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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 19 Nov 2012 11:51:03 -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/2012/Nov/19/t1353344003c1mfvjouxo89dqv.htm/, Retrieved Sun, 28 Apr 2024 13:16:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190661, Retrieved Sun, 28 Apr 2024 13:16:56 +0000

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2012-11-17 11:47:21] [6808ef3204f32b6b44f616bd4c52b0ae]
-   PD    [Multiple Regression] [] [2012-11-19 12:16:25] [6808ef3204f32b6b44f616bd4c52b0ae]
- R PD        [Multiple Regression] [] [2012-11-19 16:51:03] [2bb2c61d8bf509471ce26eaff71e2f73] [Current]

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36	27	71	8.1	3.34	11.4	81.5	3243	8.8	42.6	11.7	21	15	59	59	921
35	23	72	11.1	3.14	11.0	78.8	4281	3.6	50.7	14.4	8	10	39	57	997
44	29	74	10.4	3.21	9.8	81.6	4260	0.8	39.4	12.4	6	6	33	54	962
47	45	79	6.5	3.41	11.1	77.5	3125	27.1	50.2	20.6	18	8	24	56	982
43	35	77	7.6	3.44	9.6	84.6	6441	24.4	43.7	14.3	43	38	206	55	107
53	45	80	7.7	3.45	10.2	66.8	3325	38.5	43.1	25.5	30	32	72	54	103
43	30	74	10.9	3.23	12.1	83.9	4679	3.5	49.2	11.3	21	32	62	56	934
45	30	73	9.3	3.29	10.6	86.0	2140	5.3	40.4	10.5	6	4	4	56	899
36	24	70	9.0	3.31	10.5	83.2	6582	8.1	42.5	12.6	18	12	37	61	100
36	27	72	9.5	3.36	10.7	79.3	4213	6.7	41.0	13.2	12	7	20	59	912
52	42	79	7.7	3.39	9.6	69.2	2302	22.2	41.3	24.2	18	8	27	56	101
33	26	76	8.6	3.20	10.9	83.4	6122	16.3	44.9	10.7	88	63	278	58	102
40	34	77	9.2	3.21	10.2	77.0	4101	13.0	45.7	15.1	26	26	146	57	970
35	28	71	8.8	3.29	11.1	86.3	3042	14.7	44.6	11.4	31	21	64	60	985
37	31	75	8.0	3.26	11.9	78.4	4259	13.1	49.6	13.9	23	9	15	58	958
35	46	85	7.1	3.22	11.8	79.9	1441	14.8	51.2	16.1	1	1	1	54	860
36	30	75	7.5	3.35	11.4	81.9	4029	12.4	44.0	12.0	6	4	16	58	936
15	30	73	8.2	3.15	12.2	84.2	4824	4.7	53.1	12.7	17	8	28	38	871
31	27	74	7.2	3.44	10.8	87.0	4834	15.8	43.5	13.6	52	35	124	59	959
30	24	72	6.5	3.53	10.8	79.5	3694	13.1	33.8	12.4	11	4	11	61	941
31	45	85	7.3	3.22	11.4	80.7	1844	11.5	48.1	18.5	1	1	1	53	891
31	24	72	9.0	3.37	10.9	82.8	3226	5.1	45.2	12.3	5	3	10	61	871
42	40	77	6.1	3.45	10.4	71.8	2269	22.7	41.4	19.5	8	3	5	53	971
43	27	72	9.0	3.25	11.5	87.1	2909	7.2	51.6	9.5	7	3	10	56	887
46	55	84	5.6	3.35	11.4	79.7	2647	21.0	46.9	17.9	6	5	1	59	952
39	29	76	8.7	3.23	11.4	78.6	4412	15.6	46.6	13.2	13	7	33	60	968
35	31	81	9.2	3.10	12.0	78.3	3262	12.6	48.6	13.9	7	4	4	55	919
43	32	74	10.1	3.38	9.5	79.2	3214	2.9	43.7	12.0	11	7	32	54	844
11	53	68	9.2	2.99	12.1	90.6	4700	7.8	48.9	12.3	648	319	130	47	861
30	35	71	8.3	3.37	9.9	77.4	4474	13.1	42.6	17.7	38	37	193	57	989
50	42	82	7.3	3.49	10.4	72.5	3497	36.7	43.3	26.4	15	10	34	59	100
60	67	82	10.0	2.98	11.5	88.6	4657	13.6	47.3	22.4	3	1	1	60	861
30	20	69	8.8	3.26	11.1	85.4	2934	5.8	44.0	9.4	33	23	125	64	929
25	12	73	9.2	3.28	12.1	83.1	2095	2.0	51.9	9.8	20	11	26	50	857
45	40	80	8.3	3.32	10.1	70.3	2682	21.0	46.1	24.1	17	14	78	56	961
46	30	72	10.2	3.16	11.3	83.2	3327	8.8	45.3	12.2	4	3	8	58	923
54	54	81	7.4	3.36	9.7	72.8	3172	31.4	45.5	24.2	20	17	1	62	111
42	33	77	9.7	3.03	10.7	83.5	7462	11.3	48.7	12.4	41	26	108	58	994
42	32	76	9.1	3.32	10.5	87.5	6092	17.5	45.3	13.2	29	32	161	54	101
