Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 11 Dec 2011 12:01:44 -0500

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/2011/Dec/11/t1323622930o3ru3xw4avg2753.htm/, Retrieved Mon, 29 Apr 2024 03:24:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153826, Retrieved Mon, 29 Apr 2024 03:24:41 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

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] [] [2011-12-11 16:16:05] [ee8c3a74bf3b349877806e9a50913c60]
- R PD    [Multiple Regression] [] [2011-12-11 17:01:44] [7dc03dd48c8acabd98b217fada4a6bc0] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

96560	76	129	17	22996	0	0	78	62	72
112611	41	36	20	26706	3	7	44	56	64
98146	40	37	17	27114	0	1	80	49	66
121848	39	45	17	30594	2	0	73	63	78
31774	23	48	17	4143	1	2	107	67	71
65475	18	24	13	69008	1	0	42	59	71
108446	60	90	17	46300	0	2	76	40	59
76302	31	26	20	30976	2	2	69	34	65
30989	14	35	17	4154	-2	0	62	37	48
150580	77	124	22	45588	-4	0	46	61	72
59382	49	49	12	26263	1	0	133	60	66
341570	168	190	21	117105	0	0	71	57	68
133328	55	79	20	40909	-3	0	46	56	75
101523	42	76	22	61056	0	1	131	67	73
92499	32	57	18	21399	-2	2	47	38	59
118612	46	72	12	30080	-2	0	15	49	72
98104	54	132	17	25568	-3	0	37	32	65
84105	20	45	17	20055	0	1	0	63	69
237213	84	74	38	66198	0	3	79	67	71
133131	55	52	30	57793	4	3	77	43	54
344297	75	86	30	67654	1	5	101	84	84
174415	100	63	31	82753	3	0	105	49	66
294424	77	59	33	101494	4	2	124	58	73
362301	119	715	34	100708	2	0	83	63	69
167488	45	77	28	83737	0	0	106	29	70
152299	53	63	33	61370	2	2	25	58	72
243511	71	65	42	101338	0	2	16	62	63
132487	41	97	36	40735	3	1	22	54	66
172494	52	52	43	86687	3	0	29	53	60
224330	83	52	39	130115	0	0	5	66	66
181633	70	48	30	64466	6	0	27	53	71
210907	56	81	30	112285	2	0	29	26	50
236785	119	75	31	101481	0	5	43	43	52
244052	68	66	44	143558	2	0	158	53	70
143756	46	57	34	69094	4	4	102	54	60
182079	63	63	33	102860	2	0	123	47	76
100750	72	67	30	140867	3	0	105	43	60
152474	65	45	32	65567	0	1	33	57	70
97839	38	42	24	94785	-1	2	96	41	75
149061	44	66	26	116174	0	6	56	37	54
324799	154	108	47	97668	0	5	59	52	65
230964	53	43	30	133824	-1	0	91	52	73
174724	92	135	34	69112	0	1	76	67	42
223632	73	52	33	72654	-1	1	94	70	65
106408	30	32	14	31081	1	2	41	68	75
265769	146	37	32	83122	-2	5	67	43	66
149112	56	65	35	60578	-4	2	100	56	70
152871	58	74	28	79892	2	5	67	74	81
183167	66	66	39	82875	-4	1	135	58	71
218946	41	112	29	80670	2	4	58	63	68
196553	57	50	29	95260	-3	0	56	64	67
143246	103	42	27	106671	2	0	59	53	76
193339	78	47	35	84651	2	1	116	51	71
130585	46	57	29	95364	-4	2	98	54	70
148446	91	63	37	126846	3	8	32	48	65
243060	63	110	29	111813	-1	4	63	50	68
317394	86	53	31	91413	-3	0	113	45	70
244749	95	144	33	76643	0	1	111	61	64
128423	64	89	38	92696	2	10	120	56	70
229242	247	128	31	91721	2	0	25	46	66
324598	110	128	37	135777	2	1	109	51	59
195838	67	50	31	102372	-2	0	37	37	78
254488	83	50	39	103772	0	2	54	42	67
271856	103	91	37	54990	-3	2	55	69	67
95227	34	70	32	34777	3	0	17	56	61

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
revieuws[t] = + 21.817846268231 + 4.46194648588496e-05tijd[t] -0.00523554557071073login[t] -0.0122519036242291vieuws[t] + 9.97551507647241e-05size[t] + 0.325785484834069test[t] + 0.365758721516533shared[t] + 0.000622429043409637blogged[t] + 0.109185135487459intrisieke[t] -0.204284883129327extrisieke[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
revieuws[t] =  +  21.817846268231 +  4.46194648588496e-05tijd[t] -0.00523554557071073login[t] -0.0122519036242291vieuws[t] +  9.97551507647241e-05size[t] +  0.325785484834069test[t] +  0.365758721516533shared[t] +  0.000622429043409637blogged[t] +  0.109185135487459intrisieke[t] -0.204284883129327extrisieke[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]revieuws[t] =  +  21.817846268231 +  4.46194648588496e-05tijd[t] -0.00523554557071073login[t] -0.0122519036242291vieuws[t] +  9.97551507647241e-05size[t] +  0.325785484834069test[t] +  0.365758721516533shared[t] +  0.000622429043409637blogged[t] +  0.109185135487459intrisieke[t] -0.204284883129327extrisieke[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&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
revieuws[t] = + 21.817846268231 + 4.46194648588496e-05tijd[t] -0.00523554557071073login[t] -0.0122519036242291vieuws[t] + 9.97551507647241e-05size[t] + 0.325785484834069test[t] + 0.365758721516533shared[t] + 0.000622429043409637blogged[t] + 0.109185135487459intrisieke[t] -0.204284883129327extrisieke[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)	21.817846268231	7.187318	3.0356	0.003663	0.001832
