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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 13 Dec 2014 09:50:11 +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/2014/Dec/13/t14184642253nei24cxwvlctbp.htm/, Retrieved Thu, 16 May 2024 14:06:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266926, Retrieved Thu, 16 May 2024 14:06:03 +0000

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Estimated Impact

135

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-       [Multiple Regression] [] [2014-12-13 09:50:11] [6baf0af87d9d8aa2cb91b54f39a0a5b0] [Current]

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21 26 50 0 21 13 12 13 13
22 57 62 1 22 8 8 13 16
22 37 54 0 21 14 11 11 11
18 67 71 1 21 16 13 14 10
23 43 54 1 21 14 11 15 9
12 52 65 1 21 13 10 14 8
20 52 73 0 21 15 7 11 26
22 43 52 1 23 13 10 13 10
21 84 84 1 22 20 15 16 10
19 67 42 1 25 17 12 14 8
22 49 66 1 21 15 12 14 13
15 70 65 1 23 16 10 15 11
20 52 78 1 22 12 10 15 8
19 58 73 0 21 17 14 13 12
18 68 75 0 21 11 6 14 24
15 62 72 0 25 16 12 11 21
20 43 66 1 21 16 14 12 5
21 56 70 0 21 15 11 14 14
21 56 61 1 20 13 8 13 11
15 74 81 0 24 14 12 12 9
16 65 71 1 23 19 15 15 8
23 63 69 1 21 16 13 15 17
21 58 71 0 24 17 11 14 18
18 57 72 1 23 10 12 14 16
25 63 68 1 21 15 7 12 23
9 53 70 1 22 14 11 12 9
30 57 68 1 20 14 7 12 14
20 51 61 0 18 16 12 15 13
23 64 67 1 21 15 12 14 10
16 53 76 0 22 17 13 16 8
16 29 70 0 22 14 9 12 10
19 54 60 0 21 16 11 12 19
25 58 72 1 21 15 12 14 11
18 43 69 1 25 16 15 16 16
23 51 71 1 22 16 12 15 12
21 53 62 1 22 10 6 12 11
10 54 70 0 20 8 5 14 11
14 56 64 1 21 17 13 13 10
22 61 58 1 21 14 11 14 13
26 47 76 0 21 10 6 16 14
23 39 52 1 22 14 12 12 8
23 48 59 1 21 12 10 14 11
24 50 68 1 24 16 6 15 11
24 35 76 1 22 16 12 13 13
18 30 65 1 22 16 11 16 15
23 68 67 0 21 8 6 16 15
15 49 59 1 22 16 12 12 16
19 61 69 1 19 15 12 12 12
16 67 76 0 22 8 8 16 12
25 47 63 1 23 13 10 12 17
23 56 75 1 20 14 11 15 14
17 50 63 1 20 13 7 12 15
19 43 60 1 23 16 12 13 12
21 67 73 1 20 19 13 12 13
18 62 63 1 23 19 14 14 7
27 57 70 1 21 14 12 14 8
21 41 75 0 22 15 6 11 16
13 54 66 1 21 13 14 10 20
8 45 63 0 21 10 10 12 14
29 48 63 1 19 16 12 11 10
28 61 64 1 22 15 11 16 16
23 56 70 0 21 11 10 14 11
21 41 75 0 21 9 7 14 26
19 43 61 1 21 16 12 15 9
19 53 60 0 21 12 7 15 15
20 44 62 1 21 12 12 14 12
18 66 73 0 21 14 12 13 21
19 58 61 1 22 14 10 11 20
17 46 66 1 22 13 10 16 20
19 37 64 0 18 15 12 12 10
25 51 59 0 21 17 12 15 15
19 51 64 0 23 14 12 14 10
22 56 60 0 19 11 8 15 16
23 66 56 1 19 9 10 14 9
14 37 78 0 21 7 5 13 17
28 59 53 1 21 13 10 6 10
16 42 67 0 21 15 10 12 19
24 38 59 1 21 12 12 12 13
20 66 66 0 20 15 11 14 8
12 34 68 0 19 14 9 14 11
24 53 71 1 21 16 12 15 9
22 49 66 0 19 14 11 11 12
12 55 73 0 19 13 10 13 10
22 49 72 0 19 16 12 14 9
20 59 71 1 20 13 10 16 14
10 40 59 0 19 16 9 13 14
23 58 64 1 19 16 11 14 10
17 60 66 1 19 16 12 16 8
22 63 78 0 20 10 7 11 13
24 56 68 0 19 12 11 13 9
18 54 73 0 18 12 12 13 14
21 52 62 1 19 12 6 15 8
20 34 65 1 21 12 9 12 16
20 69 68 1 18 19 15 13 14
22 32 65 0 18 14 10 12 14
19 48 60 1 19 13 11 14 8
20 67 71 0 21 16 12 14 11
26 58 65 1 20 15 12 16 11
23 57 68 1 24 12 12 15 13
24 42 64 1 22 8 11 14 12
21 64 74 1 21 10 9 13 13
21 58 69 1 21 16 11 14 9
19 66 76 0 19 16 12 15 10
8 26 68 1 19 10 12 14 12
17 61 72 1 20 18 14 12 11
20 52 67 1 18 12 8 7 13
11 51 63 0 19 16 10 12 17
8 55 59 0 19 10 9 15 15
15 50 73 0 20 14 10 12 15
18 60 66 0 21 12 9 13 14
18 56 62 0 18 11 10 11 10
19 63 69 0 19 15 12 14 15
19 61 66 1 19 7 11 13 14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
NUMERACYTOT[t] = + 20.4702 + 0.0432288AMS.I[t] -0.0515985AMS.E[t] + 2.494gender[t] -0.0577043age[t] + 0.198896CONFSTATTOT[t] -0.335254CONFSOFTTOT[t] + 0.0558379STRESSTOT[t] + 0.00596632CESDTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NUMERACYTOT[t] =  +  20.4702 +  0.0432288AMS.I[t] -0.0515985AMS.E[t] +  2.494gender[t] -0.0577043age[t] +  0.198896CONFSTATTOT[t] -0.335254CONFSOFTTOT[t] +  0.0558379STRESSTOT[t] +  0.00596632CESDTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NUMERACYTOT[t] =  +  20.4702 +  0.0432288AMS.I[t] -0.0515985AMS.E[t] +  2.494gender[t] -0.0577043age[t] +  0.198896CONFSTATTOT[t] -0.335254CONFSOFTTOT[t] +  0.0558379STRESSTOT[t] +  0.00596632CESDTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&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
NUMERACYTOT[t] = + 20.4702 + 0.0432288AMS.I[t] -0.0515985AMS.E[t] + 2.494gender[t] -0.0577043age[t] + 0.198896CONFSTATTOT[t] -0.335254CONFSOFTTOT[t] + 0.0558379STRESSTOT[t] + 0.00596632CESDTOT[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)	20.4702	7.1158	2.877	0.00487609	0.00243804
AMS.I	0.0432288	0.0413383	1.046	0.298109	0.149055
AMS.E	-0.0515985	0.0660056	-0.7817	0.43615	0.218075
gender	2.494	0.92329	2.701	0.00806793	0.00403397
age	-0.0577043	0.273498	-0.211	0.833311	0.416655
CONFSTATTOT	0.198896	0.191577	1.038	0.301583	0.150791
CONFSOFTTOT	-0.335254	0.241761	-1.387	0.168493	0.0842467
STRESSTOT	0.0558379	0.246827	0.2262	0.821472	0.410736
CESDTOT	0.00596632	0.114062	0.05231	0.958384	0.479192

\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) & 20.4702 & 7.1158 & 2.877 & 0.00487609 & 0.00243804 \tabularnewline
AMS.I & 0.0432288 & 0.0413383 & 1.046 & 0.298109 & 0.149055 \tabularnewline
AMS.E & -0.0515985 & 0.0660056 & -0.7817 & 0.43615 & 0.218075 \tabularnewline
gender & 2.494 & 0.92329 & 2.701 & 0.00806793 & 0.00403397 \tabularnewline
age & -0.0577043 & 0.273498 & -0.211 & 0.833311 & 0.416655 \tabularnewline
