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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 27 Nov 2015 14:34:27 +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/27/t1448634946fmj51qb2g8kswe8.htm/, Retrieved Wed, 15 May 2024 16:06:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284313, Retrieved Wed, 15 May 2024 16:06:23 +0000

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Estimated Impact

106

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

-     [Multiple Regression] [multiple regression] [2015-11-27 09:58:54] [7f0b4e0465c416ccc680332e4c4dca5e]
-   PD    [Multiple Regression] [multiple regression] [2015-11-27 14:34:27] [002d4648fc88037d8570a4a28cacbbc2] [Current]

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Dataseries X:

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6.5 80.8 2.3
6.8 83.7 1.9
6.8 94.2 0.6
6.5 86.2 0.6
6.2 89 -0.4
6.2 94.7 -1.1
6.6 81.9 -1.7
6.7 80.2 -0.8
6.5 96.5 -1.2
6.4 95.6 -1
6.5 91.9 -0.1
6.8 89.9 0.3
7.1 86.5 0.6
7.2 94.6 0.7
7.1 107.1 1.7
7 98.3 1.8
6.9 94.6 2.3
6.9 111.1 2.5
7.4 91.7 2.6
7.3 91.3 2.3
7 110.7 2.9
6.8 106.4 3
6.5 105.1 2.9
6.4 102.6 3.1
6.3 97.5 3.2
6 103.7 3.4
5.9 124.5 3.5
5.7 103.8 3.4
5.7 111.8 3.4
5.7 108.4 3.7
6.2 91.7 3.8
6.4 100.9 3.6
6.2 114.6 3.6
6.2 106.6 3.6
6.1 103.5 3.9
6.1 101.3 3.5
6.2 97.6 3.7
6.1 100.7 3.7
6.1 118.2 3.4
6.2 98.6 3.2
6.2 101.5 2.8
6.2 109.8 2.3
6.4 96.8 2.3
6.4 97.2 2.9
6.4 107 2.8
6.7 111.3 2.8
6.9 104.6 2.3
7.1 98.7 2.2
7.3 97 1.5
7.2 95.5 1.2
7.1 107.7 1.1
6.9 106.9 1
6.8 105.5 1.2
6.7 110 1.6
7.2 103.4 1.5
7.2 92.8 1
7.1 109 0.9
7.1 115.1 0.6
7 105.4 0.8
7.1 102.3 1
7.3 100.4 1.1
7.2 103.3 1
7.1 111.3 0.9
7 109.9 0.6
6.9 106.7 0.4
7 114.3 0.3
7.5 101.5 0.3
7.6 92.5 0
7.5 119 -0.1
7.3 117 0.1
7.3 105.3 -0.1
7.4 105.5 -0.4
7.7 100.4 -0.7
7.8 98.6 -0.4
7.7 118.5 -0.4
7.5 110.1 0.3
7.3 102.8 0.6
7.3 116.5 0.6
7.6 100.5 0.5
7.6 96.8 0.9

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	'George Udny Yule' @ yule.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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 6.30605 + 0.00816582industrie[t] -0.224219inflatie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  6.30605 +  0.00816582industrie[t] -0.224219inflatie[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  6.30605 +  0.00816582industrie[t] -0.224219inflatie[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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
werkloosheid[t] = + 6.30605 + 0.00816582industrie[t] -0.224219inflatie[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)	+6.306	0.5274	+1.1960e+01	3.157e-19	1.578e-19
industrie	+0.008166	0.00523	+1.5610e+00	0.1226	0.06128
inflatie	-0.2242	0.03288	-6.8190e+00	1.823e-09	9.114e-10

\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) & +6.306 &  0.5274 & +1.1960e+01 &  3.157e-19 &  1.578e-19 \tabularnewline
industrie & +0.008166 &  0.00523 & +1.5610e+00 &  0.1226 &  0.06128 \tabularnewline
inflatie & -0.2242 &  0.03288 & -6.8190e+00 &  1.823e-09 &  9.114e-10 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&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]+6.306[/C][C] 0.5274[/C][C]+1.1960e+01[/C][C] 3.157e-19[/C][C] 1.578e-19[/C][/ROW]
[ROW][C]industrie[/C][C]+0.008166[/C][C] 0.00523[/C][C]+1.5610e+00[/C][C] 0.1226[/C][C] 0.06128[/C][/ROW]
[ROW][C]inflatie[/C][C]-0.2242[/C][C] 0.03288[/C][C]-6.8190e+00[/C][C] 1.823e-09[/C][C] 9.114e-10[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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)	+6.306	0.5274	+1.1960e+01	3.157e-19	1.578e-19
industrie	+0.008166	0.00523	+1.5610e+00	0.1226	0.06128
inflatie	-0.2242	0.03288	-6.8190e+00	1.823e-09	9.114e-10

Multiple Linear Regression - Regression Statistics
Multiple R	0.6138
R-squared	0.3767
Adjusted R-squared	0.3605
F-TEST (value)	23.27
F-TEST (DF numerator)	2
F-TEST (DF denominator)	77
p-value	1.247e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4245
Sum Squared Residuals	13.87

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6138 \tabularnewline
R-squared &  0.3767 \tabularnewline
Adjusted R-squared &  0.3605 \tabularnewline
F-TEST (value) &  23.27 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 77 \tabularnewline
p-value &  1.247e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.4245 \tabularnewline
Sum Squared Residuals &  13.87 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6138[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3767[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3605[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 23.27[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]77[/C][/ROW]
[ROW][C]p-value[/C][C] 1.247e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.4245[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 13.87[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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	0.6138
R-squared	0.3767
Adjusted R-squared	0.3605
F-TEST (value)	23.27
F-TEST (DF numerator)	2
F-TEST (DF denominator)	77
p-value	1.247e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4245
Sum Squared Residuals	13.87

