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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 28 Nov 2011 12:57:31 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/28/t1322503076qg33pmhmkd3o778.htm/, Retrieved Sun, 15 Jun 2025 06:48:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147924, Retrieved Sun, 15 Jun 2025 06:48:29 +0000

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Estimated Impact

218

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

-     [Multiple Regression] [Multiple Regression] [2011-11-25 10:28:05] [bc54fcbdb4f9c071218969745a8ec94b]
- R       [Multiple Regression] [] [2011-11-28 17:57:31] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Births_per_month[t] = + 9330.58695652174 + 107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] + 8.74534161490679M4[t] -879.764492753623M5[t] + 75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] + 367.186335403727M10[t] + 234.50983436853M11[t] + 11.00983436853t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Births_per_month[t] =  +  9330.58695652174 +  107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] +  8.74534161490679M4[t] -879.764492753623M5[t] +  75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] +  367.186335403727M10[t] +  234.50983436853M11[t] +  11.00983436853t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Births_per_month[t] =  +  9330.58695652174 +  107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] +  8.74534161490679M4[t] -879.764492753623M5[t] +  75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] +  367.186335403727M10[t] +  234.50983436853M11[t] +  11.00983436853t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&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
Births_per_month[t] = + 9330.58695652174 + 107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] + 8.74534161490679M4[t] -879.764492753623M5[t] + 75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] + 367.186335403727M10[t] + 234.50983436853M11[t] + 11.00983436853t + 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)	9330.58695652174	136.432397	68.3898	0	0
M1	107.620600414077	162.984839	0.6603	0.5115	0.25575
M2	-635.532091097308	162.916845	-3.901	0.000238	0.000119
M3	-287.827639751553	162.863941	-1.7673	0.082101	0.04105
M4	8.74534161490679	169.407011	0.0516	0.958995	0.479497
M5	-879.764492753623	169.297972	-5.1965	2e-06	1e-06
M6	75.7256728778466	169.203414	0.4475	0.656043	0.328022
M7	-309.617494824017	169.123363	-1.8307	0.071949	0.035975
M8	-141.293995859213	169.057838	-0.8358	0.406492	0.203246
M9	-196.97049689441	169.006856	-1.1655	0.248298	0.124149
M10	367.186335403727	168.970432	2.1731	0.033604	0.016802
M11	234.50983436853	168.948573	1.3881	0.170088	0.085044
t	11.00983436853	1.569122	7.0166	0	0

\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) & 9330.58695652174 & 136.432397 & 68.3898 & 0 & 0 \tabularnewline
M1 & 107.620600414077 & 162.984839 & 0.6603 & 0.5115 & 0.25575 \tabularnewline
M2 & -635.532091097308 & 162.916845 & -3.901 & 0.000238 & 0.000119 \tabularnewline
M3 & -287.827639751553 & 162.863941 & -1.7673 & 0.082101 & 0.04105 \tabularnewline
M4 & 8.74534161490679 & 169.407011 & 0.0516 & 0.958995 & 0.479497 \tabularnewline
M5 & -879.764492753623 & 169.297972 & -5.1965 & 2e-06 & 1e-06 \tabularnewline
M6 & 75.7256728778466 & 169.203414 & 0.4475 & 0.656043 & 0.328022 \tabularnewline
M7 & -309.617494824017 & 169.123363 & -1.8307 & 0.071949 & 0.035975 \tabularnewline
M8 & -141.293995859213 & 169.057838 & -0.8358 & 0.406492 & 0.203246 \tabularnewline
M9 & -196.97049689441 & 169.006856 & -1.1655 & 0.248298 & 0.124149 \tabularnewline
M10 & 367.186335403727 & 168.970432 & 2.1731 & 0.033604 & 0.016802 \tabularnewline
M11 & 234.50983436853 & 168.948573 & 1.3881 & 0.170088 & 0.085044 \tabularnewline
t & 11.00983436853 & 1.569122 & 7.0166 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&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]9330.58695652174[/C][C]136.432397[/C][C]68.3898[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]107.620600414077[/C][C]162.984839[/C][C]0.6603[/C][C]0.5115[/C][C]0.25575[/C][/ROW]
[ROW][C]M2[/C][C]-635.532091097308[/C][C]162.916845[/C][C]-3.901[/C][C]0.000238[/C][C]0.000119[/C][/ROW]
[ROW][C]M3[/C][C]-287.827639751553[/C][C]162.863941[/C][C]-1.7673[/C][C]0.082101[/C][C]0.04105[/C][/ROW]
[ROW][C]M4[/C][C]8.74534161490679[/C][C]169.407011[/C][C]0.0516[/C][C]0.958995[/C][C]0.479497[/C][/ROW]
[ROW][C]M5[/C][C]-879.764492753623[/C][C]169.297972[/C][C]-5.1965[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M6[/C][C]75.7256728778466[/C][C]169.203414[/C][C]0.4475[/C][C]0.656043[/C][C]0.328022[/C][/ROW]
[ROW][C]M7[/C][C]-309.617494824017[/C][C]169.123363[/C][C]-1.8307[/C][C]0.071949[/C][C]0.035975[/C][/ROW]
[ROW][C]M8[/C][C]-141.293995859213[/C][C]169.057838[/C][C]-0.8358[/C][C]0.406492[/C][C]0.203246[/C][/ROW]
[ROW][C]M9[/C][C]-196.97049689441[/C][C]169.006856[/C][C]-1.1655[/C][C]0.248298[/C][C]0.124149[/C][/ROW]