tijd	4.46194648588496e-05	1.6e-05	2.7195	0.008733	0.004366
login	-0.00523554557071073	0.027041	-0.1936	0.847193	0.423596
vieuws	-0.0122519036242291	0.010041	-1.2201	0.227619	0.11381
size	9.97551507647241e-05	3e-05	3.3014	0.001694	0.000847
test	0.325785484834069	0.347252	0.9382	0.352253	0.176126
shared	0.365758721516533	0.354295	1.0324	0.306423	0.153212
blogged	0.000622429043409637	0.0215	0.029	0.977009	0.488504
intrisieke	0.109185135487459	0.078648	1.3883	0.17065	0.085325
extrisieke	-0.204284883129327	0.111682	-1.8292	0.072798	0.036399

\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) & 21.817846268231 & 7.187318 & 3.0356 & 0.003663 & 0.001832 \tabularnewline
tijd & 4.46194648588496e-05 & 1.6e-05 & 2.7195 & 0.008733 & 0.004366 \tabularnewline
login & -0.00523554557071073 & 0.027041 & -0.1936 & 0.847193 & 0.423596 \tabularnewline
vieuws & -0.0122519036242291 & 0.010041 & -1.2201 & 0.227619 & 0.11381 \tabularnewline
size & 9.97551507647241e-05 & 3e-05 & 3.3014 & 0.001694 & 0.000847 \tabularnewline
test & 0.325785484834069 & 0.347252 & 0.9382 & 0.352253 & 0.176126 \tabularnewline
shared & 0.365758721516533 & 0.354295 & 1.0324 & 0.306423 & 0.153212 \tabularnewline
blogged & 0.000622429043409637 & 0.0215 & 0.029 & 0.977009 & 0.488504 \tabularnewline
intrisieke & 0.109185135487459 & 0.078648 & 1.3883 & 0.17065 & 0.085325 \tabularnewline
extrisieke & -0.204284883129327 & 0.111682 & -1.8292 & 0.072798 & 0.036399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&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]21.817846268231[/C][C]7.187318[/C][C]3.0356[/C][C]0.003663[/C][C]0.001832[/C][/ROW]
[ROW][C]tijd[/C][C]4.46194648588496e-05[/C][C]1.6e-05[/C][C]2.7195[/C][C]0.008733[/C][C]0.004366[/C][/ROW]
[ROW][C]login[/C][C]-0.00523554557071073[/C][C]0.027041[/C][C]-0.1936[/C][C]0.847193[/C][C]0.423596[/C][/ROW]
[ROW][C]vieuws[/C][C]-0.0122519036242291[/C][C]0.010041[/C][C]-1.2201[/C][C]0.227619[/C][C]0.11381[/C][/ROW]
[ROW][C]size[/C][C]9.97551507647241e-05[/C][C]3e-05[/C][C]3.3014[/C][C]0.001694[/C][C]0.000847[/C][/ROW]
[ROW][C]test[/C][C]0.325785484834069[/C][C]0.347252[/C][C]0.9382[/C][C]0.352253[/C][C]0.176126[/C][/ROW]
[ROW][C]shared[/C][C]0.365758721516533[/C][C]0.354295[/C][C]1.0324[/C][C]0.306423[/C][C]0.153212[/C][/ROW]
[ROW][C]blogged[/C][C]0.000622429043409637[/C][C]0.0215[/C][C]0.029[/C][C]0.977009[/C][C]0.488504[/C][/ROW]
[ROW][C]intrisieke[/C][C]0.109185135487459[/C][C]0.078648[/C][C]1.3883[/C][C]0.17065[/C][C]0.085325[/C][/ROW]
[ROW][C]extrisieke[/C][C]-0.204284883129327[/C][C]0.111682[/C][C]-1.8292[/C][C]0.072798[/C][C]0.036399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&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)	21.817846268231	7.187318	3.0356	0.003663	0.001832
tijd	4.46194648588496e-05	1.6e-05	2.7195	0.008733	0.004366
login	-0.00523554557071073	0.027041	-0.1936	0.847193	0.423596
vieuws	-0.0122519036242291	0.010041	-1.2201	0.227619	0.11381
size	9.97551507647241e-05	3e-05	3.3014	0.001694	0.000847
test	0.325785484834069	0.347252	0.9382	0.352253	0.176126
shared	0.365758721516533	0.354295	1.0324	0.306423	0.153212
blogged	0.000622429043409637	0.0215	0.029	0.977009	0.488504
intrisieke	0.109185135487459	0.078648	1.3883	0.17065	0.085325
extrisieke	-0.204284883129327	0.111682	-1.8292	0.072798	0.036399

Multiple Linear Regression - Regression Statistics
Multiple R	0.753366934981828
R-squared	0.567561738723914
Adjusted R-squared	0.496799114151464
F-TEST (value)	8.02064284858196
F-TEST (DF numerator)	9
F-TEST (DF denominator)	55
p-value	1.81569560497863e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.0572967738873
Sum Squared Residuals	2017.996431382

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.753366934981828 \tabularnewline
R-squared & 0.567561738723914 \tabularnewline
Adjusted R-squared & 0.496799114151464 \tabularnewline
F-TEST (value) & 8.02064284858196 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 1.81569560497863e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.0572967738873 \tabularnewline
Sum Squared Residuals & 2017.996431382 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.753366934981828[/C][/ROW]
[ROW][C]R-squared[/C][C]0.567561738723914[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.496799114151464[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.02064284858196[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]1.81569560497863e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.0572967738873[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2017.996431382[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&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	0.753366934981828