CONFSTATTOT & 0.198896 & 0.191577 & 1.038 & 0.301583 & 0.150791 \tabularnewline
CONFSOFTTOT & -0.335254 & 0.241761 & -1.387 & 0.168493 & 0.0842467 \tabularnewline
STRESSTOT & 0.0558379 & 0.246827 & 0.2262 & 0.821472 & 0.410736 \tabularnewline
CESDTOT & 0.00596632 & 0.114062 & 0.05231 & 0.958384 & 0.479192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&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]20.4702[/C][C]7.1158[/C][C]2.877[/C][C]0.00487609[/C][C]0.00243804[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0432288[/C][C]0.0413383[/C][C]1.046[/C][C]0.298109[/C][C]0.149055[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0515985[/C][C]0.0660056[/C][C]-0.7817[/C][C]0.43615[/C][C]0.218075[/C][/ROW]
[ROW][C]gender[/C][C]2.494[/C][C]0.92329[/C][C]2.701[/C][C]0.00806793[/C][C]0.00403397[/C][/ROW]
[ROW][C]age[/C][C]-0.0577043[/C][C]0.273498[/C][C]-0.211[/C][C]0.833311[/C][C]0.416655[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.198896[/C][C]0.191577[/C][C]1.038[/C][C]0.301583[/C][C]0.150791[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]-0.335254[/C][C]0.241761[/C][C]-1.387[/C][C]0.168493[/C][C]0.0842467[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]0.0558379[/C][C]0.246827[/C][C]0.2262[/C][C]0.821472[/C][C]0.410736[/C][/ROW]
[ROW][C]CESDTOT[/C][C]0.00596632[/C][C]0.114062[/C][C]0.05231[/C][C]0.958384[/C][C]0.479192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&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)	20.4702	7.1158	2.877	0.00487609	0.00243804
AMS.I	0.0432288	0.0413383	1.046	0.298109	0.149055
AMS.E	-0.0515985	0.0660056	-0.7817	0.43615	0.218075
gender	2.494	0.92329	2.701	0.00806793	0.00403397
age	-0.0577043	0.273498	-0.211	0.833311	0.416655
CONFSTATTOT	0.198896	0.191577	1.038	0.301583	0.150791
CONFSOFTTOT	-0.335254	0.241761	-1.387	0.168493	0.0842467
STRESSTOT	0.0558379	0.246827	0.2262	0.821472	0.410736
CESDTOT	0.00596632	0.114062	0.05231	0.958384	0.479192

Multiple Linear Regression - Regression Statistics
Multiple R	0.320816
R-squared	0.102923
Adjusted R-squared	0.0339168
F-TEST (value)	1.49151
F-TEST (DF numerator)	8
F-TEST (DF denominator)	104
p-value	0.16917
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.36696
Sum Squared Residuals	1983.32

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.320816 \tabularnewline
R-squared & 0.102923 \tabularnewline
Adjusted R-squared & 0.0339168 \tabularnewline
F-TEST (value) & 1.49151 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 104 \tabularnewline
p-value & 0.16917 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.36696 \tabularnewline
Sum Squared Residuals & 1983.32 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.320816[/C][/ROW]
[ROW][C]R-squared[/C][C]0.102923[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0339168[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.49151[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]104[/C][/ROW]
[ROW][C]p-value[/C][C]0.16917[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.36696[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1983.32[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&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	0.320816
R-squared	0.102923
Adjusted R-squared	0.0339168
F-TEST (value)	1.49151
F-TEST (DF numerator)	8
F-TEST (DF denominator)	104
p-value	0.16917
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.36696
Sum Squared Residuals	1983.32

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	21	17.1685	3.83153
2	22	20.6901	1.30988
3	22	17.8481	4.15186
4	18	20.6507	-2.65066
5	23	20.8129	2.18707
6	12	20.709	-8.70896
7	20	19.1456	0.854392
8	22	20.8314	1.16863
9	21	20.8938	0.106181
10	19	22.4384	-3.43842
11	22	20.2848	1.71521
12	15	22.0421	-7.0421
13	20	19.8374	0.162579
14	19	17.4841	1.51586
15	18	19.4293	-1.42932
16	15	17.8915	-2.89147
17	20	19.3944	0.605598
18	21	18.2282	2.77178
19	21	21.7785	-0.778547
20	15	17.59	-2.58998
21	16	20.4189	-4.41888
22	23	20.6785	2.32145
23	21	18.5116	2.48838
24	18	19.229	-1.22904
25	25	22.4111	2.58894
26	9	20.1944	-11.1944
27	30	21.9568	8.0432
28	20	18.5631	1.43691
29	23	20.8637	2.13627
30	16	17.5344	-1.5344
31	16	17.3394	-1.33941
32	19	18.7748	0.225202
33	25	20.3523	4.64767
34	18	18.9625	-0.962516
35	23	20.3043	2.69568
36	21	21.4998	-0.499837
37	10	18.8008	-8.80082
38	14	20.6794	-6.67939
39	22	21.3527	0.647314
40	26	18.323	7.67696
41	23	20.1768	2.82322
42	23	20.6646	2.33536
43	24	22.306	1.69396
44	24	19.249	4.75104
45	18	20.1151	-2.1151
46	23	19.3034	3.6966
47	15	20.6934	-5.6934
48	19	20.6465	-1.64651
49	16	18.0497	-2.04968
50	25	20.4226	4.57737
51	23	20.3789	2.62112
52	17	21.7193	-4.71926
53	19	20.3567	-1.35669
54	21	21.1081	-0.108081
55	18	20.9754	-2.97543
56	27	20.1955	6.8045
57	21	18.7848	2.21522
58	13	19.251	-6.25105
59	8	17.343	-9.34299
60	29	20.5253	8.47475
61	28	21.3139	6.68614
62	23	17.75	5.25001
63	21	17.541	3.45897
64	19	20.5143	-1.51428
65	19	19.4207	-0.420652
66	20	19.6724	0.327611
67	18	17.9575	0.0425119
68	19	21.22	-2.22
69	17	20.5236	-3.52356
70	19	17.4188	1.58122
71	25	18.704	6.296
72	19	17.6482	1.35176
73	22	19.1376	2.86244
74	23	21.1043	1.89566
75	14	17.3765	-3.3765
76	28	21.196	6.80402
77	16	18.0312	-2.03122
78	24	19.4621	4.5379
79	20	18.8888	1.11119
80	12	17.9495	-5.94951
81	24	20.4306	3.56941
82	22	17.8691	4.13092
83	12	18.0034	-6.00336
84	22	17.7716	4.22836
85	20	20.9072	-0.907153
86	10	19.0331	-9.03312
87	23	21.4087	1.59129
88	17	21.1565	-4.15646
89	22	18.3488	3.6512
