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Author

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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 11 Nov 2014 13:27:24 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/11/t1415712510uc6ypy9sgistwd4.htm/, Retrieved Sun, 19 May 2024 11:10:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253603, Retrieved Sun, 19 May 2024 11:10:12 +0000

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Original text written by user:

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No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [WS 7] [2014-11-11 13:27:24] [a0dc8dfb1ad11084a66a61bab0a3c2c7] [Current]
- RMPD    [Bootstrap Plot - Central Tendency] [WS 7 1] [2014-11-12 14:15:30] [7b9119a46b6eb1a22eecca7bc054a6e2] 
- RMPD    [Bootstrap Plot - Central Tendency] [WS 7 2] [2014-11-12 14:16:29] [7b9119a46b6eb1a22eecca7bc054a6e2] 
- RMPD    [Bootstrap Plot - Central Tendency] [WS 7 bootstrap] [2014-11-12 14:50:26] [7b9119a46b6eb1a22eecca7bc054a6e2] 
- RMPD      [Multiple Regression] [WS 7 figuur] [2014-11-12 15:03:58] [7b9119a46b6eb1a22eecca7bc054a6e2] 

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Dataseries X:

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Histogram

Boxplots

41	38	13	12	14
39	32	16	11	18
30	35	19	15	11
31	33	15	6	12
34	37	14	13	16
35	29	13	10	18
39	31	19	12	14
34	36	15	14	14
36	35	14	12	15
37	38	15	6	15
38	31	16	10	17
36	34	16	12	19
38	35	16	12	10
39	38	16	11	16
33	37	17	15	18
32	33	15	12	14
36	32	15	10	14
38	38	20	12	17
39	38	18	11	14
32	32	16	12	16
32	33	16	11	18
31	31	16	12	11
39	38	19	13	14
37	39	16	11	12
39	32	17	9	17
41	32	17	13	9
36	35	16	10	16
33	37	15	14	14
33	33	16	12	15
34	33	14	10	11
31	28	15	12	16
27	32	12	8	13
37	31	14	10	17
34	37	16	12	15
34	30	14	12	14
32	33	7	7	16
29	31	10	6	9
36	33	14	12	15
29	31	16	10	17
35	33	16	10	13
37	32	16	10	15
34	33	14	12	16
38	32	20	15	16
35	33	14	10	12
38	28	14	10	12
37	35	11	12	11
38	39	14	13	15
33	34	15	11	15
36	38	16	11	17
38	32	14	12	13
32	38	16	14	16
32	30	14	10	14
32	33	12	12	11
34	38	16	13	12
32	32	9	5	12
37	32	14	6	15
39	34	16	12	16
29	34	16	12	15
37	36	15	11	12
35	34	16	10	12
30	28	12	7	8
38	34	16	12	13
34	35	16	14	11
31	35	14	11	14
34	31	16	12	15
35	37	17	13	10
36	35	18	14	11
30	27	18	11	12
39	40	12	12	15
35	37	16	12	15
38	36	10	8	14
31	38	14	11	16
34	39	18	14	15
38	41	18	14	15
34	27	16	12	13
39	30	17	9	12
37	37	16	13	17
34	31	16	11	13
28	31	13	12	15
37	27	16	12	13
33	36	16	12	15
37	38	20	12	16
35	37	16	12	15
37	33	15	12	16
32	34	15	11	15
33	31	16	10	14
38	39	14	9	15
33	34	16	12	14
29	32	16	12	13
33	33	15	12	7
31	36	12	9	17
36	32	17	15	13
35	41	16	12	15
32	28	15	12	14
29	30	13	12	13
39	36	16	10	16
37	35	16	13	12
35	31	16	9	14
37	34	16	12	17
32	36	14	10	15
38	36	16	14	17
37	35	16	11	12
36	37	20	15	16
32	28	15	11	11
33	39	16	11	15
40	32	13	12	9
38	35	17	12	16
41	39	16	12	15
36	35	16	11	10
43	42	12	7	10
30	34	16	12	15
31	33	16	14	11
32	41	17	11	13
32	33	13	11	14
37	34	12	10	18
37	32	18	13	16
33	40	14	13	14
34	40	14	8	14
33	35	13	11	14
38	36	16	12	14
33	37	13	11	12
31	27	16	13	14
38	39	13	12	15
37	38	16	14	15
33	31	15	13	15
31	33	16	15	13
39	32	15	10	17
44	39	17	11	17
33	36	15	9	19
35	33	12	11	15
32	33	16	10	13
28	32	10	11	9
40	37	16	8	15
27	30	12	11	15
37	38	14	12	15
32	29	15	12	16
28	22	13	9	11
34	35	15	11	14
30	35	11	10	11
35	34	12	8	15
31	35	8	9	13
32	34	16	8	15
30	34	15	9	16
30	35	17	15	14
31	23	16	11	15
40	31	10	8	16
32	27	18	13	16
36	36	13	12	11
32	31	16	12	12
35	32	13	9	9
38	39	10	7	16
42	37	15	13	13
34	38	16	9	16
35	39	16	6	12
35	34	14	8	9
33	31	10	8	13
36	32	17	15	13
32	37	13	6	14
33	36	15	9	19
34	32	16	11	13
32	35	12	8	12
34	36	13	8	13

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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
connected[t] = + 18.7662 + 0.330516separate[t] + 0.334657learning[t] -0.137933software[t] + 0.0791285happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
connected[t] =  +  18.7662 +  0.330516separate[t] +  0.334657learning[t] -0.137933software[t] +  0.0791285happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]connected[t] =  +  18.7662 +  0.330516separate[t] +  0.334657learning[t] -0.137933software[t] +  0.0791285happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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
connected[t] = + 18.7662 + 0.330516separate[t] + 0.334657learning[t] -0.137933software[t] + 0.0791285happiness[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)	18.7662	2.92733	6.411	1.61505e-09	8.07523e-10
separate	0.330516	0.0697038	4.742	4.72215e-06	2.36108e-06
learning	0.334657	0.131098	2.553	0.0116418	0.0058209
software	-0.137933	0.136186	-1.013	0.312698	0.156349
happiness	0.0791285	0.107404	0.7367	0.462383	0.231192

\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) & 18.7662 & 2.92733 & 6.411 & 1.61505e-09 & 8.07523e-10 \tabularnewline
separate & 0.330516 & 0.0697038 & 4.742 & 4.72215e-06 & 2.36108e-06 \tabularnewline
learning & 0.334657 & 0.131098 & 2.553 & 0.0116418 & 0.0058209 \tabularnewline
software & -0.137933 & 0.136186 & -1.013 & 0.312698 & 0.156349 \tabularnewline
happiness & 0.0791285 & 0.107404 & 0.7367 & 0.462383 & 0.231192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&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]18.7662[/C][C]2.92733[/C][C]6.411[/C][C]1.61505e-09[/C][C]8.07523e-10[/C][/ROW]
[ROW][C]separate[/C][C]0.330516[/C][C]0.0697038[/C][C]4.742[/C][C]4.72215e-06[/C][C]2.36108e-06[/C][/ROW]
[ROW][C]learning[/C][C]0.334657[/C][C]0.131098[/C][C]2.553[/C][C]0.0116418[/C][C]0.0058209[/C][/ROW]
[ROW][C]software[/C][C]-0.137933[/C][C]0.136186[/C][C]-1.013[/C][C]0.312698[/C][C]0.156349[/C][/ROW]
[ROW][C]happiness[/C][C]0.0791285[/C][C]0.107404[/C][C]0.7367[/C][C]0.462383[/C][C]0.231192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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)	18.7662	2.92733	6.411	1.61505e-09	8.07523e-10
separate	0.330516	0.0697038	4.742	4.72215e-06	2.36108e-06
learning	0.334657	0.131098	2.553	0.0116418	0.0058209
software	-0.137933	0.136186	-1.013	0.312698	0.156349
happiness	0.0791285	0.107404	0.7367	0.462383	0.231192

Multiple Linear Regression - Regression Statistics
Multiple R	0.422196
R-squared	0.178249
Adjusted R-squared	0.157313
F-TEST (value)	8.51388
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	3.0407e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.0983
Sum Squared Residuals	1507.12

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.422196 \tabularnewline
R-squared & 0.178249 \tabularnewline
Adjusted R-squared & 0.157313 \tabularnewline
F-TEST (value) & 8.51388 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 3.0407e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.0983 \tabularnewline
Sum Squared Residuals & 1507.12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.422196[/C][/ROW]
[ROW][C]R-squared[/C][C]0.178249[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.157313[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.51388[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]3.0407e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.0983[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1507.12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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	0.422196
R-squared	0.178249
Adjusted R-squared	0.157313
F-TEST (value)	8.51388
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	3.0407e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.0983
Sum Squared Residuals	1507.12