[ROW][C]M10[/C][C]367.186335403727[/C][C]168.970432[/C][C]2.1731[/C][C]0.033604[/C][C]0.016802[/C][/ROW]
[ROW][C]M11[/C][C]234.50983436853[/C][C]168.948573[/C][C]1.3881[/C][C]0.170088[/C][C]0.085044[/C][/ROW]
[ROW][C]t[/C][C]11.00983436853[/C][C]1.569122[/C][C]7.0166[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&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)	9330.58695652174	136.432397	68.3898	0	0
M1	107.620600414077	162.984839	0.6603	0.5115	0.25575
M2	-635.532091097308	162.916845	-3.901	0.000238	0.000119
M3	-287.827639751553	162.863941	-1.7673	0.082101	0.04105
M4	8.74534161490679	169.407011	0.0516	0.958995	0.479497
M5	-879.764492753623	169.297972	-5.1965	2e-06	1e-06
M6	75.7256728778466	169.203414	0.4475	0.656043	0.328022
M7	-309.617494824017	169.123363	-1.8307	0.071949	0.035975
M8	-141.293995859213	169.057838	-0.8358	0.406492	0.203246
M9	-196.97049689441	169.006856	-1.1655	0.248298	0.124149
M10	367.186335403727	168.970432	2.1731	0.033604	0.016802
M11	234.50983436853	168.948573	1.3881	0.170088	0.085044
t	11.00983436853	1.569122	7.0166	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.846483642463992
R-squared	0.716534556959107
Adjusted R-squared	0.661670277660869
F-TEST (value)	13.0601288511253
F-TEST (DF numerator)	12
F-TEST (DF denominator)	62
p-value	8.07798272717264e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	292.614891203776
Sum Squared Residuals	5308655.42236025

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.846483642463992 \tabularnewline
R-squared & 0.716534556959107 \tabularnewline
Adjusted R-squared & 0.661670277660869 \tabularnewline
F-TEST (value) & 13.0601288511253 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 8.07798272717264e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 292.614891203776 \tabularnewline
Sum Squared Residuals & 5308655.42236025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.846483642463992[/C][/ROW]
[ROW][C]R-squared[/C][C]0.716534556959107[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.661670277660869[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.0601288511253[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/C][/ROW]
[ROW][C]p-value[/C][C]8.07798272717264e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]292.614891203776[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5308655.42236025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&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	0.846483642463992
R-squared	0.716534556959107
Adjusted R-squared	0.661670277660869
F-TEST (value)	13.0601288511253
F-TEST (DF numerator)	12
F-TEST (DF denominator)	62
p-value	8.07798272717264e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	292.614891203776
Sum Squared Residuals	5308655.42236025

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9700	9449.21739130436	250.782608695645
2	9081	8717.07453416149	363.92546583851
3	9084	9075.78881987578	8.21118012422378
4	9743	9383.37163561077	359.628364389234
5	8587	8505.87163561077	81.128364389234
6	9731	9472.37163561077	258.628364389234
7	9563	9098.03830227743	464.961697722568
8	9998	9277.37163561077	720.628364389234
9	9437	9232.7049689441	204.295031055901
10	10038	9807.87163561077	230.128364389234
11	9918	9686.2049689441	231.795031055901
12	9252	9462.7049689441	-210.704968944099
13	9737	9581.3354037267	155.664596273294
14	9035	8849.19254658385	185.807453416149
15	9133	9207.90683229814	-74.9068322981364
16	9487	9515.48964803313	-28.4896480331261
17	8700	8637.98964803313	62.010351966874
18	9627	9604.48964803313	22.510351966874
19	8947	9230.1563146998	-283.156314699793
20	9283	9409.48964803313	-126.489648033126
21	8829	9364.82298136646	-535.82298136646
22	9947	9939.98964803313	7.01035196687387
23	9628	9818.32298136646	-190.32298136646
24	9318	9594.82298136646	-276.82298136646
25	9605	9713.45341614907	-108.453416149067
26	8640	8981.31055900621	-341.310559006211
27	9214	9340.0248447205	-126.024844720497
28	9567	9647.60766045549	-80.6076604554865
29	8547	8770.10766045549	-223.107660455486
30	9185	9736.60766045549	-551.607660455487
31	9470	9362.27432712215	107.725672877847
32	9123	9541.60766045549	-418.607660455486
33	9278	9496.94099378882	-218.94099378882
34	10170	10072.1076604555	97.8923395445136
35	9434	9950.44099378882	-516.44099378882
36	9655	9726.94099378882	-71.9409937888199
37	9429	9845.57142857143	-416.571428571427
38	8739	9113.42857142857	-374.428571428571
39	9552	9472.14285714286	79.857142857143
40	9687	9779.72567287785	-92.7256728778469
41	9019	8902.22567287785	116.774327122153
42	9672	9868.72567287785	-196.725672877847
43	9206	9494.39233954451	-288.392339544514
44	9069	9673.72567287785	-604.725672877847
45	9788	9629.05900621118	158.94099378882
46	10312	10204.2256728778	107.774327122153
47	10105	10082.5590062112	22.4409937888197
48	9863	9859.05900621118	3.94099378881965
49	9656	9977.68944099379	-321.689440993788
50	9295	9245.54658385093	49.4534161490682
51	9946	9604.26086956522	341.739130434783
52	9701	9911.8436853002	-210.843685300207
