	0.849	-0.149
15	1.7	1.016	0.684
16	1.8	1.183	0.6169
17	2.3	1.35	0.9499
18	2.5	1.35	1.15
19	2.6	0.515	2.085
20	2.3	0.682	1.618
21	2.9	1.183	1.717
22	3	1.517	1.483
23	2.9	2.018	0.8819
24	3.1	2.185	0.9149
25	3.2	2.352	0.8479
26	3.4	2.853	0.5468
27	3.5	3.02	0.4798
28	3.4	3.354	0.04581
29	3.4	3.354	0.04581
30	3.7	3.354	0.3458
31	3.8	2.519	1.281
32	3.6	2.185	1.415
33	3.6	2.519	1.081
34	3.6	2.519	1.081
35	3.9	2.686	1.214
36	3.5	2.686	0.8138
37	3.7	2.519	1.181
38	3.7	2.686	1.014
39	3.4	2.686	0.7138
40	3.2	2.519	0.6809
41	2.8	2.519	0.2809
42	2.3	2.519	-0.2191
43	2.3	2.185	0.1149
44	2.9	2.185	0.7149
45	2.8	2.185	0.6149
46	2.8	1.684	1.116
47	2.3	1.35	0.9499
48	2.2	1.016	1.184
49	1.5	0.682	0.818
50	1.2	0.849	0.351
51	1.1	1.016	0.08396
52	1	1.35	-0.3501
53	1.2	1.517	-0.3171
54	1.6	1.684	-0.08409
55	1.5	0.849	0.651
56	1	0.849	0.151
57	0.9	1.016	-0.116
58	0.6	1.016	-0.416
59	0.8	1.183	-0.3831
60	1	1.016	-0.01604
61	1.1	0.682	0.418
62	1	0.849	0.151
63	0.9	1.016	-0.116
64	0.6	1.183	-0.5831
65	0.4	1.35	-0.9501
66	0.3	1.183	-0.8831
67	0.3	0.348	-0.048
68	0	0.181	-0.181
69	-0.1	0.348	-0.448
70	0.1	0.682	-0.582
71	-0.1	0.682	-0.782
72	-0.4	0.515	-0.915
73	-0.7	0.01398	-0.714
74	-0.4	-0.153	-0.247
75	-0.4	0.01398	-0.414
76	0.3	0.348	-0.048
77	0.6	0.682	-0.08202
78	0.6	0.682	-0.08202
79	0.5	0.181	0.319
80	0.9	0.181	0.719

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.5297	0.9405	0.4703
6	0.5541	0.8918	0.4459
7	0.9229	0.1542	0.07708
8	0.9556	0.08879	0.04439
9	0.9842	0.03167	0.01584
10	0.9962	0.007507	0.003754
11	0.9983	0.003483	0.001742
12	0.9983	0.003424	0.001712
13	0.9974	0.005247	0.002624
14	0.9956	0.008848	0.004424
15	0.9945	0.01092	0.005462
16	0.9939	0.01221	0.006107
17	0.9967	0.006616	0.003308
18	0.9984	0.003111	0.001555
19	0.9994	0.001117	0.0005583
20	0.9997	0.00064	0.00032
21	1	9.14e-05	4.57e-05
22	1	6.539e-06	3.27e-06
23	1	3.885e-07	1.943e-07
24	1	2.343e-08	1.171e-08
25	1	2.548e-09	1.274e-09
26	1	3.479e-10	1.74e-10
27	1	1.321e-10	6.606e-11
28	1	6.097e-11	3.049e-11
29	1	3.562e-11	1.781e-11
30	1	3.161e-11	1.58e-11
31	1	2.844e-11	1.422e-11
32	1	1.623e-11	8.117e-12
33	1	2.41e-11	1.205e-11
34	1	3.65e-11	1.825e-11
35	1	4.391e-11	2.195e-11
36	1	1.022e-10	5.111e-11
37	1	1.163e-10	5.814e-11
38	1	1.843e-10	9.217e-11
39	1	4.404e-10	2.202e-10
40	1	1.013e-09	5.065e-10
41	1	2.99e-09	1.495e-09
42	1	6.025e-09	3.013e-09
43	1	1.655e-08	8.277e-09
44	1	3.498e-08	1.749e-08
45	1	7.533e-08	3.767e-08
46	1	3.752e-08	1.876e-08