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	41	35.1289	5.87107
2	39	34.6043	4.39575
3	30	35.4941	-5.49414
4	31	34.815	-3.81501
5	34	35.1534	-1.15339
6	35	32.7467	2.25334
7	39	34.8233	4.17674
8	34	34.8613	-0.861344
9	36	34.5512	1.44883
10	37	36.705	0.295028
11	38	34.3325	3.66746
12	36	35.2065	0.793521
13	38	34.8248	3.17516
14	39	36.4291	2.57091
15	33	36.0398	-3.03976
16	32	34.1457	-2.14566
17	36	34.091	1.90899
18	38	37.7089	0.291084
19	39	36.9401	2.05985
20	32	34.3081	-2.30806
21	32	34.9348	-2.93477
22	31	33.5819	-2.5819
23	39	36.9989	2.00106
24	37	36.4431	0.556907
25	39	35.1356	3.86435
26	41	33.9509	7.04911
27	36	35.5755	0.424524
28	33	35.1919	-2.19186
29	33	34.5594	-1.55945
30	34	33.8495	0.150514
31	31	32.6513	-1.65134
32	27	33.2838	-6.28378
33	37	33.6632	3.33677
34	34	35.8815	-1.88151
35	34	32.8195	1.18054
36	32	32.3163	-0.316327
37	29	32.2433	-3.2433
38	36	33.8901	2.10987
39	29	34.3325	-5.33254
40	35	34.6771	0.322942
41	37	34.5048	2.4952
42	34	33.9693	0.0307379
43	38	35.2329	2.76711
44	35	33.9286	1.07139
45	38	32.276	5.72397
46	37	33.2307	3.76932
47	38	35.7353	2.2647
48	33	34.6932	-1.69324
49	36	36.5082	-0.508219
50	38	33.4014	4.59864
51	32	36.0153	-4.01529
52	32	33.0953	-1.09532
53	32	32.9043	-0.904304
54	34	35.8367	-1.83671
55	32	32.6145	-0.614479
56	37	34.3872	2.61278
57	39	34.9691	4.03091
58	29	34.89	-5.88996
59	37	35.1169	1.88311
60	35	34.9284	0.0715541
61	30	31.704	-1.70401
62	38	34.7317	3.26829
63	34	34.6281	-0.6281
64	31	34.61	-3.60997
65	34	33.8984	0.101584
66	35	35.6826	-0.682594
67	36	35.2974	0.702586
68	30	33.1462	-3.14621
69	39	35.5344	3.46557
70	35	35.8815	-0.881513
71	38	34.0157	3.98434
72	31	35.7598	-4.75978
73	34	36.936	-2.93599
74	38	37.597	0.402975
75	34	32.4181	1.58191
76	39	34.079	4.92103
77	37	35.9018	1.09816
78	34	33.8781	0.121907
79	28	32.8944	-4.89444
80	37	32.4181	4.58191
81	33	35.551	-2.551
82	37	37.6298	-0.629787
83	35	35.8815	-0.881513
84	37	34.3039	2.69608
85	32	34.6932	-2.69324
86	33	34.0952	-1.09515
87	38	36.287	1.71297
88	33	34.8108	-1.81084
89	29	34.0707	-5.07068
90	33	33.5918	-0.591763
91	31	34.7844	-3.78442
92	36	33.9915	2.00847
93	35	37.2036	-2.20358
94	32	32.4931	-0.493082
95	29	32.4057	-3.40567
96	39	35.906	3.09401
97	37	34.8452	2.15484
98	35	34.2331	0.766912
99	37	35.0482	1.95178
100	32	35.1575	-3.15755
101	38	35.4334	2.56661
102	37	35.121	1.87897
103	36	36.8855	-0.885471
104	32	32.3936	-0.39363
105	33	36.6805	-3.68048
106	40	32.7502	7.24981
107	38	35.6343	2.36573
108	41	36.5425	4.45746
109	36	34.9628	1.03723
110	43	36.4895	6.51051
111	30	34.89	-4.88996
112	31	33.9671	-2.96707
113	32	37.5179	-5.51791
114	32	33.6143	-1.61428
115	37	34.0646	2.93541
116	37	34.8394	2.16056
117	33	35.9867	-2.98668
118	34	36.6764	-2.67635
119	33	34.2753	-1.27531
120	38	35.4719	2.52813
121	33	34.7781	-1.77809
122	31	32.3593	-1.35929
123	38	35.5386	2.46143
124	37	35.9362	1.06384
125	33	33.4258	-0.425825
126	31	33.9874	-2.98739
127	39	34.3284	4.6716
128	44	37.1734	6.82661
129	33	35.9467	-2.94665
130	35	33.3588	1.64125
131	32	34.6771	-2.67706
132	28	31.8841	-3.88415
133	40	36.4332	3.56675
134	27	32.3672	-5.3672
135	37	35.5427	1.45729
136	32	32.9819	-0.981855
137	28	30.0171	-2.01709
138	34	34.9446	-0.944628
139	30	33.5065	-3.50655
140	35	34.1031	0.896931
141	31	32.7988	-1.79876
142	32	35.4417	-3.4417
143	30	35.0482	-5.04824
144	30	35.0622	-5.06221
145	31	31.3922	-0.392221
146	40	32.5213	7.47867
147	32	33.1869	-1.18686
148	36	34.2305	1.76949
149	32	33.661	-1.66103
150	35	33.164	1.83601
151	38	35.3034	2.6966
152	42	35.2507	6.74934
153	34	36.705	-2.70496
154	35	37.1328	-2.13276
155	35	34.2976	0.702388
156	33	32.2839	0.716052
157	36	33.9915	2.00847
158	32	35.626	-3.62601
159	33	35.9467	-2.94665
160	34	34.2086	-0.208609
161	32	34.1962	-2.1962
162	34	34.9405	-0.940501