53	9049	9034.3436853002	14.6563146997928
54	10190	10000.8436853002	189.156314699793
55	9706	9626.51035196687	79.489648033126
56	9765	9805.8436853002	-40.8436853002073
57	9893	9761.17701863354	131.822981366459
58	9994	10336.3436853002	-342.343685300207
59	10433	10214.6770186335	218.32298136646
60	10073	9991.17701863354	81.8229813664593
61	10112	10109.8074534161	2.19254658385198
62	9266	9377.6645962733	-111.664596273292
63	9820	9736.37888198758	83.6211180124221
64	10097	10043.9616977226	53.0383022774324
65	9115	9166.46169772257	-51.4616977225677
66	10411	10132.9616977226	278.038302277433
67	9678	9758.62836438923	-80.6283643892342
68	10408	9937.96169772257	470.038302277433
69	10153	9893.2950310559	259.704968944099
70	10368	10468.4616977226	-100.461697722567
71	10581	10346.7950310559	234.204968944099
72	10597	10123.2950310559	473.704968944099
73	10680	10241.9254658385	438.074534161492
74	9738	9509.78260869565	228.217391304347
75	9556	9868.49689440994	-312.496894409938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9700 & 9449.21739130436 & 250.782608695645 \tabularnewline
2 & 9081 & 8717.07453416149 & 363.92546583851 \tabularnewline
3 & 9084 & 9075.78881987578 & 8.21118012422378 \tabularnewline
4 & 9743 & 9383.37163561077 & 359.628364389234 \tabularnewline
5 & 8587 & 8505.87163561077 & 81.128364389234 \tabularnewline
6 & 9731 & 9472.37163561077 & 258.628364389234 \tabularnewline
7 & 9563 & 9098.03830227743 & 464.961697722568 \tabularnewline
8 & 9998 & 9277.37163561077 & 720.628364389234 \tabularnewline
9 & 9437 & 9232.7049689441 & 204.295031055901 \tabularnewline
10 & 10038 & 9807.87163561077 & 230.128364389234 \tabularnewline
11 & 9918 & 9686.2049689441 & 231.795031055901 \tabularnewline
12 & 9252 & 9462.7049689441 & -210.704968944099 \tabularnewline
13 & 9737 & 9581.3354037267 & 155.664596273294 \tabularnewline
14 & 9035 & 8849.19254658385 & 185.807453416149 \tabularnewline
15 & 9133 & 9207.90683229814 & -74.9068322981364 \tabularnewline
16 & 9487 & 9515.48964803313 & -28.4896480331261 \tabularnewline
17 & 8700 & 8637.98964803313 & 62.010351966874 \tabularnewline
18 & 9627 & 9604.48964803313 & 22.510351966874 \tabularnewline
19 & 8947 & 9230.1563146998 & -283.156314699793 \tabularnewline
20 & 9283 & 9409.48964803313 & -126.489648033126 \tabularnewline
21 & 8829 & 9364.82298136646 & -535.82298136646 \tabularnewline
22 & 9947 & 9939.98964803313 & 7.01035196687387 \tabularnewline
23 & 9628 & 9818.32298136646 & -190.32298136646 \tabularnewline
24 & 9318 & 9594.82298136646 & -276.82298136646 \tabularnewline
25 & 9605 & 9713.45341614907 & -108.453416149067 \tabularnewline
26 & 8640 & 8981.31055900621 & -341.310559006211 \tabularnewline
27 & 9214 & 9340.0248447205 & -126.024844720497 \tabularnewline
28 & 9567 & 9647.60766045549 & -80.6076604554865 \tabularnewline
29 & 8547 & 8770.10766045549 & -223.107660455486 \tabularnewline
30 & 9185 & 9736.60766045549 & -551.607660455487 \tabularnewline
31 & 9470 & 9362.27432712215 & 107.725672877847 \tabularnewline
32 & 9123 & 9541.60766045549 & -418.607660455486 \tabularnewline
33 & 9278 & 9496.94099378882 & -218.94099378882 \tabularnewline
34 & 10170 & 10072.1076604555 & 97.8923395445136 \tabularnewline
35 & 9434 & 9950.44099378882 & -516.44099378882 \tabularnewline
36 & 9655 & 9726.94099378882 & -71.9409937888199 \tabularnewline
37 & 9429 & 9845.57142857143 & -416.571428571427 \tabularnewline
38 & 8739 & 9113.42857142857 & -374.428571428571 \tabularnewline
39 & 9552 & 9472.14285714286 & 79.857142857143 \tabularnewline
40 & 9687 & 9779.72567287785 & -92.7256728778469 \tabularnewline
41 & 9019 & 8902.22567287785 & 116.774327122153 \tabularnewline
42 & 9672 & 9868.72567287785 & -196.725672877847 \tabularnewline
43 & 9206 & 9494.39233954451 & -288.392339544514 \tabularnewline
44 & 9069 & 9673.72567287785 & -604.725672877847 \tabularnewline
45 & 9788 & 9629.05900621118 & 158.94099378882 \tabularnewline
46 & 10312 & 10204.2256728778 & 107.774327122153 \tabularnewline
47 & 10105 & 10082.5590062112 & 22.4409937888197 \tabularnewline
48 & 9863 & 9859.05900621118 & 3.94099378881965 \tabularnewline
49 & 9656 & 9977.68944099379 & -321.689440993788 \tabularnewline
50 & 9295 & 9245.54658385093 & 49.4534161490682 \tabularnewline
51 & 9946 & 9604.26086956522 & 341.739130434783 \tabularnewline
52 & 9701 & 9911.8436853002 & -210.843685300207 \tabularnewline
53 & 9049 & 9034.3436853002 & 14.6563146997928 \tabularnewline
54 & 10190 & 10000.8436853002 & 189.156314699793 \tabularnewline
55 & 9706 & 9626.51035196687 & 79.489648033126 \tabularnewline
56 & 9765 & 9805.8436853002 & -40.8436853002073 \tabularnewline
57 & 9893 & 9761.17701863354 & 131.822981366459 \tabularnewline
58 & 9994 & 10336.3436853002 & -342.343685300207 \tabularnewline
59 & 10433 & 10214.6770186335 & 218.32298136646 \tabularnewline
60 & 10073 & 9991.17701863354 & 81.8229813664593 \tabularnewline
61 & 10112 & 10109.8074534161 & 2.19254658385198 \tabularnewline
62 & 9266 & 9377.6645962733 & -111.664596273292 \tabularnewline