R-squared	0.567561738723914
Adjusted R-squared	0.496799114151464
F-TEST (value)	8.02064284858196
F-TEST (DF numerator)	9
F-TEST (DF denominator)	55
p-value	1.81569560497863e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.0572967738873
Sum Squared Residuals	2017.996431382

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	17	18.5513904913843	-1.55139049138432
2	20	25.4560134329509	-5.45601343295092
3	17	20.5219095645166	-3.52190956451662
4	17	21.1924769059594	-4.19247690595942
5	17	16.8754420231744	0.124557976825582
6	13	23.524547462817	-10.524547462817
7	17	22.9519275394975	-5.95192753949747
8	20	19.6913780389208	0.308621961079196
9	17	16.7340227500321	0.265977249967925
10	22	21.8391815174736	0.160818482526377
11	12	19.7072982347805	-7.70729823478054
12	21	37.9092835979613	-16.9092835979613
13	20	20.4361749829553	-0.436174982955345
14	22	24.1372656301977	-2.13726563019772
15	18	19.4192943368785	-1.41929433687853
16	12	18.9872385314315	-6.98723853143146
17	17	16.1068435565757	0.893156443424257
18	17	20.0638746555694	-3.06387465556937
19	38	31.6169535944403	6.38304640555974
20	30	28.7100979548566	1.28990204514336
21	30	36.7117651249721	-6.71176512497209
22	31	29.469744593833	1.53025540616698
23	33	37.4852195123574	-4.48521951235742
24	34	32.1327628950899	1.86723710491007
25	28	25.3976767170329	2.60232328296714
26	33	26.7088413173734	6.2911586826266
27	42	36.265073843377	5.73492615662301
28	36	24.159783000198	11.840216999802
29	43	31.7776897256349	11.2223102743651
30	39	37.4618517917936	1.53814820820641
31	30	28.6525534860539	1.3474465139461
32	30	34.4380068794456	-4.43800687944563
33	31	36.8921009745419	-5.89210097454187
34	44	38.100108221747	5.899891778253
35	34	30.6540197589772	3.34598024102283
36	33	29.435399310733	3.56460068926704
37	30	32.6482086938812	-2.64820869388124
38	32	26.5800641818046	5.41993581819536
39	24	24.5458408385383	-0.545840838538274
40	26	34.2567082397691	-8.25670823976905
41	47	38.1882368535887	8.81176314641125
42	30	35.1643400097624	-5.16434000976239
43	34	33.5210407591476	0.478959240852448
44	33	32.4874269895625	0.512573010437508
45	14	22.3031223398477	-8.30312233984774
46	32	33.1815383019958	-1.18153830199582
47	35	24.729591324037	10.270408675963
48	28	29.452896290025	-1.452896290025
49	39	28.0788521199233	10.9211478800767
50	29	33.1854752822618	-4.18547528226182
51	29	31.5378514011992	-2.53785140119924
52	27	28.7460020188605	-1.74600201886053
53	35	30.0584368950049	4.94156310499511
54	29	27.303764728738	1.69623527126197
55	37	35.7323776833242	1.26762231667584
56	29	34.8837744522046	-5.88377445220462
57	31	33.7054737886169	-2.70547378861695
58	33	32.1432078317168	0.856792168283178
59	38	31.5676963582914	6.4323036417086
60	31	30.5415660364686	0.458433963531373
61	37	42.3023450133327	-5.30234501333267
62	31	27.2818786207821	3.71812137921792
63	39	34.1414278149919	4.85857218500809
64	37	31.4143485887506	5.58565141124938
65	32	23.1412945880381	8.85870541196186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17 & 18.5513904913843 & -1.55139049138432 \tabularnewline
2 & 20 & 25.4560134329509 & -5.45601343295092 \tabularnewline
3 & 17 & 20.5219095645166 & -3.52190956451662 \tabularnewline
4 & 17 & 21.1924769059594 & -4.19247690595942 \tabularnewline
5 & 17 & 16.8754420231744 & 0.124557976825582 \tabularnewline
6 & 13 & 23.524547462817 & -10.524547462817 \tabularnewline
7 & 17 & 22.9519275394975 & -5.95192753949747 \tabularnewline
8 & 20 & 19.6913780389208 & 0.308621961079196 \tabularnewline
9 & 17 & 16.7340227500321 & 0.265977249967925 \tabularnewline
10 & 22 & 21.8391815174736 & 0.160818482526377 \tabularnewline
11 & 12 & 19.7072982347805 & -7.70729823478054 \tabularnewline
12 & 21 & 37.9092835979613 & -16.9092835979613 \tabularnewline
13 & 20 & 20.4361749829553 & -0.436174982955345 \tabularnewline
14 & 22 & 24.1372656301977 & -2.13726563019772 \tabularnewline
15 & 18 & 19.4192943368785 & -1.41929433687853 \tabularnewline
16 & 12 & 18.9872385314315 & -6.98723853143146 \tabularnewline
17 & 17 & 16.1068435565757 & 0.893156443424257 \tabularnewline
18 & 17 & 20.0638746555694 & -3.06387465556937 \tabularnewline
19 & 38 & 31.6169535944403 & 6.38304640555974 \tabularnewline
20 & 30 & 28.7100979548566 & 1.28990204514336 \tabularnewline
21 & 30 & 36.7117651249721 & -6.71176512497209 \tabularnewline
22 & 31 & 29.469744593833 & 1.53025540616698 \tabularnewline
23 & 33 & 37.4852195123574 & -4.48521951235742 \tabularnewline
24 & 34 & 32.1327628950899 & 1.86723710491007 \tabularnewline