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.5	6.45	0.04986
2	6.8	6.564	0.2365
3	6.8	6.941	-0.1407
4	6.5	6.875	-0.3754
5	6.2	7.122	-0.9225
6	6.2	7.326	-1.126
7	6.6	7.356	-0.756
8	6.7	7.14	-0.4403
9	6.5	7.363	-0.8631
10	6.4	7.311	-0.9109
11	6.5	7.079	-0.5789
12	6.8	6.973	-0.1729
13	7.1	6.878	0.2221
14	7.2	6.922	0.2784
15	7.1	6.799	0.3006
16	7	6.705	0.2948
17	6.9	6.563	0.3372
18	6.9	6.653	0.2473
19	7.4	6.472	0.9281
20	7.3	6.536	0.7641
21	7	6.56	0.4402
22	6.8	6.502	0.2978
23	6.5	6.514	-0.01404
24	6.4	6.449	-0.04878
25	6.3	6.385	-0.08471
26	6	6.391	-0.3905
27	5.9	6.538	-0.6379
28	5.7	6.391	-0.6913
29	5.7	6.457	-0.7566
30	5.7	6.362	-0.6616
31	6.2	6.203	-0.002821
32	6.4	6.323	0.07721
33	6.2	6.435	-0.2347
34	6.2	6.369	-0.1693
35	6.1	6.277	-0.1768
36	6.1	6.348	-0.2485
37	6.2	6.273	-0.07342
38	6.1	6.299	-0.1987
39	6.1	6.509	-0.4089
40	6.2	6.394	-0.1937
41	6.2	6.507	-0.3071
42	6.2	6.687	-0.487
43	6.4	6.581	-0.1808
44	6.4	6.45	-0.04953
45	6.4	6.552	-0.152
46	6.7	6.587	0.1129
47	6.9	6.644	0.2555
48	7.1	6.619	0.4813
49	7.3	6.762	0.5382
50	7.2	6.817	0.3832
51	7.1	6.939	0.1611
52	6.9	6.955	-0.05475
53	6.8	6.898	-0.09848
54	6.7	6.846	-0.1455
55	7.2	6.814	0.3859
56	7.2	6.84	0.3604
57	7.1	6.994	0.1057
58	7.1	7.111	-0.0114
59	7	6.987	0.01265
60	7.1	6.917	0.1828
61	7.3	6.879	0.4207
62	7.2	6.925	0.2746
63	7.1	7.013	0.08689
64	7	7.069	-0.06894
65	6.9	7.088	-0.1877
66	7	7.172	-0.1721
67	7.5	7.068	0.4324
68	7.6	7.061	0.5386
69	7.5	7.3	0.1998
70	7.3	7.239	0.06097
71	7.3	7.188	0.1117
72	7.4	7.257	0.1428
73	7.7	7.283	0.4172
74	7.8	7.201	0.5991
75	7.7	7.363	0.3366
76	7.5	7.138	0.3622
77	7.3	7.011	0.289
78	7.3	7.123	0.1772
79	7.6	7.015	0.5854
80	7.6	6.895	0.7053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6.5 &  6.45 &  0.04986 \tabularnewline
2 &  6.8 &  6.564 &  0.2365 \tabularnewline
3 &  6.8 &  6.941 & -0.1407 \tabularnewline
4 &  6.5 &  6.875 & -0.3754 \tabularnewline
5 &  6.2 &  7.122 & -0.9225 \tabularnewline
6 &  6.2 &  7.326 & -1.126 \tabularnewline
7 &  6.6 &  7.356 & -0.756 \tabularnewline
8 &  6.7 &  7.14 & -0.4403 \tabularnewline
9 &  6.5 &  7.363 & -0.8631 \tabularnewline
10 &  6.4 &  7.311 & -0.9109 \tabularnewline
11 &  6.5 &  7.079 & -0.5789 \tabularnewline
12 &  6.8 &  6.973 & -0.1729 \tabularnewline
13 &  7.1 &  6.878 &  0.2221 \tabularnewline
14 &  7.2 &  6.922 &  0.2784 \tabularnewline
15 &  7.1 &  6.799 &  0.3006 \tabularnewline
16 &  7 &  6.705 &  0.2948 \tabularnewline
17 &  6.9 &  6.563 &  0.3372 \tabularnewline
18 &  6.9 &  6.653 &  0.2473 \tabularnewline
19 &  7.4 &  6.472 &  0.9281 \tabularnewline
20 &  7.3 &  6.536 &  0.7641 \tabularnewline
21 &  7 &  6.56 &  0.4402 \tabularnewline
22 &  6.8 &  6.502 &  0.2978 \tabularnewline
23 &  6.5 &  6.514 & -0.01404 \tabularnewline
24 &  6.4 &  6.449 & -0.04878 \tabularnewline
25 &  6.3 &  6.385 & -0.08471 \tabularnewline
26 &  6 &  6.391 & -0.3905 \tabularnewline
27 &  5.9 &  6.538 & -0.6379 \tabularnewline
28 &  5.7 &  6.391 & -0.6913 \tabularnewline
29 &  5.7 &  6.457 & -0.7566 \tabularnewline
30 &  5.7 &  6.362 & -0.6616 \tabularnewline
31 &  6.2 &  6.203 & -0.002821 \tabularnewline
32 &  6.4 &  6.323 &  0.07721 \tabularnewline
33 &  6.2 &  6.435 & -0.2347 \tabularnewline
34 &  6.2 &  6.369 & -0.1693 \tabularnewline
35 &  6.1 &  6.277 & -0.1768 \tabularnewline
36 &  6.1 &  6.348 & -0.2485 \tabularnewline
37 &  6.2 &  6.273 & -0.07342 \tabularnewline
38 &  6.1 &  6.299 & -0.1987 \tabularnewline
39 &  6.1 &  6.509 & -0.4089 \tabularnewline
40 &  6.2 &  6.394 & -0.1937 \tabularnewline
41 &  6.2 &  6.507 & -0.3071 \tabularnewline
42 &  6.2 &  6.687 & -0.487 \tabularnewline
43 &  6.4 &  6.581 & -0.1808 \tabularnewline
44 &  6.4 &  6.45 & -0.04953 \tabularnewline
45 &  6.4 &  6.552 & -0.152 \tabularnewline
46 &  6.7 &  6.587 &  0.1129 \tabularnewline
47 &  6.9 &  6.644 &  0.2555 \tabularnewline
48 &  7.1 &  6.619 &  0.4813 \tabularnewline
49 &  7.3 &  6.762 &  0.5382 \tabularnewline