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.1289 & 5.87107 \tabularnewline
2 & 39 & 34.6043 & 4.39575 \tabularnewline
3 & 30 & 35.4941 & -5.49414 \tabularnewline
4 & 31 & 34.815 & -3.81501 \tabularnewline
5 & 34 & 35.1534 & -1.15339 \tabularnewline
6 & 35 & 32.7467 & 2.25334 \tabularnewline
7 & 39 & 34.8233 & 4.17674 \tabularnewline
8 & 34 & 34.8613 & -0.861344 \tabularnewline
9 & 36 & 34.5512 & 1.44883 \tabularnewline
10 & 37 & 36.705 & 0.295028 \tabularnewline
11 & 38 & 34.3325 & 3.66746 \tabularnewline
12 & 36 & 35.2065 & 0.793521 \tabularnewline
13 & 38 & 34.8248 & 3.17516 \tabularnewline
14 & 39 & 36.4291 & 2.57091 \tabularnewline
15 & 33 & 36.0398 & -3.03976 \tabularnewline
16 & 32 & 34.1457 & -2.14566 \tabularnewline
17 & 36 & 34.091 & 1.90899 \tabularnewline
18 & 38 & 37.7089 & 0.291084 \tabularnewline
19 & 39 & 36.9401 & 2.05985 \tabularnewline
20 & 32 & 34.3081 & -2.30806 \tabularnewline
21 & 32 & 34.9348 & -2.93477 \tabularnewline
22 & 31 & 33.5819 & -2.5819 \tabularnewline
23 & 39 & 36.9989 & 2.00106 \tabularnewline
24 & 37 & 36.4431 & 0.556907 \tabularnewline
25 & 39 & 35.1356 & 3.86435 \tabularnewline
26 & 41 & 33.9509 & 7.04911 \tabularnewline
27 & 36 & 35.5755 & 0.424524 \tabularnewline
28 & 33 & 35.1919 & -2.19186 \tabularnewline
29 & 33 & 34.5594 & -1.55945 \tabularnewline
30 & 34 & 33.8495 & 0.150514 \tabularnewline
31 & 31 & 32.6513 & -1.65134 \tabularnewline
32 & 27 & 33.2838 & -6.28378 \tabularnewline
33 & 37 & 33.6632 & 3.33677 \tabularnewline
34 & 34 & 35.8815 & -1.88151 \tabularnewline
35 & 34 & 32.8195 & 1.18054 \tabularnewline
36 & 32 & 32.3163 & -0.316327 \tabularnewline
37 & 29 & 32.2433 & -3.2433 \tabularnewline
38 & 36 & 33.8901 & 2.10987 \tabularnewline
39 & 29 & 34.3325 & -5.33254 \tabularnewline
40 & 35 & 34.6771 & 0.322942 \tabularnewline
41 & 37 & 34.5048 & 2.4952 \tabularnewline
42 & 34 & 33.9693 & 0.0307379 \tabularnewline
43 & 38 & 35.2329 & 2.76711 \tabularnewline
44 & 35 & 33.9286 & 1.07139 \tabularnewline
45 & 38 & 32.276 & 5.72397 \tabularnewline
46 & 37 & 33.2307 & 3.76932 \tabularnewline
47 & 38 & 35.7353 & 2.2647 \tabularnewline
48 & 33 & 34.6932 & -1.69324 \tabularnewline
49 & 36 & 36.5082 & -0.508219 \tabularnewline
50 & 38 & 33.4014 & 4.59864 \tabularnewline
51 & 32 & 36.0153 & -4.01529 \tabularnewline
52 & 32 & 33.0953 & -1.09532 \tabularnewline
53 & 32 & 32.9043 & -0.904304 \tabularnewline
54 & 34 & 35.8367 & -1.83671 \tabularnewline
55 & 32 & 32.6145 & -0.614479 \tabularnewline
56 & 37 & 34.3872 & 2.61278 \tabularnewline
57 & 39 & 34.9691 & 4.03091 \tabularnewline
58 & 29 & 34.89 & -5.88996 \tabularnewline
59 & 37 & 35.1169 & 1.88311 \tabularnewline
60 & 35 & 34.9284 & 0.0715541 \tabularnewline
61 & 30 & 31.704 & -1.70401 \tabularnewline
62 & 38 & 34.7317 & 3.26829 \tabularnewline
63 & 34 & 34.6281 & -0.6281 \tabularnewline
64 & 31 & 34.61 & -3.60997 \tabularnewline
65 & 34 & 33.8984 & 0.101584 \tabularnewline
66 & 35 & 35.6826 & -0.682594 \tabularnewline
67 & 36 & 35.2974 & 0.702586 \tabularnewline
68 & 30 & 33.1462 & -3.14621 \tabularnewline
69 & 39 & 35.5344 & 3.46557 \tabularnewline
70 & 35 & 35.8815 & -0.881513 \tabularnewline
71 & 38 & 34.0157 & 3.98434 \tabularnewline
72 & 31 & 35.7598 & -4.75978 \tabularnewline
73 & 34 & 36.936 & -2.93599 \tabularnewline
74 & 38 & 37.597 & 0.402975 \tabularnewline
75 & 34 & 32.4181 & 1.58191 \tabularnewline
76 & 39 & 34.079 & 4.92103 \tabularnewline
77 & 37 & 35.9018 & 1.09816 \tabularnewline
78 & 34 & 33.8781 & 0.121907 \tabularnewline
79 & 28 & 32.8944 & -4.89444 \tabularnewline
80 & 37 & 32.4181 & 4.58191 \tabularnewline
81 & 33 & 35.551 & -2.551 \tabularnewline
82 & 37 & 37.6298 & -0.629787 \tabularnewline
83 & 35 & 35.8815 & -0.881513 \tabularnewline
84 & 37 & 34.3039 & 2.69608 \tabularnewline
85 & 32 & 34.6932 & -2.69324 \tabularnewline
86 & 33 & 34.0952 & -1.09515 \tabularnewline
87 & 38 & 36.287 & 1.71297 \tabularnewline
88 & 33 & 34.8108 & -1.81084 \tabularnewline
89 & 29 & 34.0707 & -5.07068 \tabularnewline
90 & 33 & 33.5918 & -0.591763 \tabularnewline
91 & 31 & 34.7844 & -3.78442 \tabularnewline
92 & 36 & 33.9915 & 2.00847 \tabularnewline
93 & 35 & 37.2036 & -2.20358 \tabularnewline
94 & 32 & 32.4931 & -0.493082 \tabularnewline
95 & 29 & 32.4057 & -3.40567 \tabularnewline
96 & 39 & 35.906 & 3.09401 \tabularnewline
97 & 37 & 34.8452 & 2.15484 \tabularnewline
98 & 35 & 34.2331 & 0.766912 \tabularnewline
99 & 37 & 35.0482 & 1.95178 \tabularnewline
100 & 32 & 35.1575 & -3.15755 \tabularnewline
101 & 38 & 35.4334 & 2.56661 \tabularnewline
102 & 37 & 35.121 & 1.87897 \tabularnewline
103 & 36 & 36.8855 & -0.885471 \tabularnewline
104 & 32 & 32.3936 & -0.39363 \tabularnewline
105 & 33 & 36.6805 & -3.68048 \tabularnewline
106 & 40 & 32.7502 & 7.24981 \tabularnewline
107 & 38 & 35.6343 & 2.36573 \tabularnewline
108 & 41 & 36.5425 & 4.45746 \tabularnewline
109 & 36 & 34.9628 & 1.03723 \tabularnewline
110 & 43 & 36.4895 & 6.51051 \tabularnewline
111 & 30 & 34.89 & -4.88996 \tabularnewline
112 & 31 & 33.9671 & -2.96707 \tabularnewline
113 & 32 & 37.5179 & -5.51791 \tabularnewline
114 & 32 & 33.6143 & -1.61428 \tabularnewline
115 & 37 & 34.0646 & 2.93541 \tabularnewline
116 & 37 & 34.8394 & 2.16056 \tabularnewline
117 & 33 & 35.9867 & -2.98668 \tabularnewline
118 & 34 & 36.6764 & -2.67635 \tabularnewline
119 & 33 & 34.2753 & -1.27531 \tabularnewline
120 & 38 & 35.4719 & 2.52813 \tabularnewline
121 & 33 & 34.7781 & -1.77809 \tabularnewline
122 & 31 & 32.3593 & -1.35929 \tabularnewline
123 & 38 & 35.5386 & 2.46143 \tabularnewline
124 & 37 & 35.9362 & 1.06384 \tabularnewline
125 & 33 & 33.4258 & -0.425825 \tabularnewline
126 & 31 & 33.9874 & -2.98739 \tabularnewline
127 & 39 & 34.3284 & 4.6716 \tabularnewline
128 & 44 & 37.1734 & 6.82661 \tabularnewline
129 & 33 & 35.9467 & -2.94665 \tabularnewline
130 & 35 & 33.3588 & 1.64125 \tabularnewline
131 & 32 & 34.6771 & -2.67706 \tabularnewline
132 & 28 & 31.8841 & -3.88415 \tabularnewline
133 & 40 & 36.4332 & 3.56675 \tabularnewline
134 & 27 & 32.3672 & -5.3672 \tabularnewline
135 & 37 & 35.5427 & 1.45729 \tabularnewline
136 & 32 & 32.9819 & -0.981855 \tabularnewline
137 & 28 & 30.0171 & -2.01709 \tabularnewline
138 & 34 & 34.9446 & -0.944628 \tabularnewline
139 & 30 & 33.5065 & -3.50655 \tabularnewline
140 & 35 & 34.1031 & 0.896931 \tabularnewline
141 & 31 & 32.7988 & -1.79876 \tabularnewline
142 & 32 & 35.4417 & -3.4417 \tabularnewline
143 & 30 & 35.0482 & -5.04824 \tabularnewline
144 & 30 & 35.0622 & -5.06221 \tabularnewline
145 & 31 & 31.3922 & -0.392221 \tabularnewline
146 & 40 & 32.5213 & 7.47867 \tabularnewline
147 & 32 & 33.1869 & -1.18686 \tabularnewline
148 & 36 & 34.2305 & 1.76949 \tabularnewline
149 & 32 & 33.661 & -1.66103 \tabularnewline
150 & 35 & 33.164 & 1.83601 \tabularnewline
151 & 38 & 35.3034 & 2.6966 \tabularnewline
152 & 42 & 35.2507 & 6.74934 \tabularnewline
153 & 34 & 36.705 & -2.70496 \tabularnewline
154 & 35 & 37.1328 & -2.13276 \tabularnewline
155 & 35 & 34.2976 & 0.702388 \tabularnewline
156 & 33 & 32.2839 & 0.716052 \tabularnewline
157 & 36 & 33.9915 & 2.00847 \tabularnewline
158 & 32 & 35.626 & -3.62601 \tabularnewline
159 & 33 & 35.9467 & -2.94665 \tabularnewline
160 & 34 & 34.2086 & -0.208609 \tabularnewline
161 & 32 & 34.1962 & -2.1962 \tabularnewline
162 & 34 & 34.9405 & -0.940501 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&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]41[/C][C]35.1289[/C][C]5.87107[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.6043[/C][C]4.39575[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.4941[/C][C]-5.49414[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.815[/C][C]-3.81501[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.1534[/C][C]-1.15339[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.7467[/C][C]2.25334[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.8233[/C][C]4.17674[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]34.8613[/C][C]-0.861344[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.5512[/C][C]1.44883[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.705[/C][C]0.295028[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.3325[/C][C]3.66746[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]35.2065[/C][C]0.793521[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.8248[/C][C]3.17516[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.4291[/C][C]2.57091[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.0398[/C][C]-3.03976[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.1457[/C][C]-2.14566[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.091[/C][C]1.90899[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.7089[/C][C]0.291084[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.9401[/C][C]2.05985[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.3081[/C][C]-2.30806[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.9348[/C][C]-2.93477[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.5819[/C][C]-2.5819[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.9989[/C][C]2.00106[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.4431[/C][C]0.556907[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.1356[/C][C]3.86435[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]33.9509[/C][C]7.04911[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.5755[/C][C]0.424524[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.1919[/C][C]-2.19186[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.5594[/C][C]-1.55945[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8495[/C][C]0.150514[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.6513[/C][C]-1.65134[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.2838[/C][C]-6.28378[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6632[/C][C]3.33677[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.8815[/C][C]-1.88151[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.8195[/C][C]1.18054[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.3163[/C][C]-0.316327[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.2433[/C][C]-3.2433[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.8901[/C][C]2.10987[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3325[/C][C]-5.33254[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.6771[/C][C]0.322942[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.5048[/C][C]2.4952[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.9693[/C][C]0.0307379[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.2329[/C][C]2.76711[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.9286[/C][C]1.07139[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.276[/C][C]5.72397[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.2307[/C][C]3.76932[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.7353[/C][C]2.2647[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.6932[/C][C]-1.69324[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.5082[/C][C]-0.508219[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.4014[/C][C]4.59864[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.0153[/C][C]-4.01529[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0953[/C][C]-1.09532[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.9043[/C][C]-0.904304[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.8367[/C][C]-1.83671[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.6145[/C][C]-0.614479[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.3872[/C][C]2.61278[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9691[/C][C]4