90	24	17.7645	6.23553
91	18	17.1723	0.827701
92	21	22.1771	-1.17713
93	20	20.0033	-0.0032585
94	20	20.9592	-0.959235
95	22	17.6465	4.35348
96	19	20.5742	-1.5742
97	20	18.4979	1.50212
98	26	20.8829	5.1171
99	23	19.8135	3.18654
100	24	18.9647	5.0353
101	21	20.4759	0.524116
102	21	21.0293	-0.0293426
103	19	18.3619	0.63806
104	8	18.3023	-10.3023
105	17	20.3542	-3.35422
106	20	20.8895	-0.889459
107	11	18.929	-7.92905
108	8	18.6058	-10.6058
109	15	17.9024	-2.9024
110	18	18.6255	-0.625512
111	18	18.1624	-0.162412
112	19	18.3685	0.63146
113	19	19.6132	-0.613163

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21 & 17.1685 & 3.83153 \tabularnewline
2 & 22 & 20.6901 & 1.30988 \tabularnewline
3 & 22 & 17.8481 & 4.15186 \tabularnewline
4 & 18 & 20.6507 & -2.65066 \tabularnewline
5 & 23 & 20.8129 & 2.18707 \tabularnewline
6 & 12 & 20.709 & -8.70896 \tabularnewline
7 & 20 & 19.1456 & 0.854392 \tabularnewline
8 & 22 & 20.8314 & 1.16863 \tabularnewline
9 & 21 & 20.8938 & 0.106181 \tabularnewline
10 & 19 & 22.4384 & -3.43842 \tabularnewline
11 & 22 & 20.2848 & 1.71521 \tabularnewline
12 & 15 & 22.0421 & -7.0421 \tabularnewline
13 & 20 & 19.8374 & 0.162579 \tabularnewline
14 & 19 & 17.4841 & 1.51586 \tabularnewline
15 & 18 & 19.4293 & -1.42932 \tabularnewline
16 & 15 & 17.8915 & -2.89147 \tabularnewline
17 & 20 & 19.3944 & 0.605598 \tabularnewline
18 & 21 & 18.2282 & 2.77178 \tabularnewline
19 & 21 & 21.7785 & -0.778547 \tabularnewline
20 & 15 & 17.59 & -2.58998 \tabularnewline
21 & 16 & 20.4189 & -4.41888 \tabularnewline
22 & 23 & 20.6785 & 2.32145 \tabularnewline
23 & 21 & 18.5116 & 2.48838 \tabularnewline
24 & 18 & 19.229 & -1.22904 \tabularnewline
25 & 25 & 22.4111 & 2.58894 \tabularnewline
26 & 9 & 20.1944 & -11.1944 \tabularnewline
27 & 30 & 21.9568 & 8.0432 \tabularnewline
28 & 20 & 18.5631 & 1.43691 \tabularnewline
29 & 23 & 20.8637 & 2.13627 \tabularnewline
30 & 16 & 17.5344 & -1.5344 \tabularnewline
31 & 16 & 17.3394 & -1.33941 \tabularnewline
32 & 19 & 18.7748 & 0.225202 \tabularnewline
33 & 25 & 20.3523 & 4.64767 \tabularnewline
34 & 18 & 18.9625 & -0.962516 \tabularnewline
35 & 23 & 20.3043 & 2.69568 \tabularnewline
36 & 21 & 21.4998 & -0.499837 \tabularnewline
37 & 10 & 18.8008 & -8.80082 \tabularnewline
38 & 14 & 20.6794 & -6.67939 \tabularnewline
39 & 22 & 21.3527 & 0.647314 \tabularnewline
40 & 26 & 18.323 & 7.67696 \tabularnewline
41 & 23 & 20.1768 & 2.82322 \tabularnewline
42 & 23 & 20.6646 & 2.33536 \tabularnewline
43 & 24 & 22.306 & 1.69396 \tabularnewline
44 & 24 & 19.249 & 4.75104 \tabularnewline
45 & 18 & 20.1151 & -2.1151 \tabularnewline
46 & 23 & 19.3034 & 3.6966 \tabularnewline
47 & 15 & 20.6934 & -5.6934 \tabularnewline
48 & 19 & 20.6465 & -1.64651 \tabularnewline
49 & 16 & 18.0497 & -2.04968 \tabularnewline
50 & 25 & 20.4226 & 4.57737 \tabularnewline
51 & 23 & 20.3789 & 2.62112 \tabularnewline
52 & 17 & 21.7193 & -4.71926 \tabularnewline
53 & 19 & 20.3567 & -1.35669 \tabularnewline
54 & 21 & 21.1081 & -0.108081 \tabularnewline
55 & 18 & 20.9754 & -2.97543 \tabularnewline
56 & 27 & 20.1955 & 6.8045 \tabularnewline
57 & 21 & 18.7848 & 2.21522 \tabularnewline
58 & 13 & 19.251 & -6.25105 \tabularnewline
59 & 8 & 17.343 & -9.34299 \tabularnewline
60 & 29 & 20.5253 & 8.47475 \tabularnewline
61 & 28 & 21.3139 & 6.68614 \tabularnewline
62 & 23 & 17.75 & 5.25001 \tabularnewline
63 & 21 & 17.541 & 3.45897 \tabularnewline
64 & 19 & 20.5143 & -1.51428 \tabularnewline
65 & 19 & 19.4207 & -0.420652 \tabularnewline
66 & 20 & 19.6724 & 0.327611 \tabularnewline
67 & 18 & 17.9575 & 0.0425119 \tabularnewline
68 & 19 & 21.22 & -2.22 \tabularnewline
69 & 17 & 20.5236 & -3.52356 \tabularnewline
70 & 19 & 17.4188 & 1.58122 \tabularnewline
71 & 25 & 18.704 & 6.296 \tabularnewline
72 & 19 & 17.6482 & 1.35176 \tabularnewline
73 & 22 & 19.1376 & 2.86244 \tabularnewline
74 & 23 & 21.1043 & 1.89566 \tabularnewline
75 & 14 & 17.3765 & -3.3765 \tabularnewline
76 & 28 & 21.196 & 6.80402 \tabularnewline
77 & 16 & 18.0312 & -2.03122 \tabularnewline
78 & 24 & 19.4621 & 4.5379 \tabularnewline
79 & 20 & 18.8888 & 1.11119 \tabularnewline
80 & 12 & 17.9495 & -5.94951 \tabularnewline
81 & 24 & 20.4306 & 3.56941 \tabularnewline
82 & 22 & 17.8691 & 4.13092 \tabularnewline
83 & 12 & 18.0034 & -6.00336 \tabularnewline
84 & 22 & 17.7716 & 4.22836 \tabularnewline
85 & 20 & 20.9072 & -0.907153 \tabularnewline
86 & 10 & 19.0331 & -9.03312 \tabularnewline
87 & 23 & 21.4087 & 1.59129 \tabularnewline
88 & 17 & 21.1565 & -4.15646 \tabularnewline
89 & 22 & 18.3488 & 3.6512 \tabularnewline
90 & 24 & 17.7645 & 6.23553 \tabularnewline
91 & 18 & 17.1723 & 0.827701 \tabularnewline
92 & 21 & 22.1771 & -1.17713 \tabularnewline
93 & 20 & 20.0033 & -0.0032585 \tabularnewline
94 & 20 & 20.9592 & -0.959235 \tabularnewline
95 & 22 & 17.6465 & 4.35348 \tabularnewline
96 & 19 & 20.5742 & -1.5742 \tabularnewline
97 & 20 & 18.4979 & 1.50212 \tabularnewline
98 & 26 & 20.8829 & 5.1171 \tabularnewline
99 & 23 & 19.8135 & 3.18654 \tabularnewline
100 & 24 & 18.9647 & 5.0353 \tabularnewline
101 & 21 & 20.4759 & 0.524116 \tabularnewline
102 & 21 & 21.0293 & -0.0293426 \tabularnewline
103 & 19 & 18.3619 & 0.63806 \tabularnewline
104 & 8 & 18.3023 & -10.3023 \tabularnewline
105 & 17 & 20.3542 & -3.35422 \tabularnewline
106 & 20 & 20.8895 & -0.889459 \tabularnewline
107 & 11 & 18.929 & -7.92905 \tabularnewline
108 & 8 & 18.6058 & -10.6058 \tabularnewline
109 & 15 & 17.9024 & -2.9024 \tabularnewline
110 & 18 & 18.6255 & -0.625512 \tabularnewline