36	29	72	9.5	3.32	10.6	77.6	3437	8.1	45.5	13.8	45	59	263	56	991
37	38	67	11.3	2.99	12.0	81.5	3387	3.6	50.3	13.5	56	21	44	73	893
42	29	72	10.7	3.19	10.1	79.5	3508	2.2	38.3	15.7	6	4	18	56	938
41	33	77	11.2	3.08	9.6	79.9	4843	2.7	38.6	14.1	11	11	89	54	946
44	39	78	8.2	3.32	11.0	79.9	3768	28.6	49.5	17.5	12	9	48	53	102
32	25	72	10.9	3.21	11.1	82.5	4355	5.0	46.4	10.8	7	4	18	60	874
34	32	79	9.3	3.23	9.7	76.8	5160	17.2	45.1	15.3	31	15	68	57	953
10	55	70	7.3	3.11	12.1	88.9	3033	5.9	51.0	14.0	144	66	20	61	839
18	48	63	9.2	2.92	12.2	87.7	4253	13.7	51.2	12.0	311	171	86	71	911
13	49	68	7.0	3.36	12.2	90.7	2702	3.0	51.9	9.7	105	32	3	71	790
35	40	64	9.6	3.02	12.2	82.5	3626	5.7	54.3	10.1	20	7	20	72	899
45	28	74	10.6	3.21	11.1	82.6	1883	3.4	41.9	12.3	5	4	20	56	904
38	24	72	9.8	3.34	11.4	78.0	4923	3.8	50.5	11.1	8	5	25	61	950
31	26	73	9.3	3.22	10.7	81.3	3249	9.5	43.9	13.6	11	7	25	59	972
40	23	71	11.3	3.28	10.3	73.8	1671	2.5	47.4	13.5	5	2	11	60	912
41	37	78	6.2	3.25	12.3	89.5	5308	25.9	59.7	10.3	65	28	102	52	967
28	32	81	7.0	3.27	12.1	81.0	3665	7.5	51.6	13.2	4	2	1	54	823
45	33	76	7.7	3.39	11.3	82.2	3152	12.1	47.3	10.9	14	11	42	56	100
45	24	70	11.8	3.25	11.1	79.8	3678	1.0	44.8	14.0	7	3	8	56	895
42	83	76	9.7	3.22	9.0	76.2	9699	4.8	42.2	14.5	8	8	49	54	911
38	28	72	8.9	3.48	10.7	79.8	3451	11.7	37.5	13.0	14	13	39	58	954

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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	8 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
sterftecijfer[t] = + 5573.72291821456 + 0.185156860093506Gem_jaarlijkse_neerslag[t] + 1.02574417873282Gem_temp_januari[t] -1.75174487619193Gem_temp_juli[t] -66.0779665382136`Omvang_bevolking_>65jaar`[t] -757.873102331411`#leden_per_huishouden`[t] + 43.0198663038218`#jaren_onderwijs_personen>22j`[t] -15.0410952553819huishoudens_met_volledig_uitgeruste_keuken[t] -0.0234468835274374`bevolking_per_mijl\302\262`[t] -20.7521944074557`omvang_niet-blanke_bevolking`[t] -6.31414431985576`#kantoormedewerkers`[t] -9.42415121583915`#gezinnen_inkomen<$3000`[t] -0.0779986609488321index_olievervuiling[t] -0.357116769719697index_stikstofoxidevervuiling[t] + 0.0103298677001577index_zwaveldioxidevervuiling[t] -2.20668562764833luchtvochtigheidgraad[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
sterftecijfer[t] =  +  5573.72291821456 +  0.185156860093506Gem_jaarlijkse_neerslag[t] +  1.02574417873282Gem_temp_januari[t] -1.75174487619193Gem_temp_juli[t] -66.0779665382136`Omvang_bevolking_>65jaar`[t] -757.873102331411`#leden_per_huishouden`[t] +  43.0198663038218`#jaren_onderwijs_personen>22j`[t] -15.0410952553819huishoudens_met_volledig_uitgeruste_keuken[t] -0.0234468835274374`bevolking_per_mijl\302\262`[t] -20.7521944074557`omvang_niet-blanke_bevolking`[t] -6.31414431985576`#kantoormedewerkers`[t] -9.42415121583915`#gezinnen_inkomen<$3000`[t] -0.0779986609488321index_olievervuiling[t] -0.357116769719697index_stikstofoxidevervuiling[t] +  0.0103298677001577index_zwaveldioxidevervuiling[t] -2.20668562764833luchtvochtigheidgraad[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]sterftecijfer[t] =  +  5573.72291821456 +  0.185156860093506Gem_jaarlijkse_neerslag[t] +  