.03091[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.89[/C][C]-5.88996[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.1169[/C][C]1.88311[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.9284[/C][C]0.0715541[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.704[/C][C]-1.70401[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.7317[/C][C]3.26829[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.6281[/C][C]-0.6281[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.61[/C][C]-3.60997[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.8984[/C][C]0.101584[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.6826[/C][C]-0.682594[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.2974[/C][C]0.702586[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.1462[/C][C]-3.14621[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.5344[/C][C]3.46557[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.8815[/C][C]-0.881513[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.0157[/C][C]3.98434[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.7598[/C][C]-4.75978[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.936[/C][C]-2.93599[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.597[/C][C]0.402975[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.4181[/C][C]1.58191[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.079[/C][C]4.92103[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.9018[/C][C]1.09816[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.8781[/C][C]0.121907[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.8944[/C][C]-4.89444[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.4181[/C][C]4.58191[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.551[/C][C]-2.551[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.6298[/C][C]-0.629787[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.8815[/C][C]-0.881513[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.3039[/C][C]2.69608[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.6932[/C][C]-2.69324[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.0952[/C][C]-1.09515[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.287[/C][C]1.71297[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.8108[/C][C]-1.81084[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]34.0707[/C][C]-5.07068[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.5918[/C][C]-0.591763[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7844[/C][C]-3.78442[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.9915[/C][C]2.00847[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.2036[/C][C]-2.20358[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.4931[/C][C]-0.493082[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.4057[/C][C]-3.40567[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.906[/C][C]3.09401[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.8452[/C][C]2.15484[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.2331[/C][C]0.766912[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.0482[/C][C]1.95178[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.1575[/C][C]-3.15755[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.4334[/C][C]2.56661[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.121[/C][C]1.87897[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.8855[/C][C]-0.885471[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.3936[/C][C]-0.39363[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.6805[/C][C]-3.68048[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.7502[/C][C]7.24981[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.6343[/C][C]2.36573[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.5425[/C][C]4.45746[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.9628[/C][C]1.03723[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.4895[/C][C]6.51051[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.89[/C][C]-4.88996[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.9671[/C][C]-2.96707[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.5179[/C][C]-5.51791[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.6143[/C][C]-1.61428[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.0646[/C][C]2.93541[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.8394[/C][C]2.16056[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.9867[/C][C]-2.98668[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.6764[/C][C]-2.67635[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.2753[/C][C]-1.27531[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.4719[/C][C]2.52813[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.7781[/C][C]-1.77809[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3593[/C][C]-1.35929[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.5386[/C][C]2.46143[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]35.9362[/C][C]1.06384[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.4258[/C][C]-0.425825[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]33.9874[/C][C]-2.98739[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.3284[/C][C]4.6716[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.1734[/C][C]6.82661[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.9467[/C][C]-2.94665[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.3588[/C][C]1.64125[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.6771[/C][C]-2.67706[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.8841[/C][C]-3.88415[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.4332[/C][C]3.56675[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.3672[/C][C]-5.3672[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.5427[/C][C]1.45729[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.9819[/C][C]-0.981855[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]30.0171[/C][C]-2.01709[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.9446[/C][C]-0.944628[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.5065[/C][C]-3.50655[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.1031[/C][C]0.896931[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.7988[/C][C]-1.79876[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.4417[/C][C]-3.4417[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.0482[/C][C]-5.04824[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.0622[/C][C]-5.06221[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.3922[/C][C]-0.392221[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.5213[/C][C]7.47867[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.1869[/C][C]-1.18686[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.2305[/C][C]1.76949[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.661[/C][C]-1.66103[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.164[/C][C]1.83601[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.3034[/C][C]2.6966[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.2507[/C][C]6.74934[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.705[/C][C]-2.70496[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.1328[/C][C]-2.13276[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.2976[/C][C]0.702388[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.2839[/C][C]0.716052[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.9915[/C][C]2.00847[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.626[/C][C]-3.62601[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.9467[/C][C]-2.94665[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.2086[/C][C]-0.208609[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.1962[/C][C]-2.1962[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.9405[/C][C]-0.940501[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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	41	35.1289	5.87107
2	39	34.6043	4.39575
3	30	35.4941	-5.49414
4	31	34.815	-3.81501
5	34	35.1534	-1.15339
6	35	32.7467	2.25334
7	39	34.8233	4.17674
8	34	34.8613	-0.861344
9	36	34.5512	1.44883
10	37	36.705	0.295028
11	38	34.3325	3.66746
12	36	35.2065	0.793521
13	38	34.8248	3.17516
14	39	36.4291	2.57091
15	33	36.0398	-3.03976
16	32	34.1457	-2.14566
17	36	34.091	1.90899
18	38	37.7089	0.291084
19	39	36.9401	2.05985
20	32	34.3081	-2.30806
21	32	34.9348	-2.93477
22	31	33.5819	-2.5819
23	39	36.9989	2.00106
24	37	36.4431	0.556907
25	39	35.1356	3.86435
26	41	33.9509	7.04911
27	36	35.5755	0.424524
28	33	35.1919	-2.19186
29	33	34.5594	-1.55945
30	34	33.8495	0.150514
31	31	32.6513	-1.65134
32	27	33.2838	-6.28378
33	37	33.6632	3.33677
34	34	35.8815	-1.88151
35	34	32.8195	1.18054
36	32	32.3163	-0.316327
37	29	32.2433	-3.2433
38	36	33.8901	2.10987
39	29	34.3325	-5.33254
40	35	34.6771	0.322942
41	37	34.5048	2.4952
42	34	33.9693	0.0307379
43	38	35.2329	2.76711
44	35	33.9286	1.07139
45	38	32.276	5.72397
46	37	33.2307	3.76932
47	38	35.7353	2.2647
48	33	34.6932	-1.69324
49	36	36.5082	-0.508219
50	38	33.4014	4.59864
51	32	36.0153	-4.01529
52	32	33.0953	-1.09532
53	32	32.9043	-0.904304
54	34	35.8367	-1.83671
55	32	32.6145	-0.614479
56	37	34.3872	2.61278
57	39	34.9691	4.03091
58	29	34.89	-5.88996
59	37	35.1169	1.88311
60	35	34.9284	0.0715541
61	30	31.704	-1.70401
62	38	34.7317	3.26829
63	34	34.6281	-0.6281
64	31	34.61	-3.60997
65	34	33.8984	0.101584
66	35	35.6826	-0.682594
67	36	35.2974	0.702586
68	30	33.1462	-3.14621
69	39	35.5344	3.46557
70	35	35.8815	-0.881513
71	38	34.0157	3.98434
72	31	35.7598	-4.75978
73	34	36.936	-2.93599
74	38	37.597	0.402975
75	34	32.4181	1.58191
76	39	34.079	4.92103
77	37	35.9018	1.09816
78	34	33.8781	0.121907
79	28	32.8944	-4.89444
80	37	32.4181	4.58191
81	33	35.551	-2.551
82	37	37.6298	-0.629787
83	35	35.8815	-0.881513
84	37	34.3039	2.69608
85	32	34.6932	-2.69324
86	33	34.0952	-1.09515
87	38	36.287	1.71297
88	33	34.8108	-1.81084
89	29	34.0707	-5.07068
90	33	33.5918	-0.591763
91	31	34.7844	-3.78442
92	36	33.9915	2.00847
93	35	37.2036	-2.20358
94	32	32.4931	-0.493082
95	29	32.4057	-3.40567
96	39	35.906	3.09401
97	37	34.8452	2.15484
98	35	34.2331	0.766912
99	37	35.0482	1.95178
100	32	35.1575	-3.15755
101	38	35.4334	2.56661
102	37	35.121	1.87897
103	36	36.8855	-0.885471
104	32	32.3936	-0.39363
105	33	36.6805	-3.68048
106	40	32.7502	7.24981
107	38	35.6343	2.36573
108	41	36.5425	4.45746
109	36	34.9628	1.03723
110	43	36.4895	6.51051
111	30	34.89	-4.88996
112	31	33.9671	-2.96707
113	32	37.5179	-5.51791
114	32	33.6143	-1.61428
115	37	34.0646	2.93541
116	37	34.8394	2.16056
117	33	35.9867	-2.98668
118	34	36.6764	-2.67635
119	33	34.2753	-1.27531
120	38	35.4719	2.52813
121	33	34.7781	-1.77809
122	31	32.3593	-1.35929
123	38	35.5386	2.46143
124	37	35.9362	1.06384
125	33	33.4258	-0.425825
126	31	33.9874	-2.98739
127	39	34.3284	4.6716
128	44	37.1734	6.82661
129	33	35.9467	-2.94665
130	35	33.3588	1.64125
131	32	34.6771	-2.67706
132	28	31.8841	-3.88415
133	40	36.4332	3.56675
134	27	32.3672	-5.3672
135	37	35.5427	1.45729
136	32	32.9819	-0.981855
137	28	30.0171	-2.01709
138	34	34.9446	-0.944628
139	30	33.5065	-3.50655
140	35	34.1031	0.896931
141	31	32.7988	-1.79876
142	32	35.4417	-3.4417
143	30	35.0482	-5.04824
144	30	35.0622	-5.06221
145	31	31.3922	-0.392221
146	40	32.5213	7.47867
147	32	33.1869	-1.18686
148	36	34.2305	1.76949
149	32	33.661	-1.66103
150	35	33.164	1.83601
151	38	35.3034	2.6966
152	42	35.2507	6.74934
153	34	36.705	-2.70496
154	35	37.1328	-2.13276
155	35	34.2976	0.702388
156	33	32.2839	0.716052
157	36	33.9915	2.00847
158	32	35.626	-3.62601
159	33	35.9467	-2.94665
160	34	34.2086	-0.208609
161	32	34.1962	-2.1962
162	34	34.9405	-0.940501