111 & 18 & 18.1624 & -0.162412 \tabularnewline
112 & 19 & 18.3685 & 0.63146 \tabularnewline
113 & 19 & 19.6132 & -0.613163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&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]21[/C][C]17.1685[/C][C]3.83153[/C][/ROW]
[ROW][C]2[/C][C]22[/C][C]20.6901[/C][C]1.30988[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]17.8481[/C][C]4.15186[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]20.6507[/C][C]-2.65066[/C][/ROW]
[ROW][C]5[/C][C]23[/C][C]20.8129[/C][C]2.18707[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]20.709[/C][C]-8.70896[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]19.1456[/C][C]0.854392[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.8314[/C][C]1.16863[/C][/ROW]
[ROW][C]9[/C][C]21[/C][C]20.8938[/C][C]0.106181[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]22.4384[/C][C]-3.43842[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]20.2848[/C][C]1.71521[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]22.0421[/C][C]-7.0421[/C][/ROW]
[ROW][C]13[/C][C]20[/C][C]19.8374[/C][C]0.162579[/C][/ROW]
[ROW][C]14[/C][C]19[/C][C]17.4841[/C][C]1.51586[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]19.4293[/C][C]-1.42932[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]17.8915[/C][C]-2.89147[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]19.3944[/C][C]0.605598[/C][/ROW]
[ROW][C]18[/C][C]21[/C][C]18.2282[/C][C]2.77178[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]21.7785[/C][C]-0.778547[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]17.59[/C][C]-2.58998[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]20.4189[/C][C]-4.41888[/C][/ROW]
[ROW][C]22[/C][C]23[/C][C]20.6785[/C][C]2.32145[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]18.5116[/C][C]2.48838[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]19.229[/C][C]-1.22904[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]22.4111[/C][C]2.58894[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]20.1944[/C][C]-11.1944[/C][/ROW]
[ROW][C]27[/C][C]30[/C][C]21.9568[/C][C]8.0432[/C][/ROW]
[ROW][C]28[/C][C]20[/C][C]18.5631[/C][C]1.43691[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]20.8637[/C][C]2.13627[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]17.5344[/C][C]-1.5344[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]17.3394[/C][C]-1.33941[/C][/ROW]
[ROW][C]32[/C][C]19[/C][C]18.7748[/C][C]0.225202[/C][/ROW]
[ROW][C]33[/C][C]25[/C][C]20.3523[/C][C]4.64767[/C][/ROW]
[ROW][C]34[/C][C]18[/C][C]18.9625[/C][C]-0.962516[/C][/ROW]
[ROW][C]35[/C][C]23[/C][C]20.3043[/C][C]2.69568[/C][/ROW]
[ROW][C]36[/C][C]21[/C][C]21.4998[/C][C]-0.499837[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]18.8008[/C][C]-8.80082[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]20.6794[/C][C]-6.67939[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]21.3527[/C][C]0.647314[/C][/ROW]
[ROW][C]40[/C][C]26[/C][C]18.323[/C][C]7.67696[/C][/ROW]
[ROW][C]41[/C][C]23[/C][C]20.1768[/C][C]2.82322[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]20.6646[/C][C]2.33536[/C][/ROW]
[ROW][C]43[/C][C]24[/C][C]22.306[/C][C]1.69396[/C][/ROW]
[ROW][C]44[/C][C]24[/C][C]19.249[/C][C]4.75104[/C][/ROW]
[ROW][C]45[/C][C]18[/C][C]20.1151[/C][C]-2.1151[/C][/ROW]
[ROW][C]46[/C][C]23[/C][C]19.3034[/C][C]3.6966[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]20.6934[/C][C]-5.6934[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]20.6465[/C][C]-1.64651[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]18.0497[/C][C]-2.04968[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]20.4226[/C][C]4.57737[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]20.3789[/C][C]2.62112[/C][/ROW]
[ROW][C]52[/C][C]17[/C][C]21.7193[/C][C]-4.71926[/C][/ROW]
[ROW][C]53[/C][C]19[/C][C]20.3567[/C][C]-1.35669[/C][/ROW]
[ROW][C]54[/C][C]21[/C][C]21.1081[/C][C]-0.108081[/C][/ROW]
[ROW][C]55[/C][C]18[/C][C]20.9754[/C][C]-2.97543[/C][/ROW]
[ROW][C]56[/C][C]27[/C][C]20.1955[/C][C]6.8045[/C][/ROW]
[ROW][C]57[/C][C]21[/C][C]18.7848[/C][C]2.21522[/C][/ROW]
[ROW][C]58[/C][C]13[/C][C]19.251[/C][C]-6.25105[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]17.343[/C][C]-9.34299[/C][/ROW]
[ROW][C]60[/C][C]29[/C][C]20.5253[/C][C]8.47475[/C][/ROW]
[ROW][C]61[/C][C]28[/C][C]21.3139[/C][C]6.68614[/C][/ROW]
[ROW][C]62[/C][C]23[/C][C]17.75[/C][C]5.25001[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]17.541[/C][C]3.45897[/C][/ROW]
[ROW][C]64[/C][C]19[/C][C]20.5143[/C][C]-1.51428[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]19.4207[/C][C]-0.420652[/C][/ROW]
[ROW][C]66[/C][C]20[/C][C]19.6724[/C][C]0.327611[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]17.9575[/C][C]0.0425119[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]21.22[/C][C]-2.22[/C][/ROW]
[ROW][C]69[/C][C]17[/C][C]20.5236[/C][C]-3.52356[/C][/ROW]
[ROW][C]70[/C][C]19[/C][C]17.4188[/C][C]1.58122[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]18.704[/C][C]6.296[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]17.6482[/C][C]1.35176[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.1376[/C][C]2.86244[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.1043[/C][C]1.89566[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]17.3765[/C][C]-3.3765[/C][/ROW]
[ROW][C]76[/C][C]28[/C][C]21.196[/C][C]6.80402[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]18.0312[/C][C]-2.03122[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]19.4621[/C][C]4.5379[/C][/ROW]