1.02574417873282Gem_temp_januari[t] -1.75174487619193Gem_temp_juli[t] -66.0779665382136`Omvang_bevolking_>65jaar`[t] -757.873102331411`#leden_per_huishouden`[t] +  43.0198663038218`#jaren_onderwijs_personen>22j`[t] -15.0410952553819huishoudens_met_volledig_uitgeruste_keuken[t] -0.0234468835274374`bevolking_per_mijl\302\262`[t] -20.7521944074557`omvang_niet-blanke_bevolking`[t] -6.31414431985576`#kantoormedewerkers`[t] -9.42415121583915`#gezinnen_inkomen<$3000`[t] -0.0779986609488321index_olievervuiling[t] -0.357116769719697index_stikstofoxidevervuiling[t] +  0.0103298677001577index_zwaveldioxidevervuiling[t] -2.20668562764833luchtvochtigheidgraad[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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
sterftecijfer[t] = + 5573.72291821456 + 0.185156860093506Gem_jaarlijkse_neerslag[t] + 1.02574417873282Gem_temp_januari[t] -1.75174487619193Gem_temp_juli[t] -66.0779665382136`Omvang_bevolking_>65jaar`[t] -757.873102331411`#leden_per_huishouden`[t] + 43.0198663038218`#jaren_onderwijs_personen>22j`[t] -15.0410952553819huishoudens_met_volledig_uitgeruste_keuken[t] -0.0234468835274374`bevolking_per_mijl\302\262`[t] -20.7521944074557`omvang_niet-blanke_bevolking`[t] -6.31414431985576`#kantoormedewerkers`[t] -9.42415121583915`#gezinnen_inkomen<$3000`[t] -0.0779986609488321index_olievervuiling[t] -0.357116769719697index_stikstofoxidevervuiling[t] + 0.0103298677001577index_zwaveldioxidevervuiling[t] -2.20668562764833luchtvochtigheidgraad[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)	5573.72291821456	3574.614741	1.5593	0.126101	0.063051
Gem_jaarlijkse_neerslag	0.185156860093506	7.324137	0.0253	0.979946	0.489973
Gem_temp_januari	1.02574417873282	5.874779	0.1746	0.862194	0.431097
Gem_temp_juli	-1.75174487619193	15.408229	-0.1137	0.910002	0.455001
`Omvang_bevolking_>65jaar`	-66.0779665382136	67.401677	-0.9804	0.332268	0.166134
`#leden_per_huishouden`	-757.873102331411	539.493434	-1.4048	0.167106	0.083553
`#jaren_onderwijs_personen>22j`	43.0198663038218	97.554801	0.441	0.661386	0.330693
huishoudens_met_volledig_uitgeruste_keuken	-15.0410952553819	12.760139	-1.1788	0.24483	0.122415
`bevolking_per_mijl\302\262`	-0.0234468835274374	0.035875	-0.6536	0.516793	0.258397
`omvang_niet-blanke_bevolking`	-20.7521944074557	11.158573	-1.8598	0.069614	0.034807
`#kantoormedewerkers`	-6.31414431985576	13.49535	-0.4679	0.642183	0.321091
`#gezinnen_inkomen<$3000`	-9.42415121583915	22.318748	-0.4223	0.674897	0.337448
index_olievervuiling	-0.0779986609488321	3.936371	-0.0198	0.984281	0.49214
index_stikstofoxidevervuiling	-0.357116769719697	8.131	-0.0439	0.965167	0.482583
index_zwaveldioxidevervuiling	0.0103298677001577	1.237964	0.0083	0.99338	0.49669
luchtvochtigheidgraad	-2.20668562764833	9.150472	-0.2412	0.810555	0.405277

\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) & 5573.72291821456 & 3574.614741 & 1.5593 & 0.126101 & 0.063051 \tabularnewline
Gem_jaarlijkse_neerslag & 0.185156860093506 & 7.324137 & 0.0253 & 0.979946 & 0.489973 \tabularnewline
Gem_temp_januari & 1.02574417873282 & 5.874779 & 0.1746 & 0.862194 & 0.431097 \tabularnewline
Gem_temp_juli & -1.75174487619193 & 15.408229 & -0.1137 & 0.910002 & 0.455001 \tabularnewline