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.94717	0.105661	0.0528303
9	0.897013	0.205974	0.102987
10	0.838568	0.322865	0.161432
11	0.763227	0.473545	0.236773
12	0.766084	0.467831	0.233916
13	0.826125	0.34775	0.173875
14	0.779067	0.441865	0.220933
15	0.801197	0.397605	0.198803
16	0.795952	0.408097	0.204048
17	0.734608	0.530783	0.265392
18	0.669004	0.661992	0.330996
19	0.633274	0.733453	0.366726
20	0.635054	0.729893	0.364946
21	0.671586	0.656829	0.328414
22	0.635425	0.729151	0.364575
23	0.602816	0.794367	0.397184
24	0.533574	0.932852	0.466426
25	0.524054	0.951892	0.475946
26	0.766249	0.467502	0.233751
27	0.715199	0.569603	0.284801
28	0.688561	0.622878	0.311439
29	0.656672	0.686656	0.343328
30	0.601835	0.79633	0.398165
31	0.569859	0.860282	0.430141
32	0.742957	0.514085	0.257043
33	0.742259	0.515481	0.257741
34	0.714025	0.57195	0.285975
35	0.670185	0.65963	0.329815
36	0.618377	0.763246	0.381623
37	0.603546	0.792909	0.396454
38	0.573324	0.853351	0.426676
39	0.694173	0.611655	0.305827
40	0.644977	0.710046	0.355023
41	0.620263	0.759475	0.379737
42	0.568062	0.863876	0.431938
43	0.539232	0.921537	0.460768
44	0.493914	0.987828	0.506086
45	0.599995	0.800009	0.400005
46	0.6253	0.749401	0.3747
47	0.599195	0.80161	0.400805
48	0.568636	0.862728	0.431364
49	0.519751	0.960499	0.480249
50	0.5578	0.8844	0.4422
51	0.595853	0.808293	0.404147
52	0.559279	0.881441	0.440721
53	0.518103	0.963794	0.481897
54	0.487957	0.975914	0.512043
55	0.440103	0.880206	0.559897
56	0.424047	0.848093	0.575953
57	0.448769	0.897538	0.551231
58	0.583272	0.833456	0.416728
59	0.551702	0.896596	0.448298
60	0.504745	0.99051	0.495255
61	0.47795	0.9559	0.52205
62	0.476637	0.953275	0.523363
63	0.433362	0.866723	0.566638
64	0.448992	0.897985	0.551008
65	0.404206	0.808412	0.595794
66	0.361886	0.723772	0.638114
67	0.320435	0.640869	0.679565
68	0.334542	0.669083	0.665458
69	0.343851	0.687703	0.656149
70	0.306091	0.612181	0.693909
71	0.328851	0.657703	0.671149
72	0.38914	0.77828	0.61086
73	0.383392	0.766785	0.616608
74	0.340807	0.681613	0.659193
75	0.308509	0.617018	0.691491
76	0.370284	0.740569	0.629716
77	0.332404	0.664807	0.667596
78	0.292623	0.585246	0.707377
79	0.355621	0.711243	0.644379
80	0.407593	0.815186	0.592407
81	0.392414	0.784828	0.607586
82	0.350335	0.700669	0.649665
83	0.311657	0.623313	0.688343
84	0.300956	0.601911	0.699044
85	0.290085	0.58017	0.709915
86	0.25716	0.514319	0.74284
87	0.232973	0.465946	0.767027
88	0.209818	0.419636	0.790182
89	0.265258	0.530515	0.734742
90	0.22983	0.45966	0.77017
91	0.24587	0.491739	0.75413
92	0.224844	0.449688	0.775156
93	0.208088	0.416176	0.791912
94	0.177981	0.355961	0.822019
95	0.182853	0.365706	0.817147
96	0.183114	0.366227	0.816886
97	0.167732	0.335463	0.832268
98	0.144214	0.288428	0.855786
99	0.12892	0.257839	0.87108
100	0.128133	0.256267	0.871867
101	0.119047	0.238093	0.880953
102	0.106494	0.212988	0.893506
103	0.0874121	0.174824	0.912588
104	0.0708005	0.141601	0.9292
105	0.075571	0.151142	0.924429
106	0.19022	0.38044	0.80978
107	0.178118	0.356235	0.821882
108	0.212795	0.425591	0.787205
109	0.191411	0.382821	0.808589
110	0.348416	0.696832	0.651584
111	0.405488	0.810977	0.594512
112	0.383644	0.767287	0.616356
113	0.463962	0.927923	0.536038
114	0.425583	0.851165	0.574417
115	0.406248	0.812496	0.593752
116	0.38398	0.76796	0.61602
117	0.391019	0.782039	0.608981
118	0.376823	0.753647	0.623177
119	0.336917	0.673834	0.663083
120	0.320752	0.641503	0.679248
121	0.288749	0.577498	0.711251
122	0.248388	0.496776	0.751612
123	0.221439	0.442878	0.778561
124	0.185121	0.370243	0.814879
125	0.150886	0.301772	0.849114
126	0.145593	0.291186	0.854407
127	0.192893	0.385785	0.807107
128	0.376498	0.752996	0.623502
129	0.353498	0.706996	0.646502
130	0.314182	0.628364	0.685818
131	0.28065	0.561301	0.71935
132	0.323253	0.646506	0.676747
133	0.412534	0.825067	0.587466
134	0.572093	0.855814	0.427907
135	0.518482	0.963036	0.481518
136	0.455532	0.911064	0.544468
137	0.428125	0.85625	0.571875
138	0.362832	0.725663	0.637168
139	0.464155	0.92831	0.535845
140	0.399105	0.79821	0.600895
141	0.597634	0.804731	0.402366
142	0.533885	0.93223	0.466115
143	0.56361	0.872781	0.43639
144	0.80812	0.38376	0.19188
145	0.745254	0.509493	0.254746
146	0.965927	0.0681459	0.0340729
147	0.956567	0.0868659	0.043433
148	0.983133	0.0337332	0.0168666
149	0.975244	0.0495125	0.0247563
150	0.95465	0.0907007	0.0453504
151	0.931401	0.137198	0.0685989
152	0.993622	0.0127567	0.00637834
153	0.977873	0.0442543	0.0221272
154	0.948082	0.103836	0.051918