50 &  7.2 &  6.817 &  0.3832 \tabularnewline
51 &  7.1 &  6.939 &  0.1611 \tabularnewline
52 &  6.9 &  6.955 & -0.05475 \tabularnewline
53 &  6.8 &  6.898 & -0.09848 \tabularnewline
54 &  6.7 &  6.846 & -0.1455 \tabularnewline
55 &  7.2 &  6.814 &  0.3859 \tabularnewline
56 &  7.2 &  6.84 &  0.3604 \tabularnewline
57 &  7.1 &  6.994 &  0.1057 \tabularnewline
58 &  7.1 &  7.111 & -0.0114 \tabularnewline
59 &  7 &  6.987 &  0.01265 \tabularnewline
60 &  7.1 &  6.917 &  0.1828 \tabularnewline
61 &  7.3 &  6.879 &  0.4207 \tabularnewline
62 &  7.2 &  6.925 &  0.2746 \tabularnewline
63 &  7.1 &  7.013 &  0.08689 \tabularnewline
64 &  7 &  7.069 & -0.06894 \tabularnewline
65 &  6.9 &  7.088 & -0.1877 \tabularnewline
66 &  7 &  7.172 & -0.1721 \tabularnewline
67 &  7.5 &  7.068 &  0.4324 \tabularnewline
68 &  7.6 &  7.061 &  0.5386 \tabularnewline
69 &  7.5 &  7.3 &  0.1998 \tabularnewline
70 &  7.3 &  7.239 &  0.06097 \tabularnewline
71 &  7.3 &  7.188 &  0.1117 \tabularnewline
72 &  7.4 &  7.257 &  0.1428 \tabularnewline
73 &  7.7 &  7.283 &  0.4172 \tabularnewline
74 &  7.8 &  7.201 &  0.5991 \tabularnewline
75 &  7.7 &  7.363 &  0.3366 \tabularnewline
76 &  7.5 &  7.138 &  0.3622 \tabularnewline
77 &  7.3 &  7.011 &  0.289 \tabularnewline
78 &  7.3 &  7.123 &  0.1772 \tabularnewline
79 &  7.6 &  7.015 &  0.5854 \tabularnewline
80 &  7.6 &  6.895 &  0.7053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&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] 6.5[/C][C] 6.45[/C][C] 0.04986[/C][/ROW]
[ROW][C]2[/C][C] 6.8[/C][C] 6.564[/C][C] 0.2365[/C][/ROW]
[ROW][C]3[/C][C] 6.8[/C][C] 6.941[/C][C]-0.1407[/C][/ROW]
[ROW][C]4[/C][C] 6.5[/C][C] 6.875[/C][C]-0.3754[/C][/ROW]
[ROW][C]5[/C][C] 6.2[/C][C] 7.122[/C][C]-0.9225[/C][/ROW]
[ROW][C]6[/C][C] 6.2[/C][C] 7.326[/C][C]-1.126[/C][/ROW]
[ROW][C]7[/C][C] 6.6[/C][C] 7.356[/C][C]-0.756[/C][/ROW]
[ROW][C]8[/C][C] 6.7[/C][C] 7.14[/C][C]-0.4403[/C][/ROW]
[ROW][C]9[/C][C] 6.5[/C][C] 7.363[/C][C]-0.8631[/C][/ROW]
[ROW][C]10[/C][C] 6.4[/C][C] 7.311[/C][C]-0.9109[/C][/ROW]
[ROW][C]11[/C][C] 6.5[/C][C] 7.079[/C][C]-0.5789[/C][/ROW]
[ROW][C]12[/C][C] 6.8[/C][C] 6.973[/C][C]-0.1729[/C][/ROW]
[ROW][C]13[/C][C] 7.1[/C][C] 6.878[/C][C] 0.2221[/C][/ROW]
[ROW][C]14[/C][C] 7.2[/C][C] 6.922[/C][C] 0.2784[/C][/ROW]
[ROW][C]15[/C][C] 7.1[/C][C] 6.799[/C][C] 0.3006[/C][/ROW]
[ROW][C]16[/C][C] 7[/C][C] 6.705[/C][C] 0.2948[/C][/ROW]
[ROW][C]17[/C][C] 6.9[/C][C] 6.563[/C][C] 0.3372[/C][/ROW]
[ROW][C]18[/C][C] 6.9[/C][C] 6.653[/C][C] 0.2473[/C][/ROW]
[ROW][C]19[/C][C] 7.4[/C][C] 6.472[/C][C] 0.9281[/C][/ROW]
[ROW][C]20[/C][C] 7.3[/C][C] 6.536[/C][C] 0.7641[/C][/ROW]
[ROW][C]21[/C][C] 7[/C][C] 6.56[/C][C] 0.4402[/C][/ROW]
[ROW][C]22[/C][C] 6.8[/C][C] 6.502[/C][C] 0.2978[/C][/ROW]
[ROW][C]23[/C][C] 6.5[/C][C] 6.514[/C][C]-0.01404[/C][/ROW]
[ROW][C]24[/C][C] 6.4[/C][C] 6.449[/C][C]-0.04878[/C][/ROW]
[ROW][C]25[/C][C] 6.3[/C][C] 6.385[/C][C]-0.08471[/C][/ROW]
[ROW][C]26[/C][C] 6[/C][C] 6.391[/C][C]-0.3905[/C][/ROW]
[ROW][C]27[/C][C] 5.9[/C][C] 6.538[/C][C]-0.6379[/C][/ROW]
[ROW][C]28[/C][C] 5.7[/C][C] 6.391[/C][C]-0.6913[/C][/ROW]
[ROW][C]29[/C][C] 5.7[/C][C] 6.457[/C][C]-0.7566[/C][/ROW]
[ROW][C]30[/C][C] 5.7[/C][C] 6.362[/C][C]-0.6616[/C][/ROW]
[ROW][C]31[/C][C] 6.2[/C][C] 6.203[/C][C]-0.002821[/C][/ROW]
[ROW][C]32[/C][C] 6.4[/C][C] 6.323[/C][C] 0.07721[/C][/ROW]
[ROW][C]33[/C][C] 6.2[/C][C] 6.435[/C][C]-0.2347[/C][/ROW]
[ROW][C]34[/C][C] 6.2[/C][C] 6.369[/C][C]-0.1693[/C][/ROW]
[ROW][C]35[/C][C] 6.1[/C][C] 6.277[/C][C]-0.1768[/C][/ROW]
[ROW][C]36[/C][C] 6.1[/C][C] 6.348[/C][C]-0.2485[/C][/ROW]
[ROW][C]37[/C][C] 6.2[/C][C] 6.273[/C][C]-0.07342[/C][/ROW]
[ROW][C]38[/C][C] 6.1[/C][C] 6.299[/C][C]-0.1987[/C][/ROW]
[ROW][C]39[/C][C] 6.1[/C][C] 6.509[/C][C]-0.4089[/C][/ROW]
[ROW][C]40[/C][C] 6.2[/C][C] 6.394[/C][C]-0.1937[/C][/ROW]
[ROW][C]41[/C][C] 6.2[/C][C] 6.507[/C][C]-0.3071[/C][/ROW]
[ROW][C]42[/C][C] 6.2[/C][C] 6.687[/C][C]-0.487[/C][/ROW]