63 & 9820 & 9736.37888198758 & 83.6211180124221 \tabularnewline
64 & 10097 & 10043.9616977226 & 53.0383022774324 \tabularnewline
65 & 9115 & 9166.46169772257 & -51.4616977225677 \tabularnewline
66 & 10411 & 10132.9616977226 & 278.038302277433 \tabularnewline
67 & 9678 & 9758.62836438923 & -80.6283643892342 \tabularnewline
68 & 10408 & 9937.96169772257 & 470.038302277433 \tabularnewline
69 & 10153 & 9893.2950310559 & 259.704968944099 \tabularnewline
70 & 10368 & 10468.4616977226 & -100.461697722567 \tabularnewline
71 & 10581 & 10346.7950310559 & 234.204968944099 \tabularnewline
72 & 10597 & 10123.2950310559 & 473.704968944099 \tabularnewline
73 & 10680 & 10241.9254658385 & 438.074534161492 \tabularnewline
74 & 9738 & 9509.78260869565 & 228.217391304347 \tabularnewline
75 & 9556 & 9868.49689440994 & -312.496894409938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&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]9700[/C][C]9449.21739130436[/C][C]250.782608695645[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]8717.07453416149[/C][C]363.92546583851[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]9075.78881987578[/C][C]8.21118012422378[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9383.37163561077[/C][C]359.628364389234[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]8505.87163561077[/C][C]81.128364389234[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9472.37163561077[/C][C]258.628364389234[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9098.03830227743[/C][C]464.961697722568[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]9277.37163561077[/C][C]720.628364389234[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9232.7049689441[/C][C]204.295031055901[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]9807.87163561077[/C][C]230.128364389234[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9686.2049689441[/C][C]231.795031055901[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9462.7049689441[/C][C]-210.704968944099[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9581.3354037267[/C][C]155.664596273294[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]8849.19254658385[/C][C]185.807453416149[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9207.90683229814[/C][C]-74.9068322981364[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9515.48964803313[/C][C]-28.4896480331261[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]8637.98964803313[/C][C]62.010351966874[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9604.48964803313[/C][C]22.510351966874[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]9230.1563146998[/C][C]-283.156314699793[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9409.48964803313[/C][C]-126.489648033126[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]9364.82298136646[/C][C]-535.82298136646[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9939.98964803313[/C][C]7.01035196687387[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9818.32298136646[/C][C]-190.32298136646[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9594.82298136646[/C][C]-276.82298136646[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9713.45341614907[/C][C]-108.453416149067[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]8981.31055900621[/C][C]-341.310559006211[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9340.0248447205[/C][C]-126.024844720497[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9647.60766045549[/C][C]-80.6076604554865[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]8770.10766045549[/C][C]-223.107660455486[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]9736.60766045549[/C][C]-551.607660455487[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9362.27432712215[/C][C]107.725672877847[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9541.60766045549[/C][C]-418.607660455486[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9496.94099378882[/C][C]-218.94099378882[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]10072.1076604555[/C][C]97.8923395445136[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9950.44099378882[/C][C]-516.44099378882[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9726.94099378882[/C][C]-71.9409937888199[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9845.57142857143[/C][C]-416.571428571427[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]9113.42857142857[/C][C]-374.428571428571[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9472.14285714286[/C][C]79.857142857143[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9779.72567287785[/C][C]-92.7256728778469[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]8902.22567287785[/C][C]116.774327122153[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9868.72567287785[/C][C]-196.725672877847[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9494.39233954451[/C][C]-288.392339544514[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]