[ROW][C]79[/C][C]20[/C][C]18.8888[/C][C]1.11119[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]17.9495[/C][C]-5.94951[/C][/ROW]
[ROW][C]81[/C][C]24[/C][C]20.4306[/C][C]3.56941[/C][/ROW]
[ROW][C]82[/C][C]22[/C][C]17.8691[/C][C]4.13092[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]18.0034[/C][C]-6.00336[/C][/ROW]
[ROW][C]84[/C][C]22[/C][C]17.7716[/C][C]4.22836[/C][/ROW]
[ROW][C]85[/C][C]20[/C][C]20.9072[/C][C]-0.907153[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]19.0331[/C][C]-9.03312[/C][/ROW]
[ROW][C]87[/C][C]23[/C][C]21.4087[/C][C]1.59129[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]21.1565[/C][C]-4.15646[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]18.3488[/C][C]3.6512[/C][/ROW]
[ROW][C]90[/C][C]24[/C][C]17.7645[/C][C]6.23553[/C][/ROW]
[ROW][C]91[/C][C]18[/C][C]17.1723[/C][C]0.827701[/C][/ROW]
[ROW][C]92[/C][C]21[/C][C]22.1771[/C][C]-1.17713[/C][/ROW]
[ROW][C]93[/C][C]20[/C][C]20.0033[/C][C]-0.0032585[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]20.9592[/C][C]-0.959235[/C][/ROW]
[ROW][C]95[/C][C]22[/C][C]17.6465[/C][C]4.35348[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]20.5742[/C][C]-1.5742[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]18.4979[/C][C]1.50212[/C][/ROW]
[ROW][C]98[/C][C]26[/C][C]20.8829[/C][C]5.1171[/C][/ROW]
[ROW][C]99[/C][C]23[/C][C]19.8135[/C][C]3.18654[/C][/ROW]
[ROW][C]100[/C][C]24[/C][C]18.9647[/C][C]5.0353[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]20.4759[/C][C]0.524116[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]21.0293[/C][C]-0.0293426[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]18.3619[/C][C]0.63806[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]18.3023[/C][C]-10.3023[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]20.3542[/C][C]-3.35422[/C][/ROW]
[ROW][C]106[/C][C]20[/C][C]20.8895[/C][C]-0.889459[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]18.929[/C][C]-7.92905[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]18.6058[/C][C]-10.6058[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]17.9024[/C][C]-2.9024[/C][/ROW]
[ROW][C]110[/C][C]18[/C][C]18.6255[/C][C]-0.625512[/C][/ROW]
[ROW][C]111[/C][C]18[/C][C]18.1624[/C][C]-0.162412[/C][/ROW]
[ROW][C]112[/C][C]19[/C][C]18.3685[/C][C]0.63146[/C][/ROW]
[ROW][C]113[/C][C]19[/C][C]19.6132[/C][C]-0.613163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&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	21	17.1685	3.83153
2	22	20.6901	1.30988
3	22	17.8481	4.15186
4	18	20.6507	-2.65066
5	23	20.8129	2.18707
6	12	20.709	-8.70896
7	20	19.1456	0.854392
8	22	20.8314	1.16863
9	21	20.8938	0.106181
10	19	22.4384	-3.43842
11	22	20.2848	1.71521
12	15	22.0421	-7.0421
13	20	19.8374	0.162579
14	19	17.4841	1.51586
15	18	19.4293	-1.42932
16	15	17.8915	-2.89147
17	20	19.3944	0.605598
18	21	18.2282	2.77178
19	21	21.7785	-0.778547
20	15	17.59	-2.58998
21	16	20.4189	-4.41888
22	23	20.6785	2.32145
23	21	18.5116	2.48838
24	18	19.229	-1.22904
25	25	22.4111	2.58894
26	9	20.1944	-11.1944
27	30	21.9568	8.0432
28	20	18.5631	1.43691
29	23	20.8637	2.13627
30	16	17.5344	-1.5344
31	16	17.3394	-1.33941
32	19	18.7748	0.225202
33	25	20.3523	4.64767
34	18	18.9625	-0.962516
35	23	20.3043	2.69568
36	21	21.4998	-0.499837
37	10	18.8008	-8.80082
38	14	20.6794	-6.67939
39	22	21.3527	0.647314
40	26	18.323	7.67696
41	23	20.1768	2.82322
42	23	20.6646	2.33536
43	24	22.306	1.69396
44	24	19.249	4.75104
45	18	20.1151	-2.1151
46	23	19.3034	3.6966
47	15	20.6934	-5.6934
48	19	20.6465	-1.64651
49	16	18.0497	-2.04968
50	25	20.4226	4.57737
51	23	20.3789	2.62112
52	17	21.7193	-4.71926
53	19	20.3567	-1.35669
54	21	21.1081	-0.108081
55	18	20.9754	-2.97543
56	27	20.1955	6.8045
57	21	18.7848	2.21522
58	13	19.251	-6.25105
59	8	17.343	-9.34299
60	29	20.5253	8.47475
61	28	21.3139	6.68614
62	23	17.75	5.25001
63	21	17.541	3.45897
64	19	20.5143	-1.51428
65	19	19.4207	-0.420652
66	20	19.6724	0.327611
67	18	17.9575	0.0425119
68	19	21.22	-2.22
69	17	20.5236	-3.52356
70	19	17.4188	1.58122
71	25	18.704	6.296
72	19	17.6482	1.35176
73	22	19.1376	2.86244
74	23	21.1043	1.89566
75	14	17.3765	-3.3765
76	28	21.196	6.80402
77	16	18.0312	-2.03122
78	24	19.4621	4.5379
79	20	18.8888	1.11119
80	12	17.9495	-5.94951
81	24	20.4306	3.56941
82	22	17.8691	4.13092
83	12	18.0034	-6.00336
84	22	17.7716	4.22836
85	20	20.9072	-0.907153
86	10	19.0331	-9.03312
87	23	21.4087	1.59129
88	17	21.1565	-4.15646
89	22	18.3488	3.6512
90	24	17.7645	6.23553
91	18	17.1723	0.827701
92	21	22.1771	-1.17713
93	20	20.0033	-0.0032585
94	20	20.9592	-0.959235
95	22	17.6465	4.35348
96	19	20.5742	-1.5742
97	20	18.4979	1.50212
98	26	20.8829	5.1171
99	23	19.8135	3.18654
100	24	18.9647	5.0353
101	21	20.4759	0.524116
102	21	21.0293	-0.0293426
103	19	18.3619	0.63806
104	8	18.3023	-10.3023
105	17	20.3542	-3.35422
106	20	20.8895	-0.889459
107	11	18.929	-7.92905
108	8	18.6058	-10.6058
109	15	17.9024	-2.9024
110	18	18.6255	-0.625512
111	18	18.1624	-0.162412
112	19	18.3685	0.63146
113	19	19.6132	-0.613163

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.583464	0.833072	0.416536
13	0.458028	0.916055	0.541972
14	0.322638	0.645275	0.677362
15	0.226236	0.452473	0.773764
16	0.32562	0.65124	0.67438
17	0.227303	0.454606	0.772697
18	0.174233	0.348466	0.825767
19	0.124207	0.248414	0.875793
20	0.0851345	0.170269	0.914865
21	0.0700657	0.140131	0.929934
22	0.0442515	0.088503	0.955749
23	0.038363	0.0767261	0.961637
24	0.0269377	0.0538755	0.973062
25	0.0260541	0.0521083	0.973946
26	0.152701	0.305402	0.847299