[ROW][C]43[/C][C] 6.4[/C][C] 6.581[/C][C]-0.1808[/C][/ROW]
[ROW][C]44[/C][C] 6.4[/C][C] 6.45[/C][C]-0.04953[/C][/ROW]
[ROW][C]45[/C][C] 6.4[/C][C] 6.552[/C][C]-0.152[/C][/ROW]
[ROW][C]46[/C][C] 6.7[/C][C] 6.587[/C][C] 0.1129[/C][/ROW]
[ROW][C]47[/C][C] 6.9[/C][C] 6.644[/C][C] 0.2555[/C][/ROW]
[ROW][C]48[/C][C] 7.1[/C][C] 6.619[/C][C] 0.4813[/C][/ROW]
[ROW][C]49[/C][C] 7.3[/C][C] 6.762[/C][C] 0.5382[/C][/ROW]
[ROW][C]50[/C][C] 7.2[/C][C] 6.817[/C][C] 0.3832[/C][/ROW]
[ROW][C]51[/C][C] 7.1[/C][C] 6.939[/C][C] 0.1611[/C][/ROW]
[ROW][C]52[/C][C] 6.9[/C][C] 6.955[/C][C]-0.05475[/C][/ROW]
[ROW][C]53[/C][C] 6.8[/C][C] 6.898[/C][C]-0.09848[/C][/ROW]
[ROW][C]54[/C][C] 6.7[/C][C] 6.846[/C][C]-0.1455[/C][/ROW]
[ROW][C]55[/C][C] 7.2[/C][C] 6.814[/C][C] 0.3859[/C][/ROW]
[ROW][C]56[/C][C] 7.2[/C][C] 6.84[/C][C] 0.3604[/C][/ROW]
[ROW][C]57[/C][C] 7.1[/C][C] 6.994[/C][C] 0.1057[/C][/ROW]
[ROW][C]58[/C][C] 7.1[/C][C] 7.111[/C][C]-0.0114[/C][/ROW]
[ROW][C]59[/C][C] 7[/C][C] 6.987[/C][C] 0.01265[/C][/ROW]
[ROW][C]60[/C][C] 7.1[/C][C] 6.917[/C][C] 0.1828[/C][/ROW]
[ROW][C]61[/C][C] 7.3[/C][C] 6.879[/C][C] 0.4207[/C][/ROW]
[ROW][C]62[/C][C] 7.2[/C][C] 6.925[/C][C] 0.2746[/C][/ROW]
[ROW][C]63[/C][C] 7.1[/C][C] 7.013[/C][C] 0.08689[/C][/ROW]
[ROW][C]64[/C][C] 7[/C][C] 7.069[/C][C]-0.06894[/C][/ROW]
[ROW][C]65[/C][C] 6.9[/C][C] 7.088[/C][C]-0.1877[/C][/ROW]
[ROW][C]66[/C][C] 7[/C][C] 7.172[/C][C]-0.1721[/C][/ROW]
[ROW][C]67[/C][C] 7.5[/C][C] 7.068[/C][C] 0.4324[/C][/ROW]
[ROW][C]68[/C][C] 7.6[/C][C] 7.061[/C][C] 0.5386[/C][/ROW]
[ROW][C]69[/C][C] 7.5[/C][C] 7.3[/C][C] 0.1998[/C][/ROW]
[ROW][C]70[/C][C] 7.3[/C][C] 7.239[/C][C] 0.06097[/C][/ROW]
[ROW][C]71[/C][C] 7.3[/C][C] 7.188[/C][C] 0.1117[/C][/ROW]
[ROW][C]72[/C][C] 7.4[/C][C] 7.257[/C][C] 0.1428[/C][/ROW]
[ROW][C]73[/C][C] 7.7[/C][C] 7.283[/C][C] 0.4172[/C][/ROW]
[ROW][C]74[/C][C] 7.8[/C][C] 7.201[/C][C] 0.5991[/C][/ROW]
[ROW][C]75[/C][C] 7.7[/C][C] 7.363[/C][C] 0.3366[/C][/ROW]
[ROW][C]76[/C][C] 7.5[/C][C] 7.138[/C][C] 0.3622[/C][/ROW]
[ROW][C]77[/C][C] 7.3[/C][C] 7.011[/C][C] 0.289[/C][/ROW]
[ROW][C]78[/C][C] 7.3[/C][C] 7.123[/C][C] 0.1772[/C][/ROW]
[ROW][C]79[/C][C] 7.6[/C][C] 7.015[/C][C] 0.5854[/C][/ROW]
[ROW][C]80[/C][C] 7.6[/C][C] 6.895[/C][C] 0.7053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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	6.5	6.45	0.04986
2	6.8	6.564	0.2365
3	6.8	6.941	-0.1407
4	6.5	6.875	-0.3754
5	6.2	7.122	-0.9225
6	6.2	7.326	-1.126
7	6.6	7.356	-0.756
8	6.7	7.14	-0.4403
9	6.5	7.363	-0.8631
10	6.4	7.311	-0.9109
11	6.5	7.079	-0.5789
12	6.8	6.973	-0.1729
13	7.1	6.878	0.2221
14	7.2	6.922	0.2784
15	7.1	6.799	0.3006
16	7	6.705	0.2948
17	6.9	6.563	0.3372
18	6.9	6.653	0.2473
19	7.4	6.472	0.9281
20	7.3	6.536	0.7641
21	7	6.56	0.4402
22	6.8	6.502	0.2978
23	6.5	6.514	-0.01404
24	6.4	6.449	-0.04878
25	6.3	6.385	-0.08471
26	6	6.391	-0.3905
27	5.9	6.538	-0.6379
28	5.7	6.391	-0.6913
29	5.7	6.457	-0.7566
30	5.7	6.362	-0.6616
31	6.2	6.203	-0.002821
32	6.4	6.323	0.07721
33	6.2	6.435	-0.2347
34	6.2	6.369	-0.1693
35	6.1	6.277	-0.1768
36	6.1	6.348	-0.2485
37	6.2	6.273	-0.07342
38	6.1	6.299	-0.1987
39	6.1	6.509	-0.4089
40	6.2	6.394	-0.1937
41	6.2	6.507	-0.3071
42	6.2	6.687	-0.487
43	6.4	6.581	-0.1808
44	6.4	6.45	-0.04953
45	6.4	6.552	-0.152
46	6.7	6.587	0.1129
47	6.9	6.644	0.2555
48	7.1	6.619	0.4813
49	7.3	6.762	0.5382
50	7.2	6.817	0.3832
51	7.1	6.939	0.1611
52	6.9	6.955	-0.05475
53	6.8	6.898	-0.09848
54	6.7	6.846	-0.1455
55	7.2	6.814	0.3859
56	7.2	6.84	0.3604
57	7.1	6.994	0.1057
58	7.1	7.111	-0.0114
59	7	6.987	0.01265
60	7.1	6.917	0.1828
61	7.3	6.879	0.4207
62	7.2	6.925	0.2746
63	7.1	7.013	0.08689
64	7	7.069	-0.06894
65	6.9	7.088	-0.1877
66	7	7.172	-0.1721
67	7.5	7.068	0.4324
68	7.6	7.061	0.5386
69	7.5	7.3	0.1998
70	7.3	7.239	0.06097
71	7.3	7.188	0.1117
72	7.4	7.257	0.1428
73	7.7	7.283	0.4172
74	7.8	7.201	0.5991
75	7.7	7.363	0.3366
76	7.5	7.138	0.3622
77	7.3	7.011	0.289
78	7.3	7.123	0.1772
79	7.6	7.015	0.5854
80	7.6	6.895	0.7053