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.94717 & 0.105661 & 0.0528303 \tabularnewline
9 & 0.897013 & 0.205974 & 0.102987 \tabularnewline
10 & 0.838568 & 0.322865 & 0.161432 \tabularnewline
11 & 0.763227 & 0.473545 & 0.236773 \tabularnewline
12 & 0.766084 & 0.467831 & 0.233916 \tabularnewline
13 & 0.826125 & 0.34775 & 0.173875 \tabularnewline
14 & 0.779067 & 0.441865 & 0.220933 \tabularnewline
15 & 0.801197 & 0.397605 & 0.198803 \tabularnewline
16 & 0.795952 & 0.408097 & 0.204048 \tabularnewline
17 & 0.734608 & 0.530783 & 0.265392 \tabularnewline
18 & 0.669004 & 0.661992 & 0.330996 \tabularnewline
19 & 0.633274 & 0.733453 & 0.366726 \tabularnewline
20 & 0.635054 & 0.729893 & 0.364946 \tabularnewline
21 & 0.671586 & 0.656829 & 0.328414 \tabularnewline
22 & 0.635425 & 0.729151 & 0.364575 \tabularnewline
23 & 0.602816 & 0.794367 & 0.397184 \tabularnewline
24 & 0.533574 & 0.932852 & 0.466426 \tabularnewline
25 & 0.524054 & 0.951892 & 0.475946 \tabularnewline
26 & 0.766249 & 0.467502 & 0.233751 \tabularnewline
27 & 0.715199 & 0.569603 & 0.284801 \tabularnewline
28 & 0.688561 & 0.622878 & 0.311439 \tabularnewline
29 & 0.656672 & 0.686656 & 0.343328 \tabularnewline
30 & 0.601835 & 0.79633 & 0.398165 \tabularnewline
31 & 0.569859 & 0.860282 & 0.430141 \tabularnewline
32 & 0.742957 & 0.514085 & 0.257043 \tabularnewline
33 & 0.742259 & 0.515481 & 0.257741 \tabularnewline
34 & 0.714025 & 0.57195 & 0.285975 \tabularnewline
35 & 0.670185 & 0.65963 & 0.329815 \tabularnewline
36 & 0.618377 & 0.763246 & 0.381623 \tabularnewline
37 & 0.603546 & 0.792909 & 0.396454 \tabularnewline
38 & 0.573324 & 0.853351 & 0.426676 \tabularnewline
39 & 0.694173 & 0.611655 & 0.305827 \tabularnewline
40 & 0.644977 & 0.710046 & 0.355023 \tabularnewline
41 & 0.620263 & 0.759475 & 0.379737 \tabularnewline
42 & 0.568062 & 0.863876 & 0.431938 \tabularnewline
43 & 0.539232 & 0.921537 & 0.460768 \tabularnewline
44 & 0.493914 & 0.987828 & 0.506086 \tabularnewline
45 & 0.599995 & 0.800009 & 0.400005 \tabularnewline
46 & 0.6253 & 0.749401 & 0.3747 \tabularnewline
47 & 0.599195 & 0.80161 & 0.400805 \tabularnewline
48 & 0.568636 & 0.862728 & 0.431364 \tabularnewline
49 & 0.519751 & 0.960499 & 0.480249 \tabularnewline
50 & 0.5578 & 0.8844 & 0.4422 \tabularnewline
51 & 0.595853 & 0.808293 & 0.404147 \tabularnewline
52 & 0.559279 & 0.881441 & 0.440721 \tabularnewline
53 & 0.518103 & 0.963794 & 0.481897 \tabularnewline
54 & 0.487957 & 0.975914 & 0.512043 \tabularnewline
55 & 0.440103 & 0.880206 & 0.559897 \tabularnewline
56 & 0.424047 & 0.848093 & 0.575953 \tabularnewline
57 & 0.448769 & 0.897538 & 0.551231 \tabularnewline
58 & 0.583272 & 0.833456 & 0.416728 \tabularnewline
59 & 0.551702 & 0.896596 & 0.448298 \tabularnewline
60 & 0.504745 & 0.99051 & 0.495255 \tabularnewline
61 & 0.47795 & 0.9559 & 0.52205 \tabularnewline
62 & 0.476637 & 0.953275 & 0.523363 \tabularnewline
63 & 0.433362 & 0.866723 & 0.566638 \tabularnewline
64 & 0.448992 & 0.897985 & 0.551008 \tabularnewline
65 & 0.404206 & 0.808412 & 0.595794 \tabularnewline
66 & 0.361886 & 0.723772 & 0.638114 \tabularnewline
67 & 0.320435 & 0.640869 & 0.679565 \tabularnewline
68 & 0.334542 & 0.669083 & 0.665458 \tabularnewline
69 & 0.343851 & 0.687703 & 0.656149 \tabularnewline
70 & 0.306091 & 0.612181 & 0.693909 \tabularnewline
71 & 0.328851 & 0.657703 & 0.671149 \tabularnewline
72 & 0.38914 & 0.77828 & 0.61086 \tabularnewline
73 & 0.383392 & 0.766785 & 0.616608 \tabularnewline
74 & 0.340807 & 0.681613 & 0.659193 \tabularnewline
75 & 0.308509 & 0.617018 & 0.691491 \tabularnewline
76 & 0.370284 & 0.740569 & 0.629716 \tabularnewline
77 & 0.332404 & 0.664807 & 0.667596 \tabularnewline
78 & 0.292623 & 0.585246 & 0.707377 \tabularnewline
79 & 0.355621 & 0.711243 & 0.644379 \tabularnewline
80 & 0.407593 & 0.815186 & 0.592407 \tabularnewline
81 & 0.392414 & 0.784828 & 0.607586 \tabularnewline
82 & 0.350335 & 0.700669 & 0.649665 \tabularnewline
83 & 0.311657 & 0.623313 & 0.688343 \tabularnewline
84 & 0.300956 & 0.601911 & 0.699044 \tabularnewline
85 & 0.290085 & 0.58017 & 0.709915 \tabularnewline
86 & 0.25716 & 0.514319 & 0.74284 \tabularnewline
87 & 0.232973 & 0.465946 & 0.767027 \tabularnewline
88 & 0.209818 & 0.419636 & 0.790182 \tabularnewline
89 & 0.265258 & 0.530515 & 0.734742 \tabularnewline
90 & 0.22983 & 0.45966 & 0.77017 \tabularnewline
91 & 0.24587 & 0.491739 & 0.75413 \tabularnewline
92 & 0.224844 & 0.449688 & 0.775156 \tabularnewline
93 & 0.208088 & 0.416176 & 0.791912 \tabularnewline
94 & 0.177981 & 0.355961 & 0.822019 \tabularnewline
95 & 0.182853 & 0.365706 & 0.817147 \tabularnewline
96 & 0.183114 & 0.366227 & 0.816886 \tabularnewline
97 & 0.167732 & 0.335463 & 0.832268 \tabularnewline
98 & 0.144214 & 0.288428 & 0.855786 \tabularnewline
99 & 0.12892 & 0.257839 & 0.87108 \tabularnewline
100 & 0.128133 & 0.256267 & 0.871867 \tabularnewline
101 & 0.119047 & 0.238093 & 0.880953 \tabularnewline
102 & 0.106494 & 0.212988 & 0.893506 \tabularnewline
103 & 0.0874121 & 0.174824 & 0.912588 \tabularnewline
104 & 0.0708005 & 0.141601 & 0.9292 \tabularnewline
105 & 0.075571 & 0.151142 & 0.924429 \tabularnewline
106 & 0.19022 & 0.38044 & 0.80978 \tabularnewline
107 & 0.178118 & 0.356235 & 0.821882 \tabularnewline
108 & 0.212795 & 0.425591 & 0.787205 \tabularnewline
109 & 0.191411 & 0.382821 & 0.808589 \tabularnewline
110 & 0.348416 & 0.696832 & 0.651584 \tabularnewline
111 & 0.405488 & 0.810977 & 0.594512 \tabularnewline
112 & 0.383644 & 0.767287 & 0.616356 \tabularnewline
113 & 0.463962 & 0.927923 & 0.536038 \tabularnewline
114 & 0.425583 & 0.851165 & 0.574417 \tabularnewline
115 & 0.406248 & 0.812496 & 0.593752 \tabularnewline
116 & 0.38398 & 0.76796 & 0.61602 \tabularnewline
117 & 0.391019 & 0.782039 & 0.608981 \tabularnewline
118 & 0.376823 & 0.753647 & 0.623177 \tabularnewline
119 & 0.336917 & 0.673834 & 0.663083 \tabularnewline
120 & 0.320752 & 0.641503 & 0.679248 \tabularnewline
121 & 0.288749 & 0.577498 & 0.711251 \tabularnewline
122 & 0.248388 & 0.496776 & 0.751612 \tabularnewline
123 & 0.221439 & 0.442878 & 0.778561 \tabularnewline
124 & 0.185121 & 0.370243 & 0.814879 \tabularnewline
125 & 0.150886 & 0.301772 & 0.849114 \tabularnewline
126 & 0.145593 & 0.291186 & 0.854407 \tabularnewline
127 & 0.192893 & 0.385785 & 0.807107 \tabularnewline
128 & 0.376498 & 0.752996 & 0.623502 \tabularnewline
129 & 0.353498 & 0.706996 & 0.646502 \tabularnewline
130 & 0.314182 & 0.628364 & 0.685818 \tabularnewline
131 & 0.28065 & 0.561301 & 0.71935 \tabularnewline
132 & 0.323253 & 0.646506 & 0.676747 \tabularnewline
133 & 0.412534 & 0.825067 & 0.587466 \tabularnewline
134 & 0.572093 & 0.855814 & 0.427907 \tabularnewline
135 & 0.518482 & 0.963036 & 0.481518 \tabularnewline
136 & 0.455532 & 0.911064 & 0.544468 \tabularnewline
137 & 0.428125 & 0.85625 & 0.571875 \tabularnewline
138 & 0.362832 & 0.725663 & 0.637168 \tabularnewline
139 & 0.464155 & 0.92831 & 0.535845 \tabularnewline
140 & 0.399105 & 0.79821 & 0.600895 \tabularnewline
141 & 0.597634 & 0.804731 & 0.402366 \tabularnewline
142 & 0.533885 & 0.93223 & 0.466115 \tabularnewline
143 & 0.56361 & 0.872781 & 0.43639 \tabularnewline
144 & 0.80812 & 0.38376 & 0.19188 \tabularnewline
145 & 0.745254 & 0.509493 & 0.254746 \tabularnewline
146 & 0.965927 & 0.0681459 & 0.0340729 \tabularnewline
147 & 0.956567 & 0.0868659 & 0.043433 \tabularnewline
148 & 0.983133 & 0.0337332 & 0.0168666 \tabularnewline
149 & 0.975244 & 0.0495125 & 0.0247563 \tabularnewline
150 & 0.95465 & 0.0907007 & 0.0453504 \tabularnewline
151 & 0.931401 & 0.137198 & 0.0685989 \tabularnewline
152 & 0.993622 & 0.0127567 & 0.00637834 \tabularnewline