47	1	1.929e-08	9.645e-09
48	1	2.291e-09	1.146e-09
49	1	9.763e-10	4.881e-10
50	1	1.631e-09	8.153e-10
51	1	4.303e-09	2.151e-09
52	1	1.396e-08	6.978e-09
53	1	4.48e-08	2.24e-08
54	1	1.175e-07	5.875e-08
55	1	5.458e-08	2.729e-08
56	1	1.085e-07	5.426e-08
57	1	3.018e-07	1.509e-07
58	1	9.489e-07	4.745e-07
59	1	2.907e-06	1.454e-06
60	1	6.349e-06	3.175e-06
61	1	5.406e-06	2.703e-06
62	1	7.058e-06	3.529e-06
63	1	1.301e-05	6.504e-06
64	1	3.93e-05	1.965e-05
65	0.9999	0.000112	5.598e-05
66	0.9999	0.0002826	0.0001413
67	0.9996	0.00077	0.000385
68	0.9989	0.002204	0.001102
69	0.9972	0.005653	0.002827
70	0.9933	0.01343	0.006715
71	0.989	0.02196	0.01098
72	0.9915	0.0171	0.008548
73	0.9898	0.02041	0.01021
74	0.9739	0.0522	0.0261
75	0.989	0.02195	0.01097

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.5297 &  0.9405 &  0.4703 \tabularnewline
6 &  0.5541 &  0.8918 &  0.4459 \tabularnewline
7 &  0.9229 &  0.1542 &  0.07708 \tabularnewline
8 &  0.9556 &  0.08879 &  0.04439 \tabularnewline
9 &  0.9842 &  0.03167 &  0.01584 \tabularnewline
10 &  0.9962 &  0.007507 &  0.003754 \tabularnewline
11 &  0.9983 &  0.003483 &  0.001742 \tabularnewline
12 &  0.9983 &  0.003424 &  0.001712 \tabularnewline
13 &  0.9974 &  0.005247 &  0.002624 \tabularnewline
14 &  0.9956 &  0.008848 &  0.004424 \tabularnewline
15 &  0.9945 &  0.01092 &  0.005462 \tabularnewline
16 &  0.9939 &  0.01221 &  0.006107 \tabularnewline
17 &  0.9967 &  0.006616 &  0.003308 \tabularnewline
18 &  0.9984 &  0.003111 &  0.001555 \tabularnewline
19 &  0.9994 &  0.001117 &  0.0005583 \tabularnewline
20 &  0.9997 &  0.00064 &  0.00032 \tabularnewline
21 &  1 &  9.14e-05 &  4.57e-05 \tabularnewline
22 &  1 &  6.539e-06 &  3.27e-06 \tabularnewline
23 &  1 &  3.885e-07 &  1.943e-07 \tabularnewline
24 &  1 &  2.343e-08 &  1.171e-08 \tabularnewline
25 &  1 &  2.548e-09 &  1.274e-09 \tabularnewline
26 &  1 &  3.479e-10 &  1.74e-10 \tabularnewline
27 &  1 &  1.321e-10 &  6.606e-11 \tabularnewline
28 &  1 &  6.097e-11 &  3.049e-11 \tabularnewline
29 &  1 &  3.562e-11 &  1.781e-11 \tabularnewline
30 &  1 &  3.161e-11 &  1.58e-11 \tabularnewline
31 &  1 &  2.844e-11 &  1.422e-11 \tabularnewline
32 &  1 &  1.623e-11 &  8.117e-12 \tabularnewline
33 &  1 &  2.41e-11 &  1.205e-11 \tabularnewline
34 &  1 &  3.65e-11 &  1.825e-11 \tabularnewline
35 &  1 &  4.391e-11 &  2.195e-11 \tabularnewline
36 &  1 &  1.022e-10 &  5.111e-11 \tabularnewline
37 &  1 &  1.163e-10 &  5.814e-11 \tabularnewline