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.05375	0.1075	0.9463
7	0.2617	0.5235	0.7383
8	0.198	0.396	0.802
9	0.1733	0.3466	0.8267
10	0.1847	0.3695	0.8153
11	0.1922	0.3844	0.8078
12	0.2629	0.5258	0.7371
13	0.5252	0.9495	0.4748
14	0.758	0.4841	0.242
15	0.7206	0.5588	0.2794
16	0.6465	0.7071	0.3535
17	0.5699	0.8602	0.4301
18	0.5589	0.8821	0.4411
19	0.7428	0.5143	0.2572
20	0.8019	0.3962	0.1981
21	0.8729	0.2543	0.1271
22	0.9204	0.1593	0.07963
23	0.961	0.07793	0.03897
24	0.9836	0.03283	0.01642
25	0.9939	0.01224	0.006119
26	0.9992	0.001628	0.0008138
27	0.9996	0.0007114	0.0003557
28	1	2.415e-05	1.207e-05
29	1	3.19e-06	1.595e-06
30	1	5.619e-07	2.809e-07
31	1	7.748e-07	3.874e-07
32	1	1.328e-06	6.642e-07
33	1	2.428e-06	1.214e-06
34	1	4.778e-06	2.389e-06
35	1	8.234e-06	4.117e-06
36	1	1.305e-05	6.524e-06
37	1	2.446e-05	1.223e-05
38	1	4.264e-05	2.132e-05
39	1	8.498e-05	4.249e-05
40	0.9999	0.0001286	6.432e-05
41	0.9999	0.0001248	6.239e-05
42	1	6.795e-05	3.397e-05
43	1	3.212e-05	1.606e-05
44	1	3.37e-05	1.685e-05
45	1	4.499e-05	2.25e-05
46	1	6.243e-05	3.122e-05
47	1	7.976e-05	3.988e-05
48	1	5.57e-05	2.785e-05
49	1	3.345e-05	1.672e-05
50	1	4.857e-05	2.429e-05
51	1	7.12e-05	3.56e-05
52	1	9.862e-05	4.931e-05
53	0.9999	0.0001081	5.404e-05
54	0.9999	0.0001548	7.741e-05
55	0.9999	0.0001736	8.679e-05
56	0.9999	0.0002878	0.0001439
57	0.9997	0.0005386	0.0002693
58	0.9995	0.001017	0.0005087
59	0.9993	0.001317	0.0006587
60	0.9989	0.002241	0.001121
61	0.9982	0.00368	0.00184
62	0.9966	0.006826	0.003413
63	0.9935	0.01293	0.006463
64	0.9924	0.01521	0.007604
65	0.9984	0.003265	0.001632
66	0.9995	0.0009488	0.0004744
67	0.9988	0.002302	0.001151
68	0.9974	0.005287	0.002643
69	0.994	0.01197	0.005984
70	0.9871	0.02587	0.01294
71	0.9879	0.02411	0.01206
72	0.9909	0.01821	0.009105
73	0.9832	0.03365	0.01682
74	0.9534	0.09319	0.04659