153 & 0.977873 & 0.0442543 & 0.0221272 \tabularnewline
154 & 0.948082 & 0.103836 & 0.051918 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&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]8[/C][C]0.94717[/C][C]0.105661[/C][C]0.0528303[/C][/ROW]
[ROW][C]9[/C][C]0.897013[/C][C]0.205974[/C][C]0.102987[/C][/ROW]
[ROW][C]10[/C][C]0.838568[/C][C]0.322865[/C][C]0.161432[/C][/ROW]
[ROW][C]11[/C][C]0.763227[/C][C]0.473545[/C][C]0.236773[/C][/ROW]
[ROW][C]12[/C][C]0.766084[/C][C]0.467831[/C][C]0.233916[/C][/ROW]
[ROW][C]13[/C][C]0.826125[/C][C]0.34775[/C][C]0.173875[/C][/ROW]
[ROW][C]14[/C][C]0.779067[/C][C]0.441865[/C][C]0.220933[/C][/ROW]
[ROW][C]15[/C][C]0.801197[/C][C]0.397605[/C][C]0.198803[/C][/ROW]
[ROW][C]16[/C][C]0.795952[/C][C]0.408097[/C][C]0.204048[/C][/ROW]
[ROW][C]17[/C][C]0.734608[/C][C]0.530783[/C][C]0.265392[/C][/ROW]
[ROW][C]18[/C][C]0.669004[/C][C]0.661992[/C][C]0.330996[/C][/ROW]
[ROW][C]19[/C][C]0.633274[/C][C]0.733453[/C][C]0.366726[/C][/ROW]
[ROW][C]20[/C][C]0.635054[/C][C]0.729893[/C][C]0.364946[/C][/ROW]
[ROW][C]21[/C][C]0.671586[/C][C]0.656829[/C][C]0.328414[/C][/ROW]
[ROW][C]22[/C][C]0.635425[/C][C]0.729151[/C][C]0.364575[/C][/ROW]
[ROW][C]23[/C][C]0.602816[/C][C]0.794367[/C][C]0.397184[/C][/ROW]
[ROW][C]24[/C][C]0.533574[/C][C]0.932852[/C][C]0.466426[/C][/ROW]
[ROW][C]25[/C][C]0.524054[/C][C]0.951892[/C][C]0.475946[/C][/ROW]
[ROW][C]26[/C][C]0.766249[/C][C]0.467502[/C][C]0.233751[/C][/ROW]
[ROW][C]27[/C][C]0.715199[/C][C]0.569603[/C][C]0.284801[/C][/ROW]
[ROW][C]28[/C][C]0.688561[/C][C]0.622878[/C][C]0.311439[/C][/ROW]
[ROW][C]29[/C][C]0.656672[/C][C]0.686656[/C][C]0.343328[/C][/ROW]
[ROW][C]30[/C][C]0.601835[/C][C]0.79633[/C][C]0.398165[/C][/ROW]
[ROW][C]31[/C][C]0.569859[/C][C]0.860282[/C][C]0.430141[/C][/ROW]
[ROW][C]32[/C][C]0.742957[/C][C]0.514085[/C][C]0.257043[/C][/ROW]
[ROW][C]33[/C][C]0.742259[/C][C]0.515481[/C][C]0.257741[/C][/ROW]
[ROW][C]34[/C][C]0.714025[/C][C]0.57195[/C][C]0.285975[/C][/ROW]
[ROW][C]35[/C][C]0.670185[/C][C]0.65963[/C][C]0.329815[/C][/ROW]
[ROW][C]36[/C][C]0.618377[/C][C]0.763246[/C][C]0.381623[/C][/ROW]
[ROW][C]37[/C][C]0.603546[/C][C]0.792909[/C][C]0.396454[/C][/ROW]
[ROW][C]38[/C][C]0.573324[/C][C]0.853351[/C][C]0.426676[/C][/ROW]
[ROW][C]39[/C][C]0.694173[/C][C]0.611655[/C][C]0.305827[/C][/ROW]
[ROW][C]40[/C][C]0.644977[/C][C]0.710046[/C][C]0.355023[/C][/ROW]
[ROW][C]41[/C][C]0.620263[/C][C]0.759475[/C][C]0.379737[/C][/ROW]
[ROW][C]42[/C][C]0.568062[/C][C]0.863876[/C][C]0.431938[/C][/ROW]
[ROW][C]43[/C][C]0.539232[/C][C]0.921537[/C][C]0.460768[/C][/ROW]
[ROW][C]44[/C][C]0.493914[/C][C]0.987828[/C][C]0.506086[/C][/ROW]
[ROW][C]45[/C][C]0.599995[/C][C]0.800009[/C][C]0.400005[/C][/ROW]
[ROW][C]46[/C][C]0.6253[/C][C]0.749401[/C][C]0.3747[/C][/ROW]
[ROW][C]47[/C][C]0.599195[/C][C]0.80161[/C][C]0.400805[/C][/ROW]
[ROW][C]48[/C][C]0.568636[/C][C]0.862728[/C][C]0.431364[/C][/ROW]
[ROW][C]49[/C][C]0.519751[/C][C]0.960499[/C][C]0.480249[/C][/ROW]
[ROW][C]50[/C][C]0.5578[/C][C]0.8844[/C][C]0.4422[/C][/ROW]
[ROW][C]51[/C][C]0.595853[/C][C]0.808293[/C][C]0.404147[/C][/ROW]
[ROW][C]52[/C][C]0.559279[/C][C]0.881441[/C][C]0.440721[/C][/ROW]
[ROW][C]53[/C][C]0.518103[/C][C]0.963794[/C][C]0.481897[/C][/ROW]
[ROW][C]54[/C][C]0.487957[/C][C]0.975914[/C][C]0.512043[/C][/ROW]
[ROW][C]55[/C][C]0.440103[/C][C]0.880206[/C][C]0.559897[/C][/ROW]
[ROW][C]56[/C][C]0.424047[/C][C]0.848093[/C][C]0.575953[/C][/ROW]
[ROW][C]57[/C][C]0.448769[/C][C]0.897538[/C][C]0.551231[/C][/ROW]
[ROW][C]58[/C][C]0.583272[/C][C]0.833456[/C][C]0.416728[/C][/ROW]
[ROW][C]59[/C][C]0.551702[/C][C]0.896596[/C][C]0.448298[/C][/ROW]
[ROW][C]60[/C][C]0.504745[/C][C]0.99051[/C][C]0.495255[/C][/ROW]
[ROW][C]61[/C][C]0.47795[/C][C]0.9559[/C][C]0.52205[/C][/ROW]
[ROW][C]62[/C][C]0.476637[/C][C]0.953275[/C][C]0.523363[/C][/ROW]
[ROW][C]63[/C][C]0.433362[/C][C]0.866723[/C][C]0.566638[/C][/ROW]
[ROW][C]64[/C][C]0.448992[/C][C]0.897985[/C][C]0.551008[/C][/ROW]
[ROW][C]65[/C][C]0.404206[/C][C]0.808412[/C][C]0.595794[/C][/ROW]
[ROW][C]66[/C][C]0.361886[/C][C]0.723772[/C][C]0.638114[/C][/ROW]
[ROW][C]67[/C][C]0.320435[/C][C]0.640869[/C][C]0.679565[/C][/ROW]
[ROW][C]68[/C][C]0.334542[/C][C]0.669083[/C][C]0.665458[/C][/ROW]
[ROW][C]69[/C][C]0.343851[/C][C]0.687703[/C][C]0.656149[/C][/ROW]
[ROW][C]70[/C][C]0.306091[/C][C]0.612181[/C][C]0.693909[/C][/ROW]
[ROW][C]71[/C][C]0.328851[/C][C]0.657703[/C][C]0.671149[/C][/ROW]
[ROW][C]72[/C][C]0.38914[/C][C]0.77828[/C][C]0.61086[/C][/ROW]
[ROW][C]73[/C][C]0.383392[/C][C]0.766785[/C][C]0.616608[/C][/ROW]
[ROW][C]74[/C][C]0.340807[/C][C]0.681613[/C][C]0.659193[/C][/ROW]
[ROW][C]75[/C][C]0.308509[/C][C]0.617018[/C][C]0.691491[/C][/ROW]
[ROW][C]76[/C][C]0.370284[/C][C]0.740569[/C][C]0.629716[/C][/ROW]
[ROW][C]77[/C][C]0.332404[/C][C]0.664807[/C][C]0.667596[/C][/ROW]
[ROW][C]78[/C][C]0.292623[/C][C]0.585246[/C][C]0.707377[/C][/ROW]
[ROW][C]79[/C][C]0.355621[/C][C]0.711243[/C][C]0.644379[/C][/ROW]
[ROW][C]80[/C][C]0.407593[/C][C]0.815186[/C][C]0.592407[/C][/ROW]
[ROW][C]81[/C][C]0.392414[/C][C]0.784828[/C][C]0.607586[/C][/ROW]
[ROW][C]82[/C][C]0.350335[/C][C]0.700669[/C][C]0.649665[/C][/ROW]
[ROW][C]83[/C][C]0.311657[/C][C]0.623313[/C][C]0.688343[/C][/ROW]
[ROW][C]84[/C][C]0.300956[/C][C]0.601911[/C][C]0.699044[/C][/ROW]
[ROW][C]85[/C][C]0.290085[/C][C]0.58017[/C][C]0.709915[/C][/ROW]
[ROW][C]86[/C][C]0.25716[/C][C]0.514319[/C][C]0.74284[/C][/ROW]
[ROW][C]87[/C][C]0.232973[/C][C]0.465946[/C][C]0.767027[/C][/ROW]
[ROW][C]88[/C][C]0.209818[/C][C]0.419636[/C][C]0.790182[/C][/ROW]
[ROW][C]89[/C][C]0.265258[/C][C]0.530515[/C][C]0.734742[/C][/ROW]
[ROW][C]90[/C][C]0.22983[/C][C]0.45966[/C][C]0.77017[/C][/ROW]
[ROW][C]91[/C][C]0.24587[/C][C]0.491739[/C][C]0.75413[/C][/ROW]
[ROW][C]92[/C][C]0.224844[/C][C]0.449688[/C][C]0.775156[/C][/ROW]
[ROW][C]93[/C][C]0.208088[/C][C]0.416176[/C][C]0.791912[/C][/ROW]
[ROW][C]94[/C][C]0.177981[/C][C]0.355961[/C][C]0.822019[/C][/ROW]
[ROW][C]95[/C][C]0.182853[/C][C]0.365706[/C][C]0.817147[/C][/ROW]
[ROW][C]96[/C][C]0.183114[/C][C]0.366227[/C][C]0.816886[/C][/ROW]
[ROW][C]97[/C][C]0.167732[/C][C]0.335463[/C][C]0.832268[/C][/ROW]
[ROW][C]98[/C][C]0.144214[/C][C]0.288428[/C][C]0.855786[/C][/ROW]
[ROW][C]99[/C][C]0.12892[/C][C]0.257839[/C][C]0.87108[/C][/ROW]
[ROW][C]100[/C][C]0.128133[/C][C]0.256267[/C][C]0.871867[/C][/ROW]
[ROW][C]101[/C][C]0.119047[/C][C]0.238093[/C][C]0.880953[/C][/ROW]
[ROW][C]102[/C][C]0.106494[/C][C]0.212988[/C][C]0.893506[/C][/ROW]
[ROW][C]103[/C][C]0.0874121[/C][C]0.174824[/C][C]0.912588[/C][/ROW]
[ROW][C]104[/C][C]0.0708005[/C][C]0.141601[/C][C]0.9292[/C][/ROW]
[ROW][C]105[/C][C]0.075571[/C][C]0.151142[/C][C]0.924429[/C][/ROW]
[ROW][C]106[/C][C]0.19022[/C][C]0.38044[/C][C]0.80978[/C][/ROW]
[ROW][C]107[/C][C]0.178118[/C][C]0.356235[/C][C]0.821882[/C][/ROW]
[ROW][C]108[/C][C]0.212795[/C][C]0.425591[/C][C]0.787205[/C][/ROW]
[ROW][C]109[/C][C]0.191411[/C][C]0.382821[/C][C]0.808589[/C][/ROW]
[ROW][C]110[/C][C]0.348416[/C][C]0.696832[/C][C]0.651584[/C][/ROW]
[ROW][C]111[/C][C]0.405488[/C][C]0.810977[/C][C]0.594512[/C][/ROW]
[ROW][C]112[/C][C]0.383644[/C][C]0.767287[/C][C]0.616356[/C][/ROW]
[ROW][C]113[/C][C]0.463962[/C][C]0.927923[/C][C]0.536038[/C][/ROW]
[ROW][C]114[/C][C]0.425583[/C][C]0.851165[/C][C]0.574417[/C][/ROW]
[ROW][C]115[/C][C]0.406248[/C][C]0.812496[/C][C]0.593752[/C][/ROW]
[ROW][C]116[/C][C]0.38398[/C][C]0.76796[/C][C]0.61602[/C][/ROW]
[ROW][C]117[/C][C]0.391019[/C][C]0.782039[/C][C]0.608981[/C][/ROW]
[ROW][C]118[/C][C]0.376823[/C][C]0.753647[/C][C]0.623177[/C][/ROW]
[ROW][C]119[/C][C]0.336917[/C][C]0.673834[/C][C]0.663083[/C][/ROW]