`Omvang_bevolking_>65jaar` & -66.0779665382136 & 67.401677 & -0.9804 & 0.332268 & 0.166134 \tabularnewline
`#leden_per_huishouden` & -757.873102331411 & 539.493434 & -1.4048 & 0.167106 & 0.083553 \tabularnewline
`#jaren_onderwijs_personen>22j` & 43.0198663038218 & 97.554801 & 0.441 & 0.661386 & 0.330693 \tabularnewline
huishoudens_met_volledig_uitgeruste_keuken & -15.0410952553819 & 12.760139 & -1.1788 & 0.24483 & 0.122415 \tabularnewline
`bevolking_per_mijl\302\262` & -0.0234468835274374 & 0.035875 & -0.6536 & 0.516793 & 0.258397 \tabularnewline
`omvang_niet-blanke_bevolking` & -20.7521944074557 & 11.158573 & -1.8598 & 0.069614 & 0.034807 \tabularnewline
`#kantoormedewerkers` & -6.31414431985576 & 13.49535 & -0.4679 & 0.642183 & 0.321091 \tabularnewline
`#gezinnen_inkomen<$3000` & -9.42415121583915 & 22.318748 & -0.4223 & 0.674897 & 0.337448 \tabularnewline
index_olievervuiling & -0.0779986609488321 & 3.936371 & -0.0198 & 0.984281 & 0.49214 \tabularnewline
index_stikstofoxidevervuiling & -0.357116769719697 & 8.131 & -0.0439 & 0.965167 & 0.482583 \tabularnewline
index_zwaveldioxidevervuiling & 0.0103298677001577 & 1.237964 & 0.0083 & 0.99338 & 0.49669 \tabularnewline
luchtvochtigheidgraad & -2.20668562764833 & 9.150472 & -0.2412 & 0.810555 & 0.405277 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&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]5573.72291821456[/C][C]3574.614741[/C][C]1.5593[/C][C]0.126101[/C][C]0.063051[/C][/ROW]
[ROW][C]Gem_jaarlijkse_neerslag[/C][C]0.185156860093506[/C][C]7.324137[/C][C]0.0253[/C][C]0.979946[/C][C]0.489973[/C][/ROW]
[ROW][C]Gem_temp_januari[/C][C]1.02574417873282[/C][C]5.874779[/C][C]0.1746[/C][C]0.862194[/C][C]0.431097[/C][/ROW]
[ROW][C]Gem_temp_juli[/C][C]-1.75174487619193[/C][C]15.408229[/C][C]-0.1137[/C][C]0.910002[/C][C]0.455001[/C][/ROW]
[ROW][C]`Omvang_bevolking_>65jaar`[/C][C]-66.0779665382136[/C][C]67.401677[/C][C]-0.9804[/C][C]0.332268[/C][C]0.166134[/C][/ROW]
[ROW][C]`#leden_per_huishouden`[/C][C]-757.873102331411[/C][C]539.493434[/C][C]-1.4048[/C][C]0.167106[/C][C]0.083553[/C][/ROW]
[ROW][C]`#jaren_onderwijs_personen>22j`[/C][C]43.0198663038218[/C][C]97.554801[/C][C]0.441[/C][C]0.661386[/C][C]0.330693[/C][/ROW]
[ROW][C]huishoudens_met_volledig_uitgeruste_keuken[/C][C]-15.0410952553819[/C][C]12.760139[/C][C]-1.1788[/C][C]0.24483[/C][C]0.122415[/C][/ROW]
[ROW][C]`bevolking_per_mijl\302\262`[/C][C]-0.0234468835274374[/C][C]0.035875[/C][C]-0.6536[/C][C]0.516793[/C][C]0.258397[/C][/ROW]
[ROW][C]`omvang_niet-blanke_bevolking`[/C][C]-20.7521944074557[/C][C]11.158573[/C][C]-1.8598[/C][C]0.069614[/C][C]0.034807[/C][/ROW]
[ROW][C]`#kantoormedewerkers`[/C][C]-6.31414431985576[/C][C]13.49535[/C][C]-0.4679[/C][C]0.642183[/C][C]0.321091[/C][/ROW]
[ROW][C]`#gezinnen_inkomen<$3000`[/C][C]-9.42415121583915[/C][C]22.318748[/C][C]-0.4223[/C][C]0.674897[/C][C]0.337448[/C][/ROW]
[ROW][C]index_olievervuiling[/C][C]-0.0779986609488321[/C][C]3.936371[/C][C]-0.0198[/C][C]0.984281[/C][C]0.49214[/C][/ROW]
[ROW][C]index_stikstofoxidevervuiling[/C][C]-0.357116769719697[/C][C]8.131[/C][C]-0.0439[/C][C]0.965167[/C][C]0.482583[/C][/ROW]