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.05375 &  0.1075 &  0.9463 \tabularnewline
7 &  0.2617 &  0.5235 &  0.7383 \tabularnewline
8 &  0.198 &  0.396 &  0.802 \tabularnewline
9 &  0.1733 &  0.3466 &  0.8267 \tabularnewline
10 &  0.1847 &  0.3695 &  0.8153 \tabularnewline
11 &  0.1922 &  0.3844 &  0.8078 \tabularnewline
12 &  0.2629 &  0.5258 &  0.7371 \tabularnewline
13 &  0.5252 &  0.9495 &  0.4748 \tabularnewline
14 &  0.758 &  0.4841 &  0.242 \tabularnewline
15 &  0.7206 &  0.5588 &  0.2794 \tabularnewline
16 &  0.6465 &  0.7071 &  0.3535 \tabularnewline
17 &  0.5699 &  0.8602 &  0.4301 \tabularnewline
18 &  0.5589 &  0.8821 &  0.4411 \tabularnewline
19 &  0.7428 &  0.5143 &  0.2572 \tabularnewline
20 &  0.8019 &  0.3962 &  0.1981 \tabularnewline
21 &  0.8729 &  0.2543 &  0.1271 \tabularnewline
22 &  0.9204 &  0.1593 &  0.07963 \tabularnewline
23 &  0.961 &  0.07793 &  0.03897 \tabularnewline
24 &  0.9836 &  0.03283 &  0.01642 \tabularnewline
25 &  0.9939 &  0.01224 &  0.006119 \tabularnewline
26 &  0.9992 &  0.001628 &  0.0008138 \tabularnewline
27 &  0.9996 &  0.0007114 &  0.0003557 \tabularnewline
28 &  1 &  2.415e-05 &  1.207e-05 \tabularnewline
29 &  1 &  3.19e-06 &  1.595e-06 \tabularnewline
30 &  1 &  5.619e-07 &  2.809e-07 \tabularnewline
31 &  1 &  7.748e-07 &  3.874e-07 \tabularnewline
32 &  1 &  1.328e-06 &  6.642e-07 \tabularnewline
33 &  1 &  2.428e-06 &  1.214e-06 \tabularnewline
34 &  1 &  4.778e-06 &  2.389e-06 \tabularnewline
35 &  1 &  8.234e-06 &  4.117e-06 \tabularnewline
36 &  1 &  1.305e-05 &  6.524e-06 \tabularnewline
37 &  1 &  2.446e-05 &  1.223e-05 \tabularnewline
38 &  1 &  4.264e-05 &  2.132e-05 \tabularnewline
39 &  1 &  8.498e-05 &  4.249e-05 \tabularnewline
40 &  0.9999 &  0.0001286 &  6.432e-05 \tabularnewline
41 &  0.9999 &  0.0001248 &  6.239e-05 \tabularnewline
42 &  1 &  6.795e-05 &  3.397e-05 \tabularnewline
43 &  1 &  3.212e-05 &  1.606e-05 \tabularnewline
44 &  1 &  3.37e-05 &  1.685e-05 \tabularnewline
45 &  1 &  4.499e-05 &  2.25e-05 \tabularnewline
46 &  1 &  6.243e-05 &  3.122e-05 \tabularnewline
47 &  1 &  7.976e-05 &  3.988e-05 \tabularnewline
48 &  1 &  5.57e-05 &  2.785e-05 \tabularnewline
49 &  1 &  3.345e-05 &  1.672e-05 \tabularnewline
50 &  1 &  4.857e-05 &  2.429e-05 \tabularnewline
51 &  1 &  7.12e-05 &  3.56e-05 \tabularnewline
52 &  1 &  9.862e-05 &  4.931e-05 \tabularnewline
53 &  0.9999 &  0.0001081 &  5.404e-05 \tabularnewline
54 &  0.9999 &  0.0001548 &  7.741e-05 \tabularnewline
55 &  0.9999 &  0.0001736 &  8.679e-05 \tabularnewline
56 &  0.9999 &  0.0002878 &  0.0001439 \tabularnewline
57 &  0.9997 &  0.0005386 &  0.0002693 \tabularnewline
58 &  0.9995 &  0.001017 &  0.0005087 \tabularnewline
59 &  0.9993 &  0.001317 &  0.0006587 \tabularnewline
60 &  0.9989 &  0.002241 &  0.001121 \tabularnewline
61 &  0.9982 &  0.00368 &  0.00184 \tabularnewline
62 &  0.9966 &  0.006826 &  0.003413 \tabularnewline
63 &  0.9935 &  0.01293 &  0.006463 \tabularnewline
64 &  0.9924 &  0.01521 &  0.007604 \tabularnewline
65 &  0.9984 &  0.003265 &  0.001632 \tabularnewline
66 &  0.9995 &  0.0009488 &  0.0004744 \tabularnewline
67 &  0.9988 &  0.002302 &  0.001151 \tabularnewline
68 &  0.9974 &  0.005287 &  0.002643 \tabularnewline
69 &  0.994 &  0.01197 &  0.005984 \tabularnewline
70 &  0.9871 &  0.02587 &  0.01294 \tabularnewline
71 &  0.9879 &  0.02411 &  0.01206 \tabularnewline
72 &  0.9909 &  0.01821 &  0.009105 \tabularnewline
73 &  0.9832 &  0.03365 &  0.01682 \tabularnewline
74 &  0.9534 &  0.09319 &  0.04659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&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]6[/C][C] 0.05375[/C][C] 0.1075[/C][C] 0.9463[/C][/ROW]