27	0.399049	0.798097	0.600951
28	0.37546	0.75092	0.62454
29	0.339441	0.678882	0.660559
30	0.284422	0.568843	0.715578
31	0.232574	0.465147	0.767426
32	0.198205	0.39641	0.801795
33	0.209714	0.419427	0.790286
34	0.168357	0.336715	0.831643
35	0.142572	0.285144	0.857428
36	0.111968	0.223935	0.888032
37	0.19368	0.387361	0.80632
38	0.313257	0.626513	0.686743
39	0.259555	0.519111	0.740445
40	0.389317	0.778634	0.610683
41	0.357698	0.715395	0.642302
42	0.312505	0.62501	0.687495
43	0.26842	0.536841	0.73158
44	0.242482	0.484964	0.757518
45	0.27711	0.55422	0.72289
46	0.268718	0.537435	0.731282
47	0.319433	0.638866	0.680567
48	0.275573	0.551146	0.724427
49	0.248764	0.497529	0.751236
50	0.250216	0.500431	0.749784
51	0.215391	0.430782	0.784609
52	0.230376	0.460753	0.769624
53	0.195281	0.390562	0.804719
54	0.159683	0.319365	0.840317
55	0.170942	0.341885	0.829058
56	0.223328	0.446656	0.776672
57	0.188178	0.376356	0.811822
58	0.241299	0.482598	0.758701
59	0.468317	0.936633	0.531683
60	0.640065	0.71987	0.359935
61	0.698296	0.603408	0.301704
62	0.703317	0.593366	0.296683
63	0.762932	0.474137	0.237068
64	0.73419	0.53162	0.26581
65	0.689411	0.621177	0.310589
66	0.635489	0.729023	0.364511
67	0.578403	0.843195	0.421597
68	0.532089	0.935822	0.467911
69	0.513646	0.972709	0.486354
70	0.467939	0.935879	0.532061
71	0.539427	0.921145	0.460573
72	0.502488	0.995024	0.497512
73	0.577445	0.84511	0.422555
74	0.527921	0.944158	0.472079
75	0.48422	0.968441	0.51578
76	0.514847	0.970305	0.485153
77	0.464999	0.929998	0.535001
78	0.458615	0.917231	0.541385
79	0.398563	0.797126	0.601437
80	0.423216	0.846433	0.576784
81	0.375771	0.751542	0.624229
82	0.362505	0.72501	0.637495
83	0.490665	0.981331	0.509335
84	0.452048	0.904096	0.547952
85	0.390912	0.781824	0.609088
86	0.466633	0.933266	0.533367
87	0.424281	0.848563	0.575719
88	0.39976	0.799521	0.60024
89	0.34139	0.682781	0.65861
90	0.35088	0.701761	0.64912
91	0.308863	0.617726	0.691137
92	0.239244	0.478487	0.760756
93	0.190002	0.380005	0.809998
94	0.143552	0.287105	0.856448
95	0.510723	0.978554	0.489277
96	0.410245	0.82049	0.589755
97	0.315727	0.631454	0.684273
98	0.651625	0.69675	0.348375
99	0.547829	0.904343	0.452171
100	0.711433	0.577134	0.288567
101	0.567881	0.864238	0.432119

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.583464 & 0.833072 & 0.416536 \tabularnewline
13 & 0.458028 & 0.916055 & 0.541972 \tabularnewline
14 & 0.322638 & 0.645275 & 0.677362 \tabularnewline
15 & 0.226236 & 0.452473 & 0.773764 \tabularnewline
16 & 0.32562 & 0.65124 & 0.67438 \tabularnewline
17 & 0.227303 & 0.454606 & 0.772697 \tabularnewline
18 & 0.174233 & 0.348466 & 0.825767 \tabularnewline
19 & 0.124207 & 0.248414 & 0.875793 \tabularnewline
20 & 0.0851345 & 0.170269 & 0.914865 \tabularnewline
21 & 0.0700657 & 0.140131 & 0.929934 \tabularnewline
22 & 0.0442515 & 0.088503 & 0.955749 \tabularnewline
23 & 0.038363 & 0.0767261 & 0.961637 \tabularnewline
24 & 0.0269377 & 0.0538755 & 0.973062 \tabularnewline
25 & 0.0260541 & 0.0521083 & 0.973946 \tabularnewline
26 & 0.152701 & 0.305402 & 0.847299 \tabularnewline
27 & 0.399049 & 0.798097 & 0.600951 \tabularnewline
28 & 0.37546 & 0.75092 & 0.62454 \tabularnewline
29 & 0.339441 & 0.678882 & 0.660559 \tabularnewline
30 & 0.284422 & 0.568843 & 0.715578 \tabularnewline
31 & 0.232574 & 0.465147 & 0.767426 \tabularnewline
32 & 0.198205 & 0.39641 & 0.801795 \tabularnewline
33 & 0.209714 & 0.419427 & 0.790286 \tabularnewline
34 & 0.168357 & 0.336715 & 0.831643 \tabularnewline
35 & 0.142572 & 0.285144 & 0.857428 \tabularnewline
36 & 0.111968 & 0.223935 & 0.888032 \tabularnewline
37 & 0.19368 & 0.387361 & 0.80632 \tabularnewline
38 & 0.313257 & 0.626513 & 0.686743 \tabularnewline
39 & 0.259555 & 0.519111 & 0.740445 \tabularnewline
40 & 0.389317 & 0.778634 & 0.610683 \tabularnewline
41 & 0.357698 & 0.715395 & 0.642302 \tabularnewline
42 & 0.312505 & 0.62501 & 0.687495 \tabularnewline
43 & 0.26842 & 0.536841 & 0.73158 \tabularnewline
44 & 0.242482 & 0.484964 & 0.757518 \tabularnewline
45 & 0.27711 & 0.55422 & 0.72289 \tabularnewline
46 & 0.268718 & 0.537435 & 0.731282 \tabularnewline
47 & 0.319433 & 0.638866 & 0.680567 \tabularnewline
48 & 0.275573 & 0.551146 & 0.724427 \tabularnewline
49 & 0.248764 & 0.497529 & 0.751236 \tabularnewline
50 & 0.250216 & 0.500431 & 0.749784 \tabularnewline
51 & 0.215391 & 0.430782 & 0.784609 \tabularnewline
52 & 0.230376 & 0.460753 & 0.769624 \tabularnewline
53 & 0.195281 & 0.390562 & 0.804719 \tabularnewline
54 & 0.159683 & 0.319365 & 0.840317 \tabularnewline
55 & 0.170942 & 0.341885 & 0.829058 \tabularnewline
56 & 0.223328 & 0.446656 & 0.776672 \tabularnewline
57 & 0.188178 & 0.376356 & 0.811822 \tabularnewline
58 & 0.241299 & 0.482598 & 0.758701 \tabularnewline
59 & 0.468317 & 0.936633 & 0.531683 \tabularnewline
60 & 0.640065 & 0.71987 & 0.359935 \tabularnewline
61 & 0.698296 & 0.603408 & 0.301704 \tabularnewline
62 & 0.703317 & 0.593366 & 0.296683 \tabularnewline
63 & 0.762932 & 0.474137 & 0.237068 \tabularnewline
64 & 0.73419 & 0.53162 & 0.26581 \tabularnewline
65 & 0.689411 & 0.621177 & 0.310589 \tabularnewline
66 & 0.635489 & 0.729023 & 0.364511 \tabularnewline
67 & 0.578403 & 0.843195 & 0.421597 \tabularnewline
68 & 0.532089 & 0.935822 & 0.467911 \tabularnewline
69 & 0.513646 & 0.972709 & 0.486354 \tabularnewline
70 & 0.467939 & 0.935879 & 0.532061 \tabularnewline
71 & 0.539427 & 0.921145 & 0.460573 \tabularnewline
72 & 0.502488 & 0.995024 & 0.497512 \tabularnewline
73 & 0.577445 & 0.84511 & 0.422555 \tabularnewline