38 &  1 &  1.843e-10 &  9.217e-11 \tabularnewline
39 &  1 &  4.404e-10 &  2.202e-10 \tabularnewline
40 &  1 &  1.013e-09 &  5.065e-10 \tabularnewline
41 &  1 &  2.99e-09 &  1.495e-09 \tabularnewline
42 &  1 &  6.025e-09 &  3.013e-09 \tabularnewline
43 &  1 &  1.655e-08 &  8.277e-09 \tabularnewline
44 &  1 &  3.498e-08 &  1.749e-08 \tabularnewline
45 &  1 &  7.533e-08 &  3.767e-08 \tabularnewline
46 &  1 &  3.752e-08 &  1.876e-08 \tabularnewline
47 &  1 &  1.929e-08 &  9.645e-09 \tabularnewline
48 &  1 &  2.291e-09 &  1.146e-09 \tabularnewline
49 &  1 &  9.763e-10 &  4.881e-10 \tabularnewline
50 &  1 &  1.631e-09 &  8.153e-10 \tabularnewline
51 &  1 &  4.303e-09 &  2.151e-09 \tabularnewline
52 &  1 &  1.396e-08 &  6.978e-09 \tabularnewline
53 &  1 &  4.48e-08 &  2.24e-08 \tabularnewline
54 &  1 &  1.175e-07 &  5.875e-08 \tabularnewline
55 &  1 &  5.458e-08 &  2.729e-08 \tabularnewline
56 &  1 &  1.085e-07 &  5.426e-08 \tabularnewline
57 &  1 &  3.018e-07 &  1.509e-07 \tabularnewline
58 &  1 &  9.489e-07 &  4.745e-07 \tabularnewline
59 &  1 &  2.907e-06 &  1.454e-06 \tabularnewline
60 &  1 &  6.349e-06 &  3.175e-06 \tabularnewline
61 &  1 &  5.406e-06 &  2.703e-06 \tabularnewline
62 &  1 &  7.058e-06 &  3.529e-06 \tabularnewline
63 &  1 &  1.301e-05 &  6.504e-06 \tabularnewline
64 &  1 &  3.93e-05 &  1.965e-05 \tabularnewline
65 &  0.9999 &  0.000112 &  5.598e-05 \tabularnewline
66 &  0.9999 &  0.0002826 &  0.0001413 \tabularnewline
67 &  0.9996 &  0.00077 &  0.000385 \tabularnewline
68 &  0.9989 &  0.002204 &  0.001102 \tabularnewline
69 &  0.9972 &  0.005653 &  0.002827 \tabularnewline
70 &  0.9933 &  0.01343 &  0.006715 \tabularnewline
71 &  0.989 &  0.02196 &  0.01098 \tabularnewline
72 &  0.9915 &  0.0171 &  0.008548 \tabularnewline
73 &  0.9898 &  0.02041 &  0.01021 \tabularnewline
74 &  0.9739 &  0.0522 &  0.0261 \tabularnewline
75 &  0.989 &  0.02195 &  0.01097 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&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]5[/C][C] 0.5297[/C][C] 0.9405[/C][C] 0.4703[/C][/ROW]
[ROW][C]6[/C][C] 0.5541[/C][C] 0.8918[/C][C] 0.4459[/C][/ROW]
[ROW][C]7[/C][C] 0.9229[/C][C] 0.1542[/C][C] 0.07708[/C][/ROW]
[ROW][C]8[/C][C] 0.9556[/C][C] 0.08879[/C][C] 0.04439[/C][/ROW]
[ROW][C]9[/C][C] 0.9842[/C][C] 0.03167[/C][C] 0.01584[/C][/ROW]
[ROW][C]10[/C][C] 0.9962[/C][C] 0.007507[/C][C] 0.003754[/C][/ROW]
[ROW][C]11[/C][C] 0.9983[/C][C] 0.003483[/C][C] 0.001742[/C][/ROW]