[ROW][C]7[/C][C] 0.2617[/C][C] 0.5235[/C][C] 0.7383[/C][/ROW]
[ROW][C]8[/C][C] 0.198[/C][C] 0.396[/C][C] 0.802[/C][/ROW]
[ROW][C]9[/C][C] 0.1733[/C][C] 0.3466[/C][C] 0.8267[/C][/ROW]
[ROW][C]10[/C][C] 0.1847[/C][C] 0.3695[/C][C] 0.8153[/C][/ROW]
[ROW][C]11[/C][C] 0.1922[/C][C] 0.3844[/C][C] 0.8078[/C][/ROW]
[ROW][C]12[/C][C] 0.2629[/C][C] 0.5258[/C][C] 0.7371[/C][/ROW]
[ROW][C]13[/C][C] 0.5252[/C][C] 0.9495[/C][C] 0.4748[/C][/ROW]
[ROW][C]14[/C][C] 0.758[/C][C] 0.4841[/C][C] 0.242[/C][/ROW]
[ROW][C]15[/C][C] 0.7206[/C][C] 0.5588[/C][C] 0.2794[/C][/ROW]
[ROW][C]16[/C][C] 0.6465[/C][C] 0.7071[/C][C] 0.3535[/C][/ROW]
[ROW][C]17[/C][C] 0.5699[/C][C] 0.8602[/C][C] 0.4301[/C][/ROW]
[ROW][C]18[/C][C] 0.5589[/C][C] 0.8821[/C][C] 0.4411[/C][/ROW]
[ROW][C]19[/C][C] 0.7428[/C][C] 0.5143[/C][C] 0.2572[/C][/ROW]
[ROW][C]20[/C][C] 0.8019[/C][C] 0.3962[/C][C] 0.1981[/C][/ROW]
[ROW][C]21[/C][C] 0.8729[/C][C] 0.2543[/C][C] 0.1271[/C][/ROW]
[ROW][C]22[/C][C] 0.9204[/C][C] 0.1593[/C][C] 0.07963[/C][/ROW]
[ROW][C]23[/C][C] 0.961[/C][C] 0.07793[/C][C] 0.03897[/C][/ROW]
[ROW][C]24[/C][C] 0.9836[/C][C] 0.03283[/C][C] 0.01642[/C][/ROW]
[ROW][C]25[/C][C] 0.9939[/C][C] 0.01224[/C][C] 0.006119[/C][/ROW]
[ROW][C]26[/C][C] 0.9992[/C][C] 0.001628[/C][C] 0.0008138[/C][/ROW]
[ROW][C]27[/C][C] 0.9996[/C][C] 0.0007114[/C][C] 0.0003557[/C][/ROW]
[ROW][C]28[/C][C] 1[/C][C] 2.415e-05[/C][C] 1.207e-05[/C][/ROW]
[ROW][C]29[/C][C] 1[/C][C] 3.19e-06[/C][C] 1.595e-06[/C][/ROW]
[ROW][C]30[/C][C] 1[/C][C] 5.619e-07[/C][C] 2.809e-07[/C][/ROW]
[ROW][C]31[/C][C] 1[/C][C] 7.748e-07[/C][C] 3.874e-07[/C][/ROW]
[ROW][C]32[/C][C] 1[/C][C] 1.328e-06[/C][C] 6.642e-07[/C][/ROW]
[ROW][C]33[/C][C] 1[/C][C] 2.428e-06[/C][C] 1.214e-06[/C][/ROW]
[ROW][C]34[/C][C] 1[/C][C] 4.778e-06[/C][C] 2.389e-06[/C][/ROW]
[ROW][C]35[/C][C] 1[/C][C] 8.234e-06[/C][C] 4.117e-06[/C][/ROW]
[ROW][C]36[/C][C] 1[/C][C] 1.305e-05[/C][C] 6.524e-06[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 2.446e-05[/C][C] 1.223e-05[/C][/ROW]
[ROW][C]38[/C][C] 1[/C][C] 4.264e-05[/C][C] 2.132e-05[/C][/ROW]
[ROW][C]39[/C][C] 1[/C][C] 8.498e-05[/C][C] 4.249e-05[/C][/ROW]
[ROW][C]40[/C][C] 0.9999[/C][C] 0.0001286[/C][C] 6.432e-05[/C][/ROW]
[ROW][C]41[/C][C] 0.9999[/C][C] 0.0001248[/C][C] 6.239e-05[/C][/ROW]
[ROW][C]42[/C][C] 1[/C][C] 6.795e-05[/C][C] 3.397e-05[/C][/ROW]
[ROW][C]43[/C][C] 1[/C][C] 3.212e-05[/C][C] 1.606e-05[/C][/ROW]
[ROW][C]44[/C][C] 1[/C][C] 3.37e-05[/C][C] 1.685e-05[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 4.499e-05[/C][C] 2.25e-05[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 6.243e-05[/C][C] 3.122e-05[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 7.976e-05[/C][C] 3.988e-05[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 5.57e-05[/C][C] 2.785e-05[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 3.345e-05[/C][C] 1.672e-05[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 4.857e-05[/C][C] 2.429e-05[/C][/ROW]
[ROW][C]51[/C][C] 1[/C][C] 7.12e-05[/C][C] 3.56e-05[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 9.862e-05[/C][C] 4.931e-05[/C][/ROW]
[ROW][C]53[/C][C] 0.9999[/C][C] 0.0001081[/C][C] 5.404e-05[/C][/ROW]
[ROW][C]54[/C][C] 0.9999[/C][C] 0.0001548[/C][C] 7.741e-05[/C][/ROW]
[ROW][C]55[/C][C] 0.9999[/C][C] 0.0001736[/C][C] 8.679e-05[/C][/ROW]
[ROW][C]56[/C][C] 0.9999[/C][C] 0.0002878[/C][C] 0.0001439[/C][/ROW]
[ROW][C]57[/C][C] 0.9997[/C][C] 0.0005386[/C][C] 0.0002693[/C][/ROW]
[ROW][C]58[/C][C] 0.9995[/C][C] 0.001017[/C][C] 0.0005087[/C][/ROW]
[ROW][C]59[/C][C] 0.9993[/C][C] 0.001317[/C][C] 0.0006587[/C][/ROW]
[ROW][C]60[/C][C] 0.9989[/C][C] 0.002241[/C][C] 0.001121[/C][/ROW]
[ROW][C]61[/C][C] 0.9982[/C][C] 0.00368[/C][C] 0.00184[/C][/ROW]