25 & 28 & 25.3976767170329 & 2.60232328296714 \tabularnewline
26 & 33 & 26.7088413173734 & 6.2911586826266 \tabularnewline
27 & 42 & 36.265073843377 & 5.73492615662301 \tabularnewline
28 & 36 & 24.159783000198 & 11.840216999802 \tabularnewline
29 & 43 & 31.7776897256349 & 11.2223102743651 \tabularnewline
30 & 39 & 37.4618517917936 & 1.53814820820641 \tabularnewline
31 & 30 & 28.6525534860539 & 1.3474465139461 \tabularnewline
32 & 30 & 34.4380068794456 & -4.43800687944563 \tabularnewline
33 & 31 & 36.8921009745419 & -5.89210097454187 \tabularnewline
34 & 44 & 38.100108221747 & 5.899891778253 \tabularnewline
35 & 34 & 30.6540197589772 & 3.34598024102283 \tabularnewline
36 & 33 & 29.435399310733 & 3.56460068926704 \tabularnewline
37 & 30 & 32.6482086938812 & -2.64820869388124 \tabularnewline
38 & 32 & 26.5800641818046 & 5.41993581819536 \tabularnewline
39 & 24 & 24.5458408385383 & -0.545840838538274 \tabularnewline
40 & 26 & 34.2567082397691 & -8.25670823976905 \tabularnewline
41 & 47 & 38.1882368535887 & 8.81176314641125 \tabularnewline
42 & 30 & 35.1643400097624 & -5.16434000976239 \tabularnewline
43 & 34 & 33.5210407591476 & 0.478959240852448 \tabularnewline
44 & 33 & 32.4874269895625 & 0.512573010437508 \tabularnewline
45 & 14 & 22.3031223398477 & -8.30312233984774 \tabularnewline
46 & 32 & 33.1815383019958 & -1.18153830199582 \tabularnewline
47 & 35 & 24.729591324037 & 10.270408675963 \tabularnewline
48 & 28 & 29.452896290025 & -1.452896290025 \tabularnewline
49 & 39 & 28.0788521199233 & 10.9211478800767 \tabularnewline
50 & 29 & 33.1854752822618 & -4.18547528226182 \tabularnewline
51 & 29 & 31.5378514011992 & -2.53785140119924 \tabularnewline
52 & 27 & 28.7460020188605 & -1.74600201886053 \tabularnewline
53 & 35 & 30.0584368950049 & 4.94156310499511 \tabularnewline
54 & 29 & 27.303764728738 & 1.69623527126197 \tabularnewline
55 & 37 & 35.7323776833242 & 1.26762231667584 \tabularnewline
56 & 29 & 34.8837744522046 & -5.88377445220462 \tabularnewline
57 & 31 & 33.7054737886169 & -2.70547378861695 \tabularnewline
58 & 33 & 32.1432078317168 & 0.856792168283178 \tabularnewline
59 & 38 & 31.5676963582914 & 6.4323036417086 \tabularnewline
60 & 31 & 30.5415660364686 & 0.458433963531373 \tabularnewline
61 & 37 & 42.3023450133327 & -5.30234501333267 \tabularnewline
62 & 31 & 27.2818786207821 & 3.71812137921792 \tabularnewline
63 & 39 & 34.1414278149919 & 4.85857218500809 \tabularnewline
64 & 37 & 31.4143485887506 & 5.58565141124938 \tabularnewline
65 & 32 & 23.1412945880381 & 8.85870541196186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&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]17[/C][C]18.5513904913843[/C][C]-1.55139049138432[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]25.4560134329509[/C][C]-5.45601343295092[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]20.5219095645166[/C][C]-3.52190956451662[/C][/ROW]
[ROW][C]4[/C][C]17[/C][C]21.1924769059594[/C][C]-4.19247690595942[/C][/ROW]
[ROW][C]5[/C][C]17[/C][C]16.8754420231744[/C][C]0.124557976825582[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]23.524547462817[/C][C]-10.524547462817[/C][/ROW]
[ROW][C]7[/C][C]17[/C][C]22.9519275394975[/C][C]-5.95192753949747[/C][/ROW]
[ROW][C]8[/C][C]20[/C][C]19.6913780389208[/C][C]0.308621961079196[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]16.7340227500321[/C][C]0.265977249967925[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]21.8391815174736[/C][C]0.160818482526377[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]19.7072982347805[/C][C]-7.70729823478054[/C][/ROW]
[ROW][C]12[/C][C]21[/C][C]37.9092835979613[/C][C]-16.9092835979613[/C][/ROW]
[ROW][C]13[/C][C]20[/C][C]20.4361749829553[/C][C]-0.436174982955345[/C][/ROW]
[ROW][C]14[/C][C]22[/C][C]24.1372656301977[/C][C]-2.13726563019772[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]19.4192943368785[/C][C]-1.41929433687853[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]18.9872385314315[/C][C]-6.98723853143146[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]16.1068435565757[/C][C]0.893156443424257[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]20.0638746555694[/C][C]-3.06387465556937[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]31.6169535944403[/C][C]6.38304640555974[/C][/ROW]
[ROW][C]20[/C][C]30[/C][C]28.7100979548566[/C][C]1.28990204514336[/C][/ROW]
[ROW][C]21[/C][C]30[/C][C]36.7117651249721[/C][C]-6.71176512497209[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]29.469744593833[/C][C]1.53025540616698[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]37.4852195123574[/C][C]-4.48521951235742[/C][/ROW]