[ROW][C]index_zwaveldioxidevervuiling[/C][C]0.0103298677001577[/C][C]1.237964[/C][C]0.0083[/C][C]0.99338[/C][C]0.49669[/C][/ROW]
[ROW][C]luchtvochtigheidgraad[/C][C]-2.20668562764833[/C][C]9.150472[/C][C]-0.2412[/C][C]0.810555[/C][C]0.405277[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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)	5573.72291821456	3574.614741	1.5593	0.126101	0.063051
Gem_jaarlijkse_neerslag	0.185156860093506	7.324137	0.0253	0.979946	0.489973
Gem_temp_januari	1.02574417873282	5.874779	0.1746	0.862194	0.431097
Gem_temp_juli	-1.75174487619193	15.408229	-0.1137	0.910002	0.455001
`Omvang_bevolking_>65jaar`	-66.0779665382136	67.401677	-0.9804	0.332268	0.166134
`#leden_per_huishouden`	-757.873102331411	539.493434	-1.4048	0.167106	0.083553
`#jaren_onderwijs_personen>22j`	43.0198663038218	97.554801	0.441	0.661386	0.330693
huishoudens_met_volledig_uitgeruste_keuken	-15.0410952553819	12.760139	-1.1788	0.24483	0.122415
`bevolking_per_mijl\302\262`	-0.0234468835274374	0.035875	-0.6536	0.516793	0.258397
`omvang_niet-blanke_bevolking`	-20.7521944074557	11.158573	-1.8598	0.069614	0.034807
`#kantoormedewerkers`	-6.31414431985576	13.49535	-0.4679	0.642183	0.321091
`#gezinnen_inkomen<$3000`	-9.42415121583915	22.318748	-0.4223	0.674897	0.337448
index_olievervuiling	-0.0779986609488321	3.936371	-0.0198	0.984281	0.49214
index_stikstofoxidevervuiling	-0.357116769719697	8.131	-0.0439	0.965167	0.482583
index_zwaveldioxidevervuiling	0.0103298677001577	1.237964	0.0083	0.99338	0.49669
luchtvochtigheidgraad	-2.20668562764833	9.150472	-0.2412	0.810555	0.405277

Multiple Linear Regression - Regression Statistics
Multiple R	0.624498386666687
R-squared	0.389998234949295
Adjusted R-squared	0.182043087772918
F-TEST (value)	1.87539592188367
F-TEST (DF numerator)	15
F-TEST (DF denominator)	44
p-value	0.0535559662015176
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	281.335775779578
Sum Squared Residuals	3482592.02427386

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.624498386666687 \tabularnewline
R-squared & 0.389998234949295 \tabularnewline
Adjusted R-squared & 0.182043087772918 \tabularnewline
F-TEST (value) & 1.87539592188367 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0.0535559662015176 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 281.335775779578 \tabularnewline
Sum Squared Residuals & 3482592.02427386 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.624498386666687[/C][/ROW]
[ROW][C]R-squared[/C][C]0.389998234949295[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.182043087772918[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.87539592188367[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0.0535559662015176[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]281.335775779578[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3482592.02427386[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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	0.624498386666687
R-squared	0.389998234949295
Adjusted R-squared	0.182043087772918
F-TEST (value)	1.87539592188367
F-TEST (DF numerator)	15
F-TEST (DF denominator)	44
p-value	0.0535559662015176