[ROW][C]62[/C][C] 0.9966[/C][C] 0.006826[/C][C] 0.003413[/C][/ROW]
[ROW][C]63[/C][C] 0.9935[/C][C] 0.01293[/C][C] 0.006463[/C][/ROW]
[ROW][C]64[/C][C] 0.9924[/C][C] 0.01521[/C][C] 0.007604[/C][/ROW]
[ROW][C]65[/C][C] 0.9984[/C][C] 0.003265[/C][C] 0.001632[/C][/ROW]
[ROW][C]66[/C][C] 0.9995[/C][C] 0.0009488[/C][C] 0.0004744[/C][/ROW]
[ROW][C]67[/C][C] 0.9988[/C][C] 0.002302[/C][C] 0.001151[/C][/ROW]
[ROW][C]68[/C][C] 0.9974[/C][C] 0.005287[/C][C] 0.002643[/C][/ROW]
[ROW][C]69[/C][C] 0.994[/C][C] 0.01197[/C][C] 0.005984[/C][/ROW]
[ROW][C]70[/C][C] 0.9871[/C][C] 0.02587[/C][C] 0.01294[/C][/ROW]
[ROW][C]71[/C][C] 0.9879[/C][C] 0.02411[/C][C] 0.01206[/C][/ROW]
[ROW][C]72[/C][C] 0.9909[/C][C] 0.01821[/C][C] 0.009105[/C][/ROW]
[ROW][C]73[/C][C] 0.9832[/C][C] 0.03365[/C][C] 0.01682[/C][/ROW]
[ROW][C]74[/C][C] 0.9534[/C][C] 0.09319[/C][C] 0.04659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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
6	0.05375	0.1075	0.9463
7	0.2617	0.5235	0.7383
8	0.198	0.396	0.802
9	0.1733	0.3466	0.8267
10	0.1847	0.3695	0.8153
11	0.1922	0.3844	0.8078
12	0.2629	0.5258	0.7371
13	0.5252	0.9495	0.4748
14	0.758	0.4841	0.242
15	0.7206	0.5588	0.2794
16	0.6465	0.7071	0.3535
17	0.5699	0.8602	0.4301
18	0.5589	0.8821	0.4411
19	0.7428	0.5143	0.2572
20	0.8019	0.3962	0.1981
21	0.8729	0.2543	0.1271
22	0.9204	0.1593	0.07963
23	0.961	0.07793	0.03897
24	0.9836	0.03283	0.01642
25	0.9939	0.01224	0.006119
26	0.9992	0.001628	0.0008138
27	0.9996	0.0007114	0.0003557
28	1	2.415e-05	1.207e-05
29	1	3.19e-06	1.595e-06
30	1	5.619e-07	2.809e-07
31	1	7.748e-07	3.874e-07
32	1	1.328e-06	6.642e-07
33	1	2.428e-06	1.214e-06
34	1	4.778e-06	2.389e-06
35	1	8.234e-06	4.117e-06
36	1	1.305e-05	6.524e-06
37	1	2.446e-05	1.223e-05
38	1	4.264e-05	2.132e-05
39	1	8.498e-05	4.249e-05
40	0.9999	0.0001286	6.432e-05
41	0.9999	0.0001248	6.239e-05
42	1	6.795e-05	3.397e-05
43	1	3.212e-05	1.606e-05
44	1	3.37e-05	1.685e-05
45	1	4.499e-05	2.25e-05
46	1	6.243e-05	3.122e-05
47	1	7.976e-05	3.988e-05
48	1	5.57e-05	2.785e-05
49	1	3.345e-05	1.672e-05
50	1	4.857e-05	2.429e-05
51	1	7.12e-05	3.56e-05
52	1	9.862e-05	4.931e-05
53	0.9999	0.0001081	5.404e-05
54	0.9999	0.0001548	7.741e-05
55	0.9999	0.0001736	8.679e-05
56	0.9999	0.0002878	0.0001439
57	0.9997	0.0005386	0.0002693
58	0.9995	0.001017	0.0005087
59	0.9993	0.001317	0.0006587
60	0.9989	0.002241	0.001121
61	0.9982	0.00368	0.00184
62	0.9966	0.006826	0.003413
63	0.9935	0.01293	0.006463
64	0.9924	0.01521	0.007604
65	0.9984	0.003265	0.001632
66	0.9995	0.0009488	0.0004744
67	0.9988	0.002302	0.001151
68	0.9974	0.005287	0.002643
69	0.994	0.01197	0.005984
70	0.9871	0.02587	0.01294
71	0.9879	0.02411	0.01206
72	0.9909	0.01821	0.009105
73	0.9832	0.03365	0.01682
74	0.9534	0.09319	0.04659

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	41	0.5942	NOK
5% type I error level	50	0.724638	NOK
10% type I error level	52	0.753623	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 & 41 &  0.5942 & NOK \tabularnewline
5% type I error level & 50 & 0.724638 & NOK \tabularnewline
10% type I error level & 52 & 0.753623 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284313&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]41[/C][C] 0.5942[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]50[/C][C]0.724638[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.753623[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284313&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284313&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	41	0.5942	NOK
5% type I error level	50	0.724638	NOK
10% type I error level	52	0.753623	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):

Parameters (R input):

par1 = 1 ; 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