[ROW][C]12[/C][C] 0.9983[/C][C] 0.003424[/C][C] 0.001712[/C][/ROW]
[ROW][C]13[/C][C] 0.9974[/C][C] 0.005247[/C][C] 0.002624[/C][/ROW]
[ROW][C]14[/C][C] 0.9956[/C][C] 0.008848[/C][C] 0.004424[/C][/ROW]
[ROW][C]15[/C][C] 0.9945[/C][C] 0.01092[/C][C] 0.005462[/C][/ROW]
[ROW][C]16[/C][C] 0.9939[/C][C] 0.01221[/C][C] 0.006107[/C][/ROW]
[ROW][C]17[/C][C] 0.9967[/C][C] 0.006616[/C][C] 0.003308[/C][/ROW]
[ROW][C]18[/C][C] 0.9984[/C][C] 0.003111[/C][C] 0.001555[/C][/ROW]
[ROW][C]19[/C][C] 0.9994[/C][C] 0.001117[/C][C] 0.0005583[/C][/ROW]
[ROW][C]20[/C][C] 0.9997[/C][C] 0.00064[/C][C] 0.00032[/C][/ROW]
[ROW][C]21[/C][C] 1[/C][C] 9.14e-05[/C][C] 4.57e-05[/C][/ROW]
[ROW][C]22[/C][C] 1[/C][C] 6.539e-06[/C][C] 3.27e-06[/C][/ROW]
[ROW][C]23[/C][C] 1[/C][C] 3.885e-07[/C][C] 1.943e-07[/C][/ROW]
[ROW][C]24[/C][C] 1[/C][C] 2.343e-08[/C][C] 1.171e-08[/C][/ROW]
[ROW][C]25[/C][C] 1[/C][C] 2.548e-09[/C][C] 1.274e-09[/C][/ROW]
[ROW][C]26[/C][C] 1[/C][C] 3.479e-10[/C][C] 1.74e-10[/C][/ROW]
[ROW][C]27[/C][C] 1[/C][C] 1.321e-10[/C][C] 6.606e-11[/C][/ROW]
[ROW][C]28[/C][C] 1[/C][C] 6.097e-11[/C][C] 3.049e-11[/C][/ROW]
[ROW][C]29[/C][C] 1[/C][C] 3.562e-11[/C][C] 1.781e-11[/C][/ROW]
[ROW][C]30[/C][C] 1[/C][C] 3.161e-11[/C][C] 1.58e-11[/C][/ROW]
[ROW][C]31[/C][C] 1[/C][C] 2.844e-11[/C][C] 1.422e-11[/C][/ROW]
[ROW][C]32[/C][C] 1[/C][C] 1.623e-11[/C][C] 8.117e-12[/C][/ROW]
[ROW][C]33[/C][C] 1[/C][C] 2.41e-11[/C][C] 1.205e-11[/C][/ROW]
[ROW][C]34[/C][C] 1[/C][C] 3.65e-11[/C][C] 1.825e-11[/C][/ROW]
[ROW][C]35[/C][C] 1[/C][C] 4.391e-11[/C][C] 2.195e-11[/C][/ROW]
[ROW][C]36[/C][C] 1[/C][C] 1.022e-10[/C][C] 5.111e-11[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 1.163e-10[/C][C] 5.814e-11[/C][/ROW]
[ROW][C]38[/C][C] 1[/C][C] 1.843e-10[/C][C] 9.217e-11[/C][/ROW]
[ROW][C]39[/C][C] 1[/C][C] 4.404e-10[/C][C] 2.202e-10[/C][/ROW]
[ROW][C]40[/C][C] 1[/C][C] 1.013e-09[/C][C] 5.065e-10[/C][/ROW]
[ROW][C]41[/C][C] 1[/C][C] 2.99e-09[/C][C] 1.495e-09[/C][/ROW]
[ROW][C]42[/C][C] 1[/C][C] 6.025e-09[/C][C] 3.013e-09[/C][/ROW]
[ROW][C]43[/C][C] 1[/C][C] 1.655e-08[/C][C] 8.277e-09[/C][/ROW]
[ROW][C]44[/C][C] 1[/C][C] 3.498e-08[/C][C] 1.749e-08[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 7.533e-08[/C][C] 3.767e-08[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 3.752e-08[/C][C] 1.876e-08[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 1.929e-08[/C][C] 9.645e-09[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 2.291e-09[/C][C] 1.146e-09[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 9.763e-10[/C][C] 4.881e-10[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 1.631e-09[/C][C] 8.153e-10[/C][/ROW]