74 & 0.527921 & 0.944158 & 0.472079 \tabularnewline
75 & 0.48422 & 0.968441 & 0.51578 \tabularnewline
76 & 0.514847 & 0.970305 & 0.485153 \tabularnewline
77 & 0.464999 & 0.929998 & 0.535001 \tabularnewline
78 & 0.458615 & 0.917231 & 0.541385 \tabularnewline
79 & 0.398563 & 0.797126 & 0.601437 \tabularnewline
80 & 0.423216 & 0.846433 & 0.576784 \tabularnewline
81 & 0.375771 & 0.751542 & 0.624229 \tabularnewline
82 & 0.362505 & 0.72501 & 0.637495 \tabularnewline
83 & 0.490665 & 0.981331 & 0.509335 \tabularnewline
84 & 0.452048 & 0.904096 & 0.547952 \tabularnewline
85 & 0.390912 & 0.781824 & 0.609088 \tabularnewline
86 & 0.466633 & 0.933266 & 0.533367 \tabularnewline
87 & 0.424281 & 0.848563 & 0.575719 \tabularnewline
88 & 0.39976 & 0.799521 & 0.60024 \tabularnewline
89 & 0.34139 & 0.682781 & 0.65861 \tabularnewline
90 & 0.35088 & 0.701761 & 0.64912 \tabularnewline
91 & 0.308863 & 0.617726 & 0.691137 \tabularnewline
92 & 0.239244 & 0.478487 & 0.760756 \tabularnewline
93 & 0.190002 & 0.380005 & 0.809998 \tabularnewline
94 & 0.143552 & 0.287105 & 0.856448 \tabularnewline
95 & 0.510723 & 0.978554 & 0.489277 \tabularnewline
96 & 0.410245 & 0.82049 & 0.589755 \tabularnewline
97 & 0.315727 & 0.631454 & 0.684273 \tabularnewline
98 & 0.651625 & 0.69675 & 0.348375 \tabularnewline
99 & 0.547829 & 0.904343 & 0.452171 \tabularnewline
100 & 0.711433 & 0.577134 & 0.288567 \tabularnewline
101 & 0.567881 & 0.864238 & 0.432119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&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]12[/C][C]0.583464[/C][C]0.833072[/C][C]0.416536[/C][/ROW]
[ROW][C]13[/C][C]0.458028[/C][C]0.916055[/C][C]0.541972[/C][/ROW]
[ROW][C]14[/C][C]0.322638[/C][C]0.645275[/C][C]0.677362[/C][/ROW]
[ROW][C]15[/C][C]0.226236[/C][C]0.452473[/C][C]0.773764[/C][/ROW]
[ROW][C]16[/C][C]0.32562[/C][C]0.65124[/C][C]0.67438[/C][/ROW]
[ROW][C]17[/C][C]0.227303[/C][C]0.454606[/C][C]0.772697[/C][/ROW]
[ROW][C]18[/C][C]0.174233[/C][C]0.348466[/C][C]0.825767[/C][/ROW]
[ROW][C]19[/C][C]0.124207[/C][C]0.248414[/C][C]0.875793[/C][/ROW]
[ROW][C]20[/C][C]0.0851345[/C][C]0.170269[/C][C]0.914865[/C][/ROW]
[ROW][C]21[/C][C]0.0700657[/C][C]0.140131[/C][C]0.929934[/C][/ROW]
[ROW][C]22[/C][C]0.0442515[/C][C]0.088503[/C][C]0.955749[/C][/ROW]
[ROW][C]23[/C][C]0.038363[/C][C]0.0767261[/C][C]0.961637[/C][/ROW]
[ROW][C]24[/C][C]0.0269377[/C][C]0.0538755[/C][C]0.973062[/C][/ROW]
[ROW][C]25[/C][C]0.0260541[/C][C]0.0521083[/C][C]0.973946[/C][/ROW]
[ROW][C]26[/C][C]0.152701[/C][C]0.305402[/C][C]0.847299[/C][/ROW]
[ROW][C]27[/C][C]0.399049[/C][C]0.798097[/C][C]0.600951[/C][/ROW]
[ROW][C]28[/C][C]0.37546[/C][C]0.75092[/C][C]0.62454[/C][/ROW]
[ROW][C]29[/C][C]0.339441[/C][C]0.678882[/C][C]0.660559[/C][/ROW]
[ROW][C]30[/C][C]0.284422[/C][C]0.568843[/C][C]0.715578[/C][/ROW]
[ROW][C]31[/C][C]0.232574[/C][C]0.465147[/C][C]0.767426[/C][/ROW]
[ROW][C]32[/C][C]0.198205[/C][C]0.39641[/C][C]0.801795[/C][/ROW]
[ROW][C]33[/C][C]0.209714[/C][C]0.419427[/C][C]0.790286[/C][/ROW]
[ROW][C]34[/C][C]0.168357[/C][C]0.336715[/C][C]0.831643[/C][/ROW]
[ROW][C]35[/C][C]0.142572[/C][C]0.285144[/C][C]0.857428[/C][/ROW]
[ROW][C]36[/C][C]0.111968[/C][C]0.223935[/C][C]0.888032[/C][/ROW]
[ROW][C]37[/C][C]0.19368[/C][C]0.387361[/C][C]0.80632[/C][/ROW]
[ROW][C]38[/C][C]0.313257[/C][C]0.626513[/C][C]0.686743[/C][/ROW]
[ROW][C]39[/C][C]0.259555[/C][C]0.519111[/C][C]0.740445[/C][/ROW]
[ROW][C]40[/C][C]0.389317[/C][C]0.778634[/C][C]0.610683[/C][/ROW]
[ROW][C]41[/C][C]0.357698[/C][C]0.715395[/C][C]0.642302[/C][/ROW]
[ROW][C]42[/C][C]0.312505[/C][C]0.62501[/C][C]0.687495[/C][/ROW]
[ROW][C]43[/C][C]0.26842[/C][C]0.536841[/C][C]0.73158[/C][/ROW]
[ROW][C]44[/C][C]0.242482[/C][C]0.484964[/C][C]0.757518[/C][/ROW]
[ROW][C]45[/C][C]0.27711[/C][C]0.55422[/C][C]0.72289[/C][/ROW]
[ROW][C]46[/C][C]0.268718[/C][C]0.537435[/C][C]0.731282[/C][/ROW]
[ROW][C]47[/C][C]0.319433[/C][C]0.638866[/C][C]0.680567[/C][/ROW]
[ROW][C]48[/C][C]0.275573[/C][C]0.551146[/C][C]0.724427[/C][/ROW]
[ROW][C]49[/C][C]0.248764[/C][C]0.497529[/C][C]0.751236[/C][/ROW]
[ROW][C]50[/C][C]0.250216[/C][C]0.500431[/C][C]0.749784[/C][/ROW]
[ROW][C]51[/C][C]0.215391[/C][C]0.430782[/C][C]0.784609[/C][/ROW]
[ROW][C]52[/C][C]0.230376[/C][C]0.460753[/C][C]0.769624[/C][/ROW]
[ROW][C]53[/C][C]0.195281[/C][C]0.390562[/C][C]0.804719[/C][/ROW]
[ROW][C]54[/C][C]0.159683[/C][C]0.319365[/C][C]0.840317[/C][/ROW]
[ROW][C]55[/C][C]0.170942[/C][C]0.341885[/C][C]0.829058[/C][/ROW]
[ROW][C]56[/C][C]0.223328[/C][C]0.446656[/C][C]0.776672[/C][/ROW]
[ROW][C]57[/C][C]0.188178[/C][C]0.376356[/C][C]0.811822[/C][/ROW]
[ROW][C]58[/C][C]0.241299[/C][C]0.482598[/C][C]0.758701[/C][/ROW]
[ROW][C]59[/C][C]0.468317[/C][C]0.936633[/C][C]0.531683[/C][/ROW]
[ROW][C]60[/C][C]0.640065[/C][C]0.71987[/C][C]0.359935[/C][/ROW]
[ROW][C]61[/C][C]0.698296[/C][C]0.603408[/C][C]0.301704[/C][/ROW]
[ROW][C]62[/C][C]0.703317[/C][C]0.593366[/C][C]0.296683[/C][/ROW]
[ROW][C]63[/C][C]0.762932[/C][C]0.474137[/C][C]0.237068[/C][/ROW]
[ROW][C]64[/C][C]0.73419[/C][C]0.53162[/C][C]0.26581[/C][/ROW]
[ROW][C]65[/C][C]0.689411[/C][C]0.621177[/C][C]0.310589[/C][/ROW]
[ROW][C]66[/C][C]0.635489[/C][C]0.729023[/C][C]0.364511[/C][/ROW]
[ROW][C]67[/C][C]0.578403[/C][C]0.843195[/C][C]0.421597[/C][/ROW]
[ROW][C]68[/C][C]0.532089[/C][C]0.935822[/C][C]0.467911[/C][/ROW]
[ROW][C]69[/C][C]0.513646[/C][C]0.972709[/C][C]0.486354[/C][/ROW]
[ROW][C]70[/C][C]0.467939[/C][C]0.935879[/C][C]0.532061[/C][/ROW]
[ROW][C]71[/C][C]0.539427[/C][C]0.921145[/C][C]0.460573[/C][/ROW]