[ROW][C]24[/C][C]34[/C][C]32.1327628950899[/C][C]1.86723710491007[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]25.3976767170329[/C][C]2.60232328296714[/C][/ROW]
[ROW][C]26[/C][C]33[/C][C]26.7088413173734[/C][C]6.2911586826266[/C][/ROW]
[ROW][C]27[/C][C]42[/C][C]36.265073843377[/C][C]5.73492615662301[/C][/ROW]
[ROW][C]28[/C][C]36[/C][C]24.159783000198[/C][C]11.840216999802[/C][/ROW]
[ROW][C]29[/C][C]43[/C][C]31.7776897256349[/C][C]11.2223102743651[/C][/ROW]
[ROW][C]30[/C][C]39[/C][C]37.4618517917936[/C][C]1.53814820820641[/C][/ROW]
[ROW][C]31[/C][C]30[/C][C]28.6525534860539[/C][C]1.3474465139461[/C][/ROW]
[ROW][C]32[/C][C]30[/C][C]34.4380068794456[/C][C]-4.43800687944563[/C][/ROW]
[ROW][C]33[/C][C]31[/C][C]36.8921009745419[/C][C]-5.89210097454187[/C][/ROW]
[ROW][C]34[/C][C]44[/C][C]38.100108221747[/C][C]5.899891778253[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]30.6540197589772[/C][C]3.34598024102283[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]29.435399310733[/C][C]3.56460068926704[/C][/ROW]
[ROW][C]37[/C][C]30[/C][C]32.6482086938812[/C][C]-2.64820869388124[/C][/ROW]
[ROW][C]38[/C][C]32[/C][C]26.5800641818046[/C][C]5.41993581819536[/C][/ROW]
[ROW][C]39[/C][C]24[/C][C]24.5458408385383[/C][C]-0.545840838538274[/C][/ROW]
[ROW][C]40[/C][C]26[/C][C]34.2567082397691[/C][C]-8.25670823976905[/C][/ROW]
[ROW][C]41[/C][C]47[/C][C]38.1882368535887[/C][C]8.81176314641125[/C][/ROW]
[ROW][C]42[/C][C]30[/C][C]35.1643400097624[/C][C]-5.16434000976239[/C][/ROW]
[ROW][C]43[/C][C]34[/C][C]33.5210407591476[/C][C]0.478959240852448[/C][/ROW]
[ROW][C]44[/C][C]33[/C][C]32.4874269895625[/C][C]0.512573010437508[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]22.3031223398477[/C][C]-8.30312233984774[/C][/ROW]
[ROW][C]46[/C][C]32[/C][C]33.1815383019958[/C][C]-1.18153830199582[/C][/ROW]
[ROW][C]47[/C][C]35[/C][C]24.729591324037[/C][C]10.270408675963[/C][/ROW]
[ROW][C]48[/C][C]28[/C][C]29.452896290025[/C][C]-1.452896290025[/C][/ROW]
[ROW][C]49[/C][C]39[/C][C]28.0788521199233[/C][C]10.9211478800767[/C][/ROW]
[ROW][C]50[/C][C]29[/C][C]33.1854752822618[/C][C]-4.18547528226182[/C][/ROW]
[ROW][C]51[/C][C]29[/C][C]31.5378514011992[/C][C]-2.53785140119924[/C][/ROW]
[ROW][C]52[/C][C]27[/C][C]28.7460020188605[/C][C]-1.74600201886053[/C][/ROW]
[ROW][C]53[/C][C]35[/C][C]30.0584368950049[/C][C]4.94156310499511[/C][/ROW]
[ROW][C]54[/C][C]29[/C][C]27.303764728738[/C][C]1.69623527126197[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]35.7323776833242[/C][C]1.26762231667584[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]34.8837744522046[/C][C]-5.88377445220462[/C][/ROW]
[ROW][C]57[/C][C]31[/C][C]33.7054737886169[/C][C]-2.70547378861695[/C][/ROW]
[ROW][C]58[/C][C]33[/C][C]32.1432078317168[/C][C]0.856792168283178[/C][/ROW]
[ROW][C]59[/C][C]38[/C][C]31.5676963582914[/C][C]6.4323036417086[/C][/ROW]
[ROW][C]60[/C][C]31[/C][C]30.5415660364686[/C][C]0.458433963531373[/C][/ROW]
[ROW][C]61[/C][C]37[/C][C]42.3023450133327[/C][C]-5.30234501333267[/C][/ROW]
[ROW][C]62[/C][C]31[/C][C]27.2818786207821[/C][C]3.71812137921792[/C][/ROW]
[ROW][C]63[/C][C]39[/C][C]34.1414278149919[/C][C]4.85857218500809[/C][/ROW]
[ROW][C]64[/C][C]37[/C][C]31.4143485887506[/C][C]5.58565141124938[/C][/ROW]
[ROW][C]65[/C][C]32[/C][C]23.1412945880381[/C][C]8.85870541196186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&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	17	18.5513904913843	-1.55139049138432
2	20	25.4560134329509	-5.45601343295092
3	17	20.5219095645166	-3.52190956451662
4	17	21.1924769059594	-4.19247690595942
5	17	16.8754420231744	0.124557976825582
6	13	23.524547462817	-10.524547462817
7	17	22.9519275394975	-5.95192753949747
8	20	19.6913780389208	0.308621961079196
9	17	16.7340227500321	0.265977249967925
10	22	21.8391815174736	0.160818482526377
11	12	19.7072982347805	-7.70729823478054
12	21	37.9092835979613	-16.9092835979613
13	20	20.4361749829553	-0.436174982955345
14	22	24.1372656301977	-2.13726563019772
15	18	19.4192943368785	-1.41929433687853
16	12	18.9872385314315	-6.98723853143146
17	17	16.1068435565757	0.893156443424257
18	17	20.0638746555694	-3.06387465556937
19	38	31.6169535944403	6.38304640555974
20	30	28.7100979548566	1.28990204514336
21	30	36.7117651249721	-6.71176512497209
22	31	29.469744593833	1.53025540616698
23	33	37.4852195123574	-4.48521951235742
24	34	32.1327628950899	1.86723710491007
25	28	25.3976767170329	2.60232328296714
26	33	26.7088413173734	6.2911586826266
27	42	36.265073843377	5.73492615662301
28	36	24.159783000198	11.840216999802