9673.72567287785[/C][C]-604.725672877847[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9629.05900621118[/C][C]158.94099378882[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]10204.2256728778[/C][C]107.774327122153[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]10082.5590062112[/C][C]22.4409937888197[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9859.05900621118[/C][C]3.94099378881965[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9977.68944099379[/C][C]-321.689440993788[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9245.54658385093[/C][C]49.4534161490682[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]9604.26086956522[/C][C]341.739130434783[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9911.8436853002[/C][C]-210.843685300207[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9034.3436853002[/C][C]14.6563146997928[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]10000.8436853002[/C][C]189.156314699793[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9626.51035196687[/C][C]79.489648033126[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9805.8436853002[/C][C]-40.8436853002073[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9761.17701863354[/C][C]131.822981366459[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]10336.3436853002[/C][C]-342.343685300207[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]10214.6770186335[/C][C]218.32298136646[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9991.17701863354[/C][C]81.8229813664593[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10109.8074534161[/C][C]2.19254658385198[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9377.6645962733[/C][C]-111.664596273292[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9736.37888198758[/C][C]83.6211180124221[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]10043.9616977226[/C][C]53.0383022774324[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9166.46169772257[/C][C]-51.4616977225677[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]10132.9616977226[/C][C]278.038302277433[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9758.62836438923[/C][C]-80.6283643892342[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]9937.96169772257[/C][C]470.038302277433[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]9893.2950310559[/C][C]259.704968944099[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10468.4616977226[/C][C]-100.461697722567[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10346.7950310559[/C][C]234.204968944099[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10123.2950310559[/C][C]473.704968944099[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10241.9254658385[/C][C]438.074534161492[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]9509.78260869565[/C][C]228.217391304347[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]9868.49689440994[/C][C]-312.496894409938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&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	9700	9449.21739130436	250.782608695645
2	9081	8717.07453416149	363.92546583851
3	9084	9075.78881987578	8.21118012422378
4	9743	9383.37163561077	359.628364389234
5	8587	8505.87163561077	81.128364389234
6	9731	9472.37163561077	258.628364389234
7	9563	9098.03830227743	464.961697722568
8	9998	9277.37163561077	720.628364389234
9	9437	9232.7049689441	204.295031055901
10	10038	9807.87163561077	230.128364389234
11	9918	9686.2049689441	231.795031055901
12	9252	9462.7049689441	-210.704968944099
13	9737	9581.3354037267	155.664596273294
14	9035	8849.19254658385	185.807453416149
15	9133	9207.90683229814	-74.9068322981364
16	9487	9515.48964803313	-28.4896480331261
17	8700	8637.98964803313	62.010351966874
18	9627	9604.48964803313	22.510351966874
19	8947	9230.1563146998	-283.156314699793
20	9283	9409.48964803313	-126.489648033126
21	8829	9364.82298136646	-535.82298136646
22	9947	9939.98964803313	7.01035196687387
23	9628	9818.32298136646	-190.32298136646
24	9318	9594.82298136646	-276.82298136646
25	9605	9713.45341614907	-108.453416149067
26	8640	8981.31055900621	-341.310559006211
27	9214	9340.0248447205	-126.024844720497
28	9567	9647.60766045549	-80.6076604554865
29	8547	8770.10766045549	-223.107660455486
30	9185	9736.60766045549	-551.607660455487
31	9470	9362.27432712215	107.725672877847
32	9123	9541.60766045549	-418.607660455486
33	9278	9496.94099378882	-218.94099378882
34	10170	10072.1076604555	97.8923395445136
35	9434	9950.44099378882	-516.44099378882
36	9655	9726.94099378882	-71.9409937888199
37	9429	9845.57142857143	-416.571428571427
38	8739	9113.42857142857	-374.428571428571
39	9552	9472.14285714286	79.857142857143
40	9687	9779.72567287785	-92.7256728778469
41	9019	8902.22567287785	116.774327122153
42	9672	9868.72567287785	-196.725672877847
43	9206	9494.39233954451	-288.392339544514
44	9069	9673.72567287785	-604.725672877847
45	9788	9629.05900621118	158.94099378882
46	10312	10204.2256728778	107.774327122153