[ROW][C]72[/C][C]0.502488[/C][C]0.995024[/C][C]0.497512[/C][/ROW]
[ROW][C]73[/C][C]0.577445[/C][C]0.84511[/C][C]0.422555[/C][/ROW]
[ROW][C]74[/C][C]0.527921[/C][C]0.944158[/C][C]0.472079[/C][/ROW]
[ROW][C]75[/C][C]0.48422[/C][C]0.968441[/C][C]0.51578[/C][/ROW]
[ROW][C]76[/C][C]0.514847[/C][C]0.970305[/C][C]0.485153[/C][/ROW]
[ROW][C]77[/C][C]0.464999[/C][C]0.929998[/C][C]0.535001[/C][/ROW]
[ROW][C]78[/C][C]0.458615[/C][C]0.917231[/C][C]0.541385[/C][/ROW]
[ROW][C]79[/C][C]0.398563[/C][C]0.797126[/C][C]0.601437[/C][/ROW]
[ROW][C]80[/C][C]0.423216[/C][C]0.846433[/C][C]0.576784[/C][/ROW]
[ROW][C]81[/C][C]0.375771[/C][C]0.751542[/C][C]0.624229[/C][/ROW]
[ROW][C]82[/C][C]0.362505[/C][C]0.72501[/C][C]0.637495[/C][/ROW]
[ROW][C]83[/C][C]0.490665[/C][C]0.981331[/C][C]0.509335[/C][/ROW]
[ROW][C]84[/C][C]0.452048[/C][C]0.904096[/C][C]0.547952[/C][/ROW]
[ROW][C]85[/C][C]0.390912[/C][C]0.781824[/C][C]0.609088[/C][/ROW]
[ROW][C]86[/C][C]0.466633[/C][C]0.933266[/C][C]0.533367[/C][/ROW]
[ROW][C]87[/C][C]0.424281[/C][C]0.848563[/C][C]0.575719[/C][/ROW]
[ROW][C]88[/C][C]0.39976[/C][C]0.799521[/C][C]0.60024[/C][/ROW]
[ROW][C]89[/C][C]0.34139[/C][C]0.682781[/C][C]0.65861[/C][/ROW]
[ROW][C]90[/C][C]0.35088[/C][C]0.701761[/C][C]0.64912[/C][/ROW]
[ROW][C]91[/C][C]0.308863[/C][C]0.617726[/C][C]0.691137[/C][/ROW]
[ROW][C]92[/C][C]0.239244[/C][C]0.478487[/C][C]0.760756[/C][/ROW]
[ROW][C]93[/C][C]0.190002[/C][C]0.380005[/C][C]0.809998[/C][/ROW]
[ROW][C]94[/C][C]0.143552[/C][C]0.287105[/C][C]0.856448[/C][/ROW]
[ROW][C]95[/C][C]0.510723[/C][C]0.978554[/C][C]0.489277[/C][/ROW]
[ROW][C]96[/C][C]0.410245[/C][C]0.82049[/C][C]0.589755[/C][/ROW]
[ROW][C]97[/C][C]0.315727[/C][C]0.631454[/C][C]0.684273[/C][/ROW]
[ROW][C]98[/C][C]0.651625[/C][C]0.69675[/C][C]0.348375[/C][/ROW]
[ROW][C]99[/C][C]0.547829[/C][C]0.904343[/C][C]0.452171[/C][/ROW]
[ROW][C]100[/C][C]0.711433[/C][C]0.577134[/C][C]0.288567[/C][/ROW]
[ROW][C]101[/C][C]0.567881[/C][C]0.864238[/C][C]0.432119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&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
12	0.583464	0.833072	0.416536
13	0.458028	0.916055	0.541972
14	0.322638	0.645275	0.677362
15	0.226236	0.452473	0.773764
16	0.32562	0.65124	0.67438
17	0.227303	0.454606	0.772697
18	0.174233	0.348466	0.825767
19	0.124207	0.248414	0.875793
20	0.0851345	0.170269	0.914865
21	0.0700657	0.140131	0.929934
22	0.0442515	0.088503	0.955749
23	0.038363	0.0767261	0.961637
24	0.0269377	0.0538755	0.973062
25	0.0260541	0.0521083	0.973946
26	0.152701	0.305402	0.847299
27	0.399049	0.798097	0.600951
28	0.37546	0.75092	0.62454
29	0.339441	0.678882	0.660559
30	0.284422	0.568843	0.715578
31	0.232574	0.465147	0.767426
32	0.198205	0.39641	0.801795
33	0.209714	0.419427	0.790286
34	0.168357	0.336715	0.831643
35	0.142572	0.285144	0.857428
36	0.111968	0.223935	0.888032
37	0.19368	0.387361	0.80632
38	0.313257	0.626513	0.686743
39	0.259555	0.519111	0.740445
40	0.389317	0.778634	0.610683
41	0.357698	0.715395	0.642302
42	0.312505	0.62501	0.687495
43	0.26842	0.536841	0.73158
44	0.242482	0.484964	0.757518
45	0.27711	0.55422	0.72289
46	0.268718	0.537435	0.731282
47	0.319433	0.638866	0.680567
48	0.275573	0.551146	0.724427
49	0.248764	0.497529	0.751236
50	0.250216	0.500431	0.749784
51	0.215391	0.430782	0.784609
52	0.230376	0.460753	0.769624
53	0.195281	0.390562	0.804719
54	0.159683	0.319365	0.840317
55	0.170942	0.341885	0.829058
56	0.223328	0.446656	0.776672
57	0.188178	0.376356	0.811822
58	0.241299	0.482598	0.758701
59	0.468317	0.936633	0.531683
60	0.640065	0.71987	0.359935
61	0.698296	0.603408	0.301704
62	0.703317	0.593366	0.296683
63	0.762932	0.474137	0.237068
64	0.73419	0.53162	0.26581
65	0.689411	0.621177	0.310589
66	0.635489	0.729023	0.364511
67	0.578403	0.843195	0.421597
68	0.532089	0.935822	0.467911
69	0.513646	0.972709	0.486354
70	0.467939	0.935879	0.532061
71	0.539427	0.921145	0.460573
72	0.502488	0.995024	0.497512
73	0.577445	0.84511	0.422555
74	0.527921	0.944158	0.472079
75	0.48422	0.968441	0.51578
76	0.514847	0.970305	0.485153
77	0.464999	0.929998	0.535001
78	0.458615	0.917231	0.541385
79	0.398563	0.797126	0.601437
80	0.423216	0.846433	0.576784
81	0.375771	0.751542	0.624229
82	0.362505	0.72501	0.637495
83	0.490665	0.981331	0.509335
84	0.452048	0.904096	0.547952
85	0.390912	0.781824	0.609088
86	0.466633	0.933266	0.533367
87	0.424281	0.848563	0.575719
88	0.39976	0.799521	0.60024
89	0.34139	0.682781	0.65861
90	0.35088	0.701761	0.64912
91	0.308863	0.617726	0.691137
92	0.239244	0.478487	0.760756
93	0.190002	0.380005	0.809998
94	0.143552	0.287105	0.856448
95	0.510723	0.978554	0.489277
96	0.410245	0.82049	0.589755
97	0.315727	0.631454	0.684273
98	0.651625	0.69675	0.348375
99	0.547829	0.904343	0.452171
100	0.711433	0.577134	0.288567
101	0.567881	0.864238	0.432119

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	4	0.0444444	OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0444444 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266926&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0444444[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266926&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266926&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	4	0.0444444	OK

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):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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
par1 <- as.numeric(par1)
x <- 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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