[ROW][C]51[/C][C] 1[/C][C] 4.303e-09[/C][C] 2.151e-09[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 1.396e-08[/C][C] 6.978e-09[/C][/ROW]
[ROW][C]53[/C][C] 1[/C][C] 4.48e-08[/C][C] 2.24e-08[/C][/ROW]
[ROW][C]54[/C][C] 1[/C][C] 1.175e-07[/C][C] 5.875e-08[/C][/ROW]
[ROW][C]55[/C][C] 1[/C][C] 5.458e-08[/C][C] 2.729e-08[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C] 1.085e-07[/C][C] 5.426e-08[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C] 3.018e-07[/C][C] 1.509e-07[/C][/ROW]
[ROW][C]58[/C][C] 1[/C][C] 9.489e-07[/C][C] 4.745e-07[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 2.907e-06[/C][C] 1.454e-06[/C][/ROW]
[ROW][C]60[/C][C] 1[/C][C] 6.349e-06[/C][C] 3.175e-06[/C][/ROW]
[ROW][C]61[/C][C] 1[/C][C] 5.406e-06[/C][C] 2.703e-06[/C][/ROW]
[ROW][C]62[/C][C] 1[/C][C] 7.058e-06[/C][C] 3.529e-06[/C][/ROW]
[ROW][C]63[/C][C] 1[/C][C] 1.301e-05[/C][C] 6.504e-06[/C][/ROW]
[ROW][C]64[/C][C] 1[/C][C] 3.93e-05[/C][C] 1.965e-05[/C][/ROW]
[ROW][C]65[/C][C] 0.9999[/C][C] 0.000112[/C][C] 5.598e-05[/C][/ROW]
[ROW][C]66[/C][C] 0.9999[/C][C] 0.0002826[/C][C] 0.0001413[/C][/ROW]
[ROW][C]67[/C][C] 0.9996[/C][C] 0.00077[/C][C] 0.000385[/C][/ROW]
[ROW][C]68[/C][C] 0.9989[/C][C] 0.002204[/C][C] 0.001102[/C][/ROW]
[ROW][C]69[/C][C] 0.9972[/C][C] 0.005653[/C][C] 0.002827[/C][/ROW]
[ROW][C]70[/C][C] 0.9933[/C][C] 0.01343[/C][C] 0.006715[/C][/ROW]
[ROW][C]71[/C][C] 0.989[/C][C] 0.02196[/C][C] 0.01098[/C][/ROW]
[ROW][C]72[/C][C] 0.9915[/C][C] 0.0171[/C][C] 0.008548[/C][/ROW]
[ROW][C]73[/C][C] 0.9898[/C][C] 0.02041[/C][C] 0.01021[/C][/ROW]
[ROW][C]74[/C][C] 0.9739[/C][C] 0.0522[/C][C] 0.0261[/C][/ROW]
[ROW][C]75[/C][C] 0.989[/C][C] 0.02195[/C][C] 0.01097[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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
5	0.5297	0.9405	0.4703
6	0.5541	0.8918	0.4459
7	0.9229	0.1542	0.07708
8	0.9556	0.08879	0.04439
9	0.9842	0.03167	0.01584
10	0.9962	0.007507	0.003754
11	0.9983	0.003483	0.001742
12	0.9983	0.003424	0.001712
13	0.9974	0.005247	0.002624
14	0.9956	0.008848	0.004424
15	0.9945	0.01092	0.005462
16	0.9939	0.01221	0.006107
17	0.9967	0.006616	0.003308
18	0.9984	0.003111	0.001555
19	0.9994	0.001117	0.0005583
20	0.9997	0.00064	0.00032
21	1	9.14e-05	4.57e-05