[ROW][C]120[/C][C]0.320752[/C][C]0.641503[/C][C]0.679248[/C][/ROW]
[ROW][C]121[/C][C]0.288749[/C][C]0.577498[/C][C]0.711251[/C][/ROW]
[ROW][C]122[/C][C]0.248388[/C][C]0.496776[/C][C]0.751612[/C][/ROW]
[ROW][C]123[/C][C]0.221439[/C][C]0.442878[/C][C]0.778561[/C][/ROW]
[ROW][C]124[/C][C]0.185121[/C][C]0.370243[/C][C]0.814879[/C][/ROW]
[ROW][C]125[/C][C]0.150886[/C][C]0.301772[/C][C]0.849114[/C][/ROW]
[ROW][C]126[/C][C]0.145593[/C][C]0.291186[/C][C]0.854407[/C][/ROW]
[ROW][C]127[/C][C]0.192893[/C][C]0.385785[/C][C]0.807107[/C][/ROW]
[ROW][C]128[/C][C]0.376498[/C][C]0.752996[/C][C]0.623502[/C][/ROW]
[ROW][C]129[/C][C]0.353498[/C][C]0.706996[/C][C]0.646502[/C][/ROW]
[ROW][C]130[/C][C]0.314182[/C][C]0.628364[/C][C]0.685818[/C][/ROW]
[ROW][C]131[/C][C]0.28065[/C][C]0.561301[/C][C]0.71935[/C][/ROW]
[ROW][C]132[/C][C]0.323253[/C][C]0.646506[/C][C]0.676747[/C][/ROW]
[ROW][C]133[/C][C]0.412534[/C][C]0.825067[/C][C]0.587466[/C][/ROW]
[ROW][C]134[/C][C]0.572093[/C][C]0.855814[/C][C]0.427907[/C][/ROW]
[ROW][C]135[/C][C]0.518482[/C][C]0.963036[/C][C]0.481518[/C][/ROW]
[ROW][C]136[/C][C]0.455532[/C][C]0.911064[/C][C]0.544468[/C][/ROW]
[ROW][C]137[/C][C]0.428125[/C][C]0.85625[/C][C]0.571875[/C][/ROW]
[ROW][C]138[/C][C]0.362832[/C][C]0.725663[/C][C]0.637168[/C][/ROW]
[ROW][C]139[/C][C]0.464155[/C][C]0.92831[/C][C]0.535845[/C][/ROW]
[ROW][C]140[/C][C]0.399105[/C][C]0.79821[/C][C]0.600895[/C][/ROW]
[ROW][C]141[/C][C]0.597634[/C][C]0.804731[/C][C]0.402366[/C][/ROW]
[ROW][C]142[/C][C]0.533885[/C][C]0.93223[/C][C]0.466115[/C][/ROW]
[ROW][C]143[/C][C]0.56361[/C][C]0.872781[/C][C]0.43639[/C][/ROW]
[ROW][C]144[/C][C]0.80812[/C][C]0.38376[/C][C]0.19188[/C][/ROW]
[ROW][C]145[/C][C]0.745254[/C][C]0.509493[/C][C]0.254746[/C][/ROW]
[ROW][C]146[/C][C]0.965927[/C][C]0.0681459[/C][C]0.0340729[/C][/ROW]
[ROW][C]147[/C][C]0.956567[/C][C]0.0868659[/C][C]0.043433[/C][/ROW]
[ROW][C]148[/C][C]0.983133[/C][C]0.0337332[/C][C]0.0168666[/C][/ROW]
[ROW][C]149[/C][C]0.975244[/C][C]0.0495125[/C][C]0.0247563[/C][/ROW]
[ROW][C]150[/C][C]0.95465[/C][C]0.0907007[/C][C]0.0453504[/C][/ROW]
[ROW][C]151[/C][C]0.931401[/C][C]0.137198[/C][C]0.0685989[/C][/ROW]
[ROW][C]152[/C][C]0.993622[/C][C]0.0127567[/C][C]0.00637834[/C][/ROW]
[ROW][C]153[/C][C]0.977873[/C][C]0.0442543[/C][C]0.0221272[/C][/ROW]
[ROW][C]154[/C][C]0.948082[/C][C]0.103836[/C][C]0.051918[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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
8	0.94717	0.105661	0.0528303
9	0.897013	0.205974	0.102987
10	0.838568	0.322865	0.161432
11	0.763227	0.473545	0.236773
12	0.766084	0.467831	0.233916
13	0.826125	0.34775	0.173875
14	0.779067	0.441865	0.220933
15	0.801197	0.397605	0.198803
16	0.795952	0.408097	0.204048
17	0.734608	0.530783	0.265392
18	0.669004	0.661992	0.330996
19	0.633274	0.733453	0.366726
20	0.635054	0.729893	0.364946
21	0.671586	0.656829	0.328414
22	0.635425	0.729151	0.364575
23	0.602816	0.794367	0.397184
24	0.533574	0.932852	0.466426
25	0.524054	0.951892	0.475946
26	0.766249	0.467502	0.233751
27	0.715199	0.569603	0.284801
28	0.688561	0.622878	0.311439
29	0.656672	0.686656	0.343328
30	0.601835	0.79633	0.398165
31	0.569859	0.860282	0.430141
32	0.742957	0.514085	0.257043
33	0.742259	0.515481	0.257741
34	0.714025	0.57195	0.285975
35	0.670185	0.65963	0.329815
36	0.618377	0.763246	0.381623
37	0.603546	0.792909	0.396454
38	0.573324	0.853351	0.426676
39	0.694173	0.611655	0.305827
40	0.644977	0.710046	0.355023
41	0.620263	0.759475	0.379737
42	0.568062	0.863876	0.431938
43	0.539232	0.921537	0.460768
44	0.493914	0.987828	0.506086
45	0.599995	0.800009	0.400005
46	0.6253	0.749401	0.3747
47	0.599195	0.80161	0.400805
48	0.568636	0.862728	0.431364
49	0.519751	0.960499	0.480249
50	0.5578	0.8844	0.4422
51	0.595853	0.808293	0.404147
52	0.559279	0.881441	0.440721
53	0.518103	0.963794	0.481897
54	0.487957	0.975914	0.512043
55	0.440103	0.880206	0.559897
56	0.424047	0.848093	0.575953
57	0.448769	0.897538	0.551231
58	0.583272	0.833456	0.416728
59	0.551702	0.896596	0.448298
60	0.504745	0.99051	0.495255
61	0.47795	0.9559	0.52205
62	0.476637	0.953275	0.523363
63	0.433362	0.866723	0.566638
64	0.448992	0.897985	0.551008
65	0.404206	0.808412	0.595794
66	0.361886	0.723772	0.638114
67	0.320435	0.640869	0.679565
68	0.334542	0.669083	0.665458
69	0.343851	0.687703	0.656149
70	0.306091	0.612181	0.693909
71	0.328851	0.657703	0.671149
72	0.38914	0.77828	0.61086
73	0.383392	0.766785	0.616608
74	0.340807	0.681613	0.659193
75	0.308509	0.617018	0.691491
76	0.370284	0.740569	0.629716
77	0.332404	0.664807	0.667596
78	0.292623	0.585246	0.707377
79	0.355621	0.711243	0.644379
80	0.407593	0.815186	0.592407
81	0.392414	0.784828	0.607586
82	0.350335	0.700669	0.649665
83	0.311657	0.623313	0.688343
84	0.300956	0.601911	0.699044
85	0.290085	0.58017	0.709915
86	0.25716	0.514319	0.74284
87	0.232973	0.465946	0.767027
88	0.209818	0.419636	0.790182
89	0.265258	0.530515	0.734742
90	0.22983	0.45966	0.77017
91	0.24587	0.491739	0.75413
92	0.224844	0.449688	0.775156
93	0.208088	0.416176	0.791912
94	0.177981	0.355961	0.822019
95	0.182853	0.365706	0.817147
96	0.183114	0.366227	0.816886
97	0.167732	0.335463	0.832268
98	0.144214	0.288428	0.855786
99	0.12892	0.257839	0.87108
100	0.128133	0.256267	0.871867
101	0.119047	0.238093	0.880953
102	0.106494	0.212988	0.893506
103	0.0874121	0.174824	0.912588
104	0.0708005	0.141601	0.9292
105	0.075571	0.151142	0.924429
106	0.19022	0.38044	0.80978
107	0.178118	0.356235	0.821882
108	0.212795	0.425591	0.787205
109	0.191411	0.382821	0.808589
110	0.348416	0.696832	0.651584
111	0.405488	0.810977	0.594512
112	0.383644	0.767287	0.616356
113	0.463962	0.927923	0.536038
114	0.425583	0.851165	0.574417
115	0.406248	0.812496	0.593752
116	0.38398	0.76796	0.61602
117	0.391019	0.782039	0.608981
118	0.376823	0.753647	0.623177
119	0.336917	0.673834	0.663083
120	0.320752	0.641503	0.679248
121	0.288749	0.577498	0.711251
122	0.248388	0.496776	0.751612
123	0.221439	0.442878	0.778561
124	0.185121	0.370243	0.814879
125	0.150886	0.301772	0.849114
126	0.145593	0.291186	0.854407
127	0.192893	0.385785	0.807107
128	0.376498	0.752996	0.623502
129	0.353498	0.706996	0.646502
130	0.314182	0.628364	0.685818
131	0.28065	0.561301	0.71935
132	0.323253	0.646506	0.676747
133	0.412534	0.825067	0.587466
134	0.572093	0.855814	0.427907
135	0.518482	0.963036	0.481518
136	0.455532	0.911064	0.544468
137	0.428125	0.85625	0.571875
138	0.362832	0.725663	0.637168
139	0.464155	0.92831	0.535845
140	0.399105	0.79821	0.600895
141	0.597634	0.804731	0.402366
142	0.533885	0.93223	0.466115
143	0.56361	0.872781	0.43639
144	0.80812	0.38376	0.19188
145	0.745254	0.509493	0.254746
146	0.965927	0.0681459	0.0340729
147	0.956567	0.0868659	0.043433
148	0.983133	0.0337332	0.0168666
149	0.975244	0.0495125	0.0247563
150	0.95465	0.0907007	0.0453504
151	0.931401	0.137198	0.0685989
152	0.993622	0.0127567	0.00637834
153	0.977873	0.0442543	0.0221272
154	0.948082	0.103836	0.051918

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	4	0.0272109	OK
10% type I error level	7	0.047619	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 & 4 & 0.0272109 & OK \tabularnewline
10% type I error level & 7 & 0.047619 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253603&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]4[/C][C]0.0272109[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.047619[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253603&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253603&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	4	0.0272109	OK
10% type I error level	7	0.047619	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