29	43	31.7776897256349	11.2223102743651
30	39	37.4618517917936	1.53814820820641
31	30	28.6525534860539	1.3474465139461
32	30	34.4380068794456	-4.43800687944563
33	31	36.8921009745419	-5.89210097454187
34	44	38.100108221747	5.899891778253
35	34	30.6540197589772	3.34598024102283
36	33	29.435399310733	3.56460068926704
37	30	32.6482086938812	-2.64820869388124
38	32	26.5800641818046	5.41993581819536
39	24	24.5458408385383	-0.545840838538274
40	26	34.2567082397691	-8.25670823976905
41	47	38.1882368535887	8.81176314641125
42	30	35.1643400097624	-5.16434000976239
43	34	33.5210407591476	0.478959240852448
44	33	32.4874269895625	0.512573010437508
45	14	22.3031223398477	-8.30312233984774
46	32	33.1815383019958	-1.18153830199582
47	35	24.729591324037	10.270408675963
48	28	29.452896290025	-1.452896290025
49	39	28.0788521199233	10.9211478800767
50	29	33.1854752822618	-4.18547528226182
51	29	31.5378514011992	-2.53785140119924
52	27	28.7460020188605	-1.74600201886053
53	35	30.0584368950049	4.94156310499511
54	29	27.303764728738	1.69623527126197
55	37	35.7323776833242	1.26762231667584
56	29	34.8837744522046	-5.88377445220462
57	31	33.7054737886169	-2.70547378861695
58	33	32.1432078317168	0.856792168283178
59	38	31.5676963582914	6.4323036417086
60	31	30.5415660364686	0.458433963531373
61	37	42.3023450133327	-5.30234501333267
62	31	27.2818786207821	3.71812137921792
63	39	34.1414278149919	4.85857218500809
64	37	31.4143485887506	5.58565141124938
65	32	23.1412945880381	8.85870541196186

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.0480787952850852	0.0961575905701705	0.951921204714915
14	0.0302904645221878	0.0605809290443756	0.969709535477812
15	0.0314958081925179	0.0629916163850358	0.968504191807482
16	0.0827966416479564	0.165593283295913	0.917203358352044
17	0.0534813553054075	0.106962710610815	0.946518644694593
18	0.0361028591626144	0.0722057183252287	0.963897140837386
19	0.205285353831117	0.410570707662233	0.794714646168883
20	0.339399179961512	0.678798359923023	0.660600820038488
21	0.523349289357503	0.953301421284993	0.476650710642497
22	0.566134053904651	0.867731892190698	0.433865946095349
23	0.501515514898852	0.996968970202297	0.498484485101148
24	0.462998116501636	0.925996233003272	0.537001883498364
25	0.386519774731401	0.773039549462802	0.613480225268599
26	0.519476489328408	0.961047021343184	0.480523510671592
27	0.591418713189644	0.817162573620712	0.408581286810356
28	0.734006144888508	0.531987710222984	0.265993855111492
29	0.843456407613651	0.313087184772699	0.156543592386349
30	0.840519197727475	0.31896160454505	0.159480802272525
31	0.786885987899373	0.426228024201255	0.213114012100627
32	0.791632930139431	0.416734139721139	0.208367069860569
33	0.749566932324353	0.500866135351293	0.250433067675647
34	0.786309702543422	0.427380594913156	0.213690297456578
35	0.725370768077357	0.549258463845286	0.274629231922643
36	0.669831245663431	0.660337508673138	0.330168754336569
37	0.598552233430487	0.802895533139026	0.401447766569513
38	0.586948264133979	0.826103471732042	0.413051735866021
39	0.523806812605852	0.952386374788296	0.476193187394148
40	0.628214593085504	0.743570813828992	0.371785406914496
41	0.825132991942259	0.349734016115481	0.174867008057741
42	0.770304006712751	0.459391986574498	0.229695993287249
43	0.727947399276213	0.544105201447575	0.272052600723787
44	0.641736865542706	0.716526268914588	0.358263134457294
45	0.905401794736176	0.189196410527648	0.0945982052638241
46	0.945475079348949	0.109049841302102	0.0545249206510511
47	0.929381983231332	0.141236033537337	0.0706180167686684
48	0.875367582500641	0.249264834998718	0.124632417499359
49	0.928792385625066	0.142415228749869	0.0712076143749343
50	0.907496069267382	0.185007861465235	0.0925039307326177
51	0.82126307924661	0.357473841506779	0.17873692075339
52	0.765818835268997	0.468362329462007	0.234181164731003

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.0480787952850852 & 0.0961575905701705 & 0.951921204714915 \tabularnewline
14 & 0.0302904645221878 & 0.0605809290443756 & 0.969709535477812 \tabularnewline
15 & 0.0314958081925179 & 0.0629916163850358 & 0.968504191807482 \tabularnewline
16 & 0.0827966416479564 & 0.165593283295913 & 0.917203358352044 \tabularnewline
17 & 0.0534813553054075 & 0.106962710610815 & 0.946518644694593 \tabularnewline
18 & 0.0361028591626144 & 0.0722057183252287 & 0.963897140837386 \tabularnewline
19 & 0.205285353831117 & 0.410570707662233 & 0.794714646168883 \tabularnewline
20 & 0.339399179961512 & 0.678798359923023 & 0.660600820038488 \tabularnewline
21 & 0.523349289357503 & 0.953301421284993 & 0.476650710642497 \tabularnewline