47	10105	10082.5590062112	22.4409937888197
48	9863	9859.05900621118	3.94099378881965
49	9656	9977.68944099379	-321.689440993788
50	9295	9245.54658385093	49.4534161490682
51	9946	9604.26086956522	341.739130434783
52	9701	9911.8436853002	-210.843685300207
53	9049	9034.3436853002	14.6563146997928
54	10190	10000.8436853002	189.156314699793
55	9706	9626.51035196687	79.489648033126
56	9765	9805.8436853002	-40.8436853002073
57	9893	9761.17701863354	131.822981366459
58	9994	10336.3436853002	-342.343685300207
59	10433	10214.6770186335	218.32298136646
60	10073	9991.17701863354	81.8229813664593
61	10112	10109.8074534161	2.19254658385198
62	9266	9377.6645962733	-111.664596273292
63	9820	9736.37888198758	83.6211180124221
64	10097	10043.9616977226	53.0383022774324
65	9115	9166.46169772257	-51.4616977225677
66	10411	10132.9616977226	278.038302277433
67	9678	9758.62836438923	-80.6283643892342
68	10408	9937.96169772257	470.038302277433
69	10153	9893.2950310559	259.704968944099
70	10368	10468.4616977226	-100.461697722567
71	10581	10346.7950310559	234.204968944099
72	10597	10123.2950310559	473.704968944099
73	10680	10241.9254658385	438.074534161492
74	9738	9509.78260869565	228.217391304347
75	9556	9868.49689440994	-312.496894409938

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0857950324780312	0.171590064956062	0.914204967521969
17	0.0524991423628487	0.104998284725697	0.947500857637151
18	0.023104396566747	0.0462087931334939	0.976895603433253
19	0.231495210219769	0.462990420439537	0.768504789780231
20	0.455792536295224	0.911585072590448	0.544207463704776
21	0.504624854768491	0.990750290463017	0.495375145231509
22	0.437677666203556	0.875355332407112	0.562322333796444
23	0.340351871668432	0.680703743336865	0.659648128331568
24	0.310855860228004	0.621711720456009	0.689144139771996
25	0.267236336881096	0.534472673762192	0.732763663118904
26	0.20684988325679	0.413699766513581	0.79315011674321
27	0.23991625264691	0.47983250529382	0.76008374735309
28	0.205631454710158	0.411262909420317	0.794368545289842
29	0.153268958573314	0.306537917146629	0.846731041426686
30	0.173008461432075	0.34601692286415	0.826991538567925
31	0.268002998181083	0.536005996362166	0.731997001818917
32	0.261416420975536	0.522832841951071	0.738583579024464
33	0.268458916033421	0.536917832066843	0.731541083966579
34	0.342995129165927	0.685990258331853	0.657004870834073
35	0.358773674727471	0.717547349454941	0.641226325272529
36	0.431905718587353	0.863811437174706	0.568094281412647
37	0.380362945006806	0.760725890013611	0.619637054993194
38	0.329100876838706	0.658201753677411	0.670899123161294
39	0.450505003315906	0.901010006631813	0.549494996684094
40	0.403190199995984	0.806380399991967	0.596809800004017
41	0.485094127735028	0.970188255470056	0.514905872264972
42	0.454317102071593	0.908634204143185	0.545682897928407
43	0.380756271864458	0.761512543728915	0.619243728135542
44	0.606126854145477	0.787746291709047	0.393873145854523
45	0.674996769060583	0.650006461878834	0.325003230939417
46	0.752290768263355	0.495418463473291	0.247709231736645
47	0.728811920081673	0.542376159836653	0.271188079918327
48	0.704138728110714	0.591722543778572	0.295861271889286
49	0.74712682558055	0.505746348838901	0.25287317441945
50	0.699421991351507	0.601156017296987	0.300578008648493
51	0.916224561685615	0.167550876628771	0.0837754383143854
52	0.872687059430543	0.254625881138913	0.127312940569457
53	0.8375866932	0.324826613600001	0.1624133068
54	0.791320712635808	0.417358574728383	0.208679287364191
55	0.7894838579563	0.421032284087401	0.210516142043701
56	0.775925335602774	0.448149328794452	0.224074664397226
57	0.673058641197512	0.653882717604975	0.326941358802487
58	0.53945230881435	0.9210953823713	0.46054769118565
59	0.416110442244623	0.832220884489246	0.583889557755377

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0857950324780312 & 0.171590064956062 & 0.914204967521969 \tabularnewline
17 & 0.0524991423628487 & 0.104998284725697 & 0.947500857637151 \tabularnewline
18 & 0.023104396566747 & 0.0462087931334939 & 0.976895603433253 \tabularnewline
19 & 0.231495210219769 & 0.462990420439537 & 0.768504789780231 \tabularnewline
20 & 0.455792536295224 & 0.911585072590448 & 0.544207463704776 \tabularnewline
21 & 0.504624854768491 & 0.990750290463017 & 0.495375145231509 \tabularnewline
22 & 0.437677666203556 & 0.875355332407112 & 0.562322333796444 \tabularnewline
23 & 0.340351871668432 & 0.680703743336865 & 0.659648128331568 \tabularnewline
24 & 0.310855860228004 & 0.621711720456009 & 0.689144139771996 \tabularnewline
25 & 0.267236336881096 & 0.534472673762192 & 0.732763663118904 \tabularnewline
26 & 0.20684988325679 & 0.413699766513581 & 0.79315011674321 \tabularnewline
27 & 0.23991625264691 & 0.47983250529382 & 0.76008374735309 \tabularnewline
28 & 0.205631454710158 & 0.411262909420317 & 0.794368545289842 \tabularnewline
29 & 0.153268958573314 & 0.306537917146629 & 0.846731041426686 \tabularnewline
30 & 0.173008461432075 & 0.34601692286415 & 0.826991538567925 \tabularnewline