22	1	6.539e-06	3.27e-06
23	1	3.885e-07	1.943e-07
24	1	2.343e-08	1.171e-08
25	1	2.548e-09	1.274e-09
26	1	3.479e-10	1.74e-10
27	1	1.321e-10	6.606e-11
28	1	6.097e-11	3.049e-11
29	1	3.562e-11	1.781e-11
30	1	3.161e-11	1.58e-11
31	1	2.844e-11	1.422e-11
32	1	1.623e-11	8.117e-12
33	1	2.41e-11	1.205e-11
34	1	3.65e-11	1.825e-11
35	1	4.391e-11	2.195e-11
36	1	1.022e-10	5.111e-11
37	1	1.163e-10	5.814e-11
38	1	1.843e-10	9.217e-11
39	1	4.404e-10	2.202e-10
40	1	1.013e-09	5.065e-10
41	1	2.99e-09	1.495e-09
42	1	6.025e-09	3.013e-09
43	1	1.655e-08	8.277e-09
44	1	3.498e-08	1.749e-08
45	1	7.533e-08	3.767e-08
46	1	3.752e-08	1.876e-08
47	1	1.929e-08	9.645e-09
48	1	2.291e-09	1.146e-09
49	1	9.763e-10	4.881e-10
50	1	1.631e-09	8.153e-10
51	1	4.303e-09	2.151e-09
52	1	1.396e-08	6.978e-09
53	1	4.48e-08	2.24e-08
54	1	1.175e-07	5.875e-08
55	1	5.458e-08	2.729e-08
56	1	1.085e-07	5.426e-08
57	1	3.018e-07	1.509e-07
58	1	9.489e-07	4.745e-07
59	1	2.907e-06	1.454e-06
60	1	6.349e-06	3.175e-06
61	1	5.406e-06	2.703e-06
62	1	7.058e-06	3.529e-06
63	1	1.301e-05	6.504e-06
64	1	3.93e-05	1.965e-05
65	0.9999	0.000112	5.598e-05
66	0.9999	0.0002826	0.0001413
67	0.9996	0.00077	0.000385
68	0.9989	0.002204	0.001102
69	0.9972	0.005653	0.002827
70	0.9933	0.01343	0.006715
71	0.989	0.02196	0.01098
72	0.9915	0.0171	0.008548
73	0.9898	0.02041	0.01021
74	0.9739	0.0522	0.0261
75	0.989	0.02195	0.01097

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	58	0.8169	NOK
5% type I error level	66	0.929577	NOK
10% type I error level	68	0.957746	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 & 58 &  0.8169 & NOK \tabularnewline
5% type I error level & 66 & 0.929577 & NOK \tabularnewline
10% type I error level & 68 & 0.957746 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284405&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]58[/C][C] 0.8169[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.929577[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]68[/C][C]0.957746[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284405&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284405&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	58	0.8169	NOK
5% type I error level	66	0.929577	NOK
10% type I error level	68	0.957746	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
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.'
}
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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code