22 & 0.566134053904651 & 0.867731892190698 & 0.433865946095349 \tabularnewline
23 & 0.501515514898852 & 0.996968970202297 & 0.498484485101148 \tabularnewline
24 & 0.462998116501636 & 0.925996233003272 & 0.537001883498364 \tabularnewline
25 & 0.386519774731401 & 0.773039549462802 & 0.613480225268599 \tabularnewline
26 & 0.519476489328408 & 0.961047021343184 & 0.480523510671592 \tabularnewline
27 & 0.591418713189644 & 0.817162573620712 & 0.408581286810356 \tabularnewline
28 & 0.734006144888508 & 0.531987710222984 & 0.265993855111492 \tabularnewline
29 & 0.843456407613651 & 0.313087184772699 & 0.156543592386349 \tabularnewline
30 & 0.840519197727475 & 0.31896160454505 & 0.159480802272525 \tabularnewline
31 & 0.786885987899373 & 0.426228024201255 & 0.213114012100627 \tabularnewline
32 & 0.791632930139431 & 0.416734139721139 & 0.208367069860569 \tabularnewline
33 & 0.749566932324353 & 0.500866135351293 & 0.250433067675647 \tabularnewline
34 & 0.786309702543422 & 0.427380594913156 & 0.213690297456578 \tabularnewline
35 & 0.725370768077357 & 0.549258463845286 & 0.274629231922643 \tabularnewline
36 & 0.669831245663431 & 0.660337508673138 & 0.330168754336569 \tabularnewline
37 & 0.598552233430487 & 0.802895533139026 & 0.401447766569513 \tabularnewline
38 & 0.586948264133979 & 0.826103471732042 & 0.413051735866021 \tabularnewline
39 & 0.523806812605852 & 0.952386374788296 & 0.476193187394148 \tabularnewline
40 & 0.628214593085504 & 0.743570813828992 & 0.371785406914496 \tabularnewline
41 & 0.825132991942259 & 0.349734016115481 & 0.174867008057741 \tabularnewline
42 & 0.770304006712751 & 0.459391986574498 & 0.229695993287249 \tabularnewline
43 & 0.727947399276213 & 0.544105201447575 & 0.272052600723787 \tabularnewline
44 & 0.641736865542706 & 0.716526268914588 & 0.358263134457294 \tabularnewline
45 & 0.905401794736176 & 0.189196410527648 & 0.0945982052638241 \tabularnewline
46 & 0.945475079348949 & 0.109049841302102 & 0.0545249206510511 \tabularnewline
47 & 0.929381983231332 & 0.141236033537337 & 0.0706180167686684 \tabularnewline
48 & 0.875367582500641 & 0.249264834998718 & 0.124632417499359 \tabularnewline
49 & 0.928792385625066 & 0.142415228749869 & 0.0712076143749343 \tabularnewline
50 & 0.907496069267382 & 0.185007861465235 & 0.0925039307326177 \tabularnewline
51 & 0.82126307924661 & 0.357473841506779 & 0.17873692075339 \tabularnewline
52 & 0.765818835268997 & 0.468362329462007 & 0.234181164731003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&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]13[/C][C]0.0480787952850852[/C][C]0.0961575905701705[/C][C]0.951921204714915[/C][/ROW]
[ROW][C]14[/C][C]0.0302904645221878[/C][C]0.0605809290443756[/C][C]0.969709535477812[/C][/ROW]
[ROW][C]15[/C][C]0.0314958081925179[/C][C]0.0629916163850358[/C][C]0.968504191807482[/C][/ROW]
[ROW][C]16[/C][C]0.0827966416479564[/C][C]0.165593283295913[/C][C]0.917203358352044[/C][/ROW]
[ROW][C]17[/C][C]0.0534813553054075[/C][C]0.106962710610815[/C][C]0.946518644694593[/C][/ROW]
[ROW][C]18[/C][C]0.0361028591626144[/C][C]0.0722057183252287[/C][C]0.963897140837386[/C][/ROW]
[ROW][C]19[/C][C]0.205285353831117[/C][C]0.410570707662233[/C][C]0.794714646168883[/C][/ROW]
[ROW][C]20[/C][C]0.339399179961512[/C][C]0.678798359923023[/C][C]0.660600820038488[/C][/ROW]
[ROW][C]21[/C][C]0.523349289357503[/C][C]0.953301421284993[/C][C]0.476650710642497[/C][/ROW]
[ROW][C]22[/C][C]0.566134053904651[/C][C]0.867731892190698[/C][C]0.433865946095349[/C][/ROW]
[ROW][C]23[/C][C]0.501515514898852[/C][C]0.996968970202297[/C][C]0.498484485101148[/C][/ROW]
[ROW][C]24[/C][C]0.462998116501636[/C][C]0.925996233003272[/C][C]0.537001883498364[/C][/ROW]
[ROW][C]25[/C][C]0.386519774731401[/C][C]0.773039549462802[/C][C]0.613480225268599[/C][/ROW]
[ROW][C]26[/C][C]0.519476489328408[/C][C]0.961047021343184[/C][C]0.480523510671592[/C][/ROW]
[ROW][C]27[/C][C]0.591418713189644[/C][C]0.817162573620712[/C][C]0.408581286810356[/C][/ROW]
[ROW][C]28[/C][C]0.734006144888508[/C][C]0.531987710222984[/C][C]0.265993855111492[/C][/ROW]
[ROW][C]29[/C][C]0.843456407613651[/C][C]0.313087184772699[/C][C]0.156543592386349[/C][/ROW]
[ROW][C]30[/C][C]0.840519197727475[/C][C]0.31896160454505[/C][C]0.159480802272525[/C][/ROW]
[ROW][C]31[/C][C]0.786885987899373[/C][C]0.426228024201255[/C][C]0.213114012100627[/C][/ROW]
[ROW][C]32[/C][C]0.791632930139431[/C][C]0.416734139721139[/C][C]0.208367069860569[/C][/ROW]
[ROW][C]33[/C][C]0.749566932324353[/C][C]0.500866135351293[/C][C]0.250433067675647[/C][/ROW]
[ROW][C]34[/C][C]0.786309702543422[/C][C]0.427380594913156[/C][C]0.213690297456578[/C][/ROW]