31 & 0.268002998181083 & 0.536005996362166 & 0.731997001818917 \tabularnewline
32 & 0.261416420975536 & 0.522832841951071 & 0.738583579024464 \tabularnewline
33 & 0.268458916033421 & 0.536917832066843 & 0.731541083966579 \tabularnewline
34 & 0.342995129165927 & 0.685990258331853 & 0.657004870834073 \tabularnewline
35 & 0.358773674727471 & 0.717547349454941 & 0.641226325272529 \tabularnewline
36 & 0.431905718587353 & 0.863811437174706 & 0.568094281412647 \tabularnewline
37 & 0.380362945006806 & 0.760725890013611 & 0.619637054993194 \tabularnewline
38 & 0.329100876838706 & 0.658201753677411 & 0.670899123161294 \tabularnewline
39 & 0.450505003315906 & 0.901010006631813 & 0.549494996684094 \tabularnewline
40 & 0.403190199995984 & 0.806380399991967 & 0.596809800004017 \tabularnewline
41 & 0.485094127735028 & 0.970188255470056 & 0.514905872264972 \tabularnewline
42 & 0.454317102071593 & 0.908634204143185 & 0.545682897928407 \tabularnewline
43 & 0.380756271864458 & 0.761512543728915 & 0.619243728135542 \tabularnewline
44 & 0.606126854145477 & 0.787746291709047 & 0.393873145854523 \tabularnewline
45 & 0.674996769060583 & 0.650006461878834 & 0.325003230939417 \tabularnewline
46 & 0.752290768263355 & 0.495418463473291 & 0.247709231736645 \tabularnewline
47 & 0.728811920081673 & 0.542376159836653 & 0.271188079918327 \tabularnewline
48 & 0.704138728110714 & 0.591722543778572 & 0.295861271889286 \tabularnewline
49 & 0.74712682558055 & 0.505746348838901 & 0.25287317441945 \tabularnewline
50 & 0.699421991351507 & 0.601156017296987 & 0.300578008648493 \tabularnewline
51 & 0.916224561685615 & 0.167550876628771 & 0.0837754383143854 \tabularnewline
52 & 0.872687059430543 & 0.254625881138913 & 0.127312940569457 \tabularnewline
53 & 0.8375866932 & 0.324826613600001 & 0.1624133068 \tabularnewline
54 & 0.791320712635808 & 0.417358574728383 & 0.208679287364191 \tabularnewline
55 & 0.7894838579563 & 0.421032284087401 & 0.210516142043701 \tabularnewline
56 & 0.775925335602774 & 0.448149328794452 & 0.224074664397226 \tabularnewline
57 & 0.673058641197512 & 0.653882717604975 & 0.326941358802487 \tabularnewline
58 & 0.53945230881435 & 0.9210953823713 & 0.46054769118565 \tabularnewline
59 & 0.416110442244623 & 0.832220884489246 & 0.583889557755377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&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]16[/C][C]0.0857950324780312[/C][C]0.171590064956062[/C][C]0.914204967521969[/C][/ROW]
[ROW][C]17[/C][C]0.0524991423628487[/C][C]0.104998284725697[/C][C]0.947500857637151[/C][/ROW]
[ROW][C]18[/C][C]0.023104396566747[/C][C]0.0462087931334939[/C][C]0.976895603433253[/C][/ROW]
[ROW][C]19[/C][C]0.231495210219769[/C][C]0.462990420439537[/C][C]0.768504789780231[/C][/ROW]
[ROW][C]20[/C][C]0.455792536295224[/C][C]0.911585072590448[/C][C]0.544207463704776[/C][/ROW]
[ROW][C]21[/C][C]0.504624854768491[/C][C]0.990750290463017[/C][C]0.495375145231509[/C][/ROW]
[ROW][C]22[/C][C]0.437677666203556[/C][C]0.875355332407112[/C][C]0.562322333796444[/C][/ROW]
[ROW][C]23[/C][C]0.340351871668432[/C][C]0.680703743336865[/C][C]0.659648128331568[/C][/ROW]
[ROW][C]24[/C][C]0.310855860228004[/C][C]0.621711720456009[/C][C]0.689144139771996[/C][/ROW]
[ROW][C]25[/C][C]0.267236336881096[/C][C]0.534472673762192[/C][C]0.732763663118904[/C][/ROW]
[ROW][C]26[/C][C]0.20684988325679[/C][C]0.413699766513581[/C][C]0.79315011674321[/C][/ROW]
[ROW][C]27[/C][C]0.23991625264691[/C][C]0.47983250529382[/C][C]0.76008374735309[/C][/ROW]
[ROW][C]28[/C][C]0.205631454710158[/C][C]0.411262909420317[/C][C]0.794368545289842[/C][/ROW]
[ROW][C]29[/C][C]0.153268958573314[/C][C]0.306537917146629[/C][C]0.846731041426686[/C][/ROW]
[ROW][C]30[/C][C]0.173008461432075[/C][C]0.34601692286415[/C][C]0.826991538567925[/C][/ROW]
[ROW][C]31[/C][C]0.268002998181083[/C][C]0.536005996362166[/C][C]0.731997001818917[/C][/ROW]
[ROW][C]32[/C][C]0.261416420975536[/C][C]0.522832841951071[/C][C]0.738583579024464[/C][/ROW]
[ROW][C]33[/C][C]0.268458916033421[/C][C]0.536917832066843[/C][C]0.731541083966579[/C][/ROW]
[ROW][C]34[/C][C]0.342995129165927[/C][C]0.685990258331853[/C][C]0.657004870834073[/C][/ROW]
[ROW][C]35[/C][C]0.358773674727471[/C][C]0.717547349454941[/C][C]0.641226325272529[/C][/ROW]
[ROW][C]36[/C][C]0.431905718587353[/C][C]0.863811437174706[/C][C]0.568094281412647[/C][/ROW]
[ROW][C]37[/C][C]0.380362945006806[/C][C]0.760725890013611[/C][C]0.619637054993194[/C][/ROW]
[ROW][C]38[/C][C]0.329100876838706[/C][C]0.658201753677411[/C][C]0.670899123161294[/C][/ROW]
[ROW][C]39[/C][C]0.450505003315906[/C][C]0.901010006631813[/C][C]0.549494996684094[/C][/ROW]
[ROW][C]40[/C][C]0.403190199995984[/C][C]0.806380399991967[/C][C]0.596809800004017[/C][/ROW]
[ROW][C]41[/C][C]0.485094127735028[/C][C]0.970188255470056[/C][C]0.514905872264972[/C][/ROW]
[ROW][C]42[/C][C]0.454317102071593[/C][C]0.908634204143185[/C][C]0.545682897928407[/C][/ROW]
[ROW][C]43[/C][C]0.380756271864458[/C][C]0.761512543728915[/C][C]0.619243728135542[/C][/ROW]
[ROW][C]44[/C][C]0.606126854145477[/C][C]0.787746291709047[/C][C]0.393873145854523[/C][/ROW]
[ROW][C]45[/C][C]0.674996769060583[/C][C]0.650006461878834[/C][C]0.325003230939417[/C][/ROW]
[ROW][C]46[/C][C]0.752290768263355[/C][C]0.495418463473291[/C][C]0.247709231736645[/C][/ROW]
[ROW][C]47[/C][C]0.728811920081673[/C][C]0.542376159836653[/C][C]0.271188079918327[/C][/ROW]
[ROW][C]48[/C][C]0.704138728110714[/C][C]0.591722543778572[/C][C]0.295861271889286[/C][/ROW]
[ROW][C]49[/C][C]0.74712682558055[/C][C]0.505746348838901[/C][C]0.25287317441945[/C][/ROW]
[ROW][C]50[/C][C]0.699421991351507[/C][C]0.601156017296987[/C][C]0.300578008648493[/C][/ROW]
[ROW][C]51[/C][C]0.916224561685615[/C][C]0.167550876628771[/C][C]0.0837754383143854[/C][/ROW]
[ROW][C]52[/C][C]0.872687059430543[/C][C]0.254625881138913[/C][C]0.127312940569457[/C][/ROW]
[ROW][C]53[/C][C]0.8375866932[/C][C]0.324826613600001[/C][C]0.1624133068[/C][/ROW]
[ROW][C]54[/C][C]0.791320712635808[/C][C]0.417358574728383[/C][C]0.208679287364191[/C][/ROW]
[ROW][C]55[/C][C]0.7894838579563[/C][C]0.421032284087401[/C][C]0.210516142043701[/C][/ROW]
[ROW][C]56[/C][C]0.775925335602774[/C][C]0.448149328794452[/C][C]0.224074664397226[/C][/ROW]
[ROW][C]57[/C][C]0.673058641197512[/C][C]0.653882717604975[/C][C]0.326941358802487[/C][/ROW]
[ROW][C]58[/C][C]0.53945230881435[/C][C]0.9210953823713[/C][C]0.46054769118565[/C][/ROW]
[ROW][C]59[/C][C]0.416110442244623[/C][C]0.832220884489246[/C][C]0.583889557755377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&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
16	0.0857950324780312	0.171590064956062	0.914204967521969
17	0.0524991423628487	0.104998284725697	0.947500857637151
18	0.023104396566747	0.0462087931334939	0.976895603433253
19	0.231495210219769	0.462990420439537	0.768504789780231
20	0.455792536295224	0.911585072590448	0.544207463704776
21	0.504624854768491	0.990750290463017	0.495375145231509
22	0.437677666203556	0.875355332407112	0.562322333796444
23	0.340351871668432	0.680703743336865	0.659648128331568
24	0.310855860228004	0.621711720456009	0.689144139771996
25	0.267236336881096	0.534472673762192	0.732763663118904
26	0.20684988325679	0.413699766513581	0.79315011674321
27	0.23991625264691	0.47983250529382	0.76008374735309
28	0.205631454710158	0.411262909420317	0.794368545289842
29	0.153268958573314	0.306537917146629	0.846731041426686
30	0.173008461432075	0.34601692286415	0.826991538567925
31	0.268002998181083	0.536005996362166	0.731997001818917
32	0.261416420975536	0.522832841951071	0.738583579024464
33	0.268458916033421	0.536917832066843	0.731541083966579
34	0.342995129165927	0.685990258331853	0.657004870834073
35	0.358773674727471	0.717547349454941	0.641226325272529
36	0.431905718587353	0.863811437174706	0.568094281412647
37	0.380362945006806	0.760725890013611	0.619637054993194
38	0.329100876838706	0.658201753677411	0.670899123161294
39	0.450505003315906	0.901010006631813	0.549494996684094
40	0.403190199995984	0.806380399991967	0.596809800004017
41	0.485094127735028	0.970188255470056	0.514905872264972
42	0.454317102071593	0.908634204143185	0.545682897928407
43	0.380756271864458	0.761512543728915	0.619243728135542
44	0.606126854145477	0.787746291709047	0.393873145854523
45	0.674996769060583	0.650006461878834	0.325003230939417
46	0.752290768263355	0.495418463473291	0.247709231736645
47	0.728811920081673	0.542376159836653	0.271188079918327
48	0.704138728110714	0.591722543778572	0.295861271889286
49	0.74712682558055	0.505746348838901	0.25287317441945
50	0.699421991351507	0.601156017296987	0.300578008648493
51	0.916224561685615	0.167550876628771	0.0837754383143854
52	0.872687059430543	0.254625881138913	0.127312940569457
53	0.8375866932	0.324826613600001	0.1624133068
54	0.791320712635808	0.417358574728383	0.208679287364191
55	0.7894838579563	0.421032284087401	0.210516142043701
56	0.775925335602774	0.448149328794452	0.224074664397226
57	0.673058641197512	0.653882717604975	0.326941358802487
58	0.53945230881435	0.9210953823713	0.46054769118565
59	0.416110442244623	0.832220884489246	0.583889557755377

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	1	0.0227272727272727	OK
10% type I error level	1	0.0227272727272727	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 & 1 & 0.0227272727272727 & OK \tabularnewline
10% type I error level & 1 & 0.0227272727272727 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147924&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]1[/C][C]0.0227272727272727[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0227272727272727[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147924&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147924&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	1	0.0227272727272727	OK
10% type I error level	1	0.0227272727272727	OK

Figure 1

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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 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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