[ROW][C]35[/C][C]0.725370768077357[/C][C]0.549258463845286[/C][C]0.274629231922643[/C][/ROW]
[ROW][C]36[/C][C]0.669831245663431[/C][C]0.660337508673138[/C][C]0.330168754336569[/C][/ROW]
[ROW][C]37[/C][C]0.598552233430487[/C][C]0.802895533139026[/C][C]0.401447766569513[/C][/ROW]
[ROW][C]38[/C][C]0.586948264133979[/C][C]0.826103471732042[/C][C]0.413051735866021[/C][/ROW]
[ROW][C]39[/C][C]0.523806812605852[/C][C]0.952386374788296[/C][C]0.476193187394148[/C][/ROW]
[ROW][C]40[/C][C]0.628214593085504[/C][C]0.743570813828992[/C][C]0.371785406914496[/C][/ROW]
[ROW][C]41[/C][C]0.825132991942259[/C][C]0.349734016115481[/C][C]0.174867008057741[/C][/ROW]
[ROW][C]42[/C][C]0.770304006712751[/C][C]0.459391986574498[/C][C]0.229695993287249[/C][/ROW]
[ROW][C]43[/C][C]0.727947399276213[/C][C]0.544105201447575[/C][C]0.272052600723787[/C][/ROW]
[ROW][C]44[/C][C]0.641736865542706[/C][C]0.716526268914588[/C][C]0.358263134457294[/C][/ROW]
[ROW][C]45[/C][C]0.905401794736176[/C][C]0.189196410527648[/C][C]0.0945982052638241[/C][/ROW]
[ROW][C]46[/C][C]0.945475079348949[/C][C]0.109049841302102[/C][C]0.0545249206510511[/C][/ROW]
[ROW][C]47[/C][C]0.929381983231332[/C][C]0.141236033537337[/C][C]0.0706180167686684[/C][/ROW]
[ROW][C]48[/C][C]0.875367582500641[/C][C]0.249264834998718[/C][C]0.124632417499359[/C][/ROW]
[ROW][C]49[/C][C]0.928792385625066[/C][C]0.142415228749869[/C][C]0.0712076143749343[/C][/ROW]
[ROW][C]50[/C][C]0.907496069267382[/C][C]0.185007861465235[/C][C]0.0925039307326177[/C][/ROW]
[ROW][C]51[/C][C]0.82126307924661[/C][C]0.357473841506779[/C][C]0.17873692075339[/C][/ROW]
[ROW][C]52[/C][C]0.765818835268997[/C][C]0.468362329462007[/C][C]0.234181164731003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&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
13	0.0480787952850852	0.0961575905701705	0.951921204714915
14	0.0302904645221878	0.0605809290443756	0.969709535477812
15	0.0314958081925179	0.0629916163850358	0.968504191807482
16	0.0827966416479564	0.165593283295913	0.917203358352044
17	0.0534813553054075	0.106962710610815	0.946518644694593
18	0.0361028591626144	0.0722057183252287	0.963897140837386
19	0.205285353831117	0.410570707662233	0.794714646168883
20	0.339399179961512	0.678798359923023	0.660600820038488
21	0.523349289357503	0.953301421284993	0.476650710642497
22	0.566134053904651	0.867731892190698	0.433865946095349
23	0.501515514898852	0.996968970202297	0.498484485101148
24	0.462998116501636	0.925996233003272	0.537001883498364
25	0.386519774731401	0.773039549462802	0.613480225268599
26	0.519476489328408	0.961047021343184	0.480523510671592
27	0.591418713189644	0.817162573620712	0.408581286810356
28	0.734006144888508	0.531987710222984	0.265993855111492
29	0.843456407613651	0.313087184772699	0.156543592386349
30	0.840519197727475	0.31896160454505	0.159480802272525
31	0.786885987899373	0.426228024201255	0.213114012100627
32	0.791632930139431	0.416734139721139	0.208367069860569
33	0.749566932324353	0.500866135351293	0.250433067675647
34	0.786309702543422	0.427380594913156	0.213690297456578
35	0.725370768077357	0.549258463845286	0.274629231922643
36	0.669831245663431	0.660337508673138	0.330168754336569
37	0.598552233430487	0.802895533139026	0.401447766569513
38	0.586948264133979	0.826103471732042	0.413051735866021
39	0.523806812605852	0.952386374788296	0.476193187394148
40	0.628214593085504	0.743570813828992	0.371785406914496
41	0.825132991942259	0.349734016115481	0.174867008057741
42	0.770304006712751	0.459391986574498	0.229695993287249
43	0.727947399276213	0.544105201447575	0.272052600723787
44	0.641736865542706	0.716526268914588	0.358263134457294
45	0.905401794736176	0.189196410527648	0.0945982052638241
46	0.945475079348949	0.109049841302102	0.0545249206510511
47	0.929381983231332	0.141236033537337	0.0706180167686684
48	0.875367582500641	0.249264834998718	0.124632417499359
49	0.928792385625066	0.142415228749869	0.0712076143749343
50	0.907496069267382	0.185007861465235	0.0925039307326177
51	0.82126307924661	0.357473841506779	0.17873692075339
52	0.765818835268997	0.468362329462007	0.234181164731003

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	0.1	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153826&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]0.1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153826&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153826&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	0.1	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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