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	281.335775779578
Sum Squared Residuals	3482592.02427386

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	921	907.276909018646	13.7230909813539
2	997	891.970870973005	105.029129026995
3	962	952.692015386276	9.30798461372387
4	982	513.826577947916	468.173422052084
5	107	309.706868237439	-202.706868237439
6	103	278.589239115447	-175.589239115447
7	934	837.751292686365	96.248707313635
8	899	899.861207581887	-0.861207581886657
9	100	729.981491208755	-629.981491208755
10	912	820.793644679549	91.2063553204512
11	101	651.132790489304	-550.132790489304
12	102	673.770201771466	-571.770201771466
13	970	788.68729351898	181.31270648102
14	985	682.049467553252	302.950532446748
15	958	865.683298588334	92.3167014116655
16	860	939.317016013865	-79.3170160138652
17	936	831.455340813285	104.544659186715
18	871	1055.27917833629	-184.279178336291
19	959	561.193983828368	397.806016171632
20	941	816.293768104562	124.706231895438
21	891	953.289945945515	-62.2899459455154
22	871	833.976722773196	37.0232772268036
23	971	749.312745470374	221.687254529626
24	887	852.063404490594	34.9365955094056
25	952	779.125815998156	172.874184001844
26	968	781.552109489621	186.447890510379
27	919	952.181523456547	-33.1815234565473
28	844	826.56895423211	17.4310457678903
29	861	831.283807896779	29.7161921032207
30	989	697.531396489493	291.468603510507
31	100	211.616145819393	-111.616145819393
32	861	718.59610784849	142.403892151511
33	929	913.491129603782	15.5088703962183
34	857	1013.42734822636	-156.427348226358
35	961	649.491986076389	311.508013923611
36	923	861.796982764346	61.2030172356539
37	111	398.305049407989	-287.305049407989
38	994	774.360181968579	219.639818031421
39	101	451.736312263537	-350.736312263537
40	991	817.487688609385	173.512311390615
41	893	1008.4017197109	-115.401719710897
42	938	956.222281681493	-18.2222816814931
43	946	947.945756994411	-1.94575699441064
44	102	418.752789000392	-316.752789000392
45	874	827.954637655654	46.0453623443457
46	953	634.401153110721	318.598846889279
47	839	1036.68622341773	-197.686223417734
48	911	839.253574216682	71.7464257833183
49	790	937.898704716838	-147.898704716838
50	899	1065.86583583595	-166.865835835949
51	904	962.699729371848	-58.6997293718483
52	950	863.093057451586	86.9069425484144
53	972	848.871628812539	123.128371187461
54	912	929.945971546506	-17.9459715465057
55	967	528.101883833459	438.898116166541
56	823	1019.30079389803	-196.300793898027
57	100	799.979014003808	-699.979014003808
58	895	871.573511024697	23.4264889753032
59	911	840.97861269217	70.02138730783
60	954	701.565306340964	252.434693659036

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 921 & 907.276909018646 & 13.7230909813539 \tabularnewline
2 & 997 & 891.970870973005 & 105.029129026995 \tabularnewline
3 & 962 & 952.692015386276 & 9.30798461372387 \tabularnewline
4 & 982 & 513.826577947916 & 468.173422052084 \tabularnewline
5 & 107 & 309.706868237439 & -202.706868237439 \tabularnewline
6 & 103 & 278.589239115447 & -175.589239115447 \tabularnewline
7 & 934 & 837.751292686365 & 96.248707313635 \tabularnewline
8 & 899 & 899.861207581887 & -0.861207581886657 \tabularnewline
9 & 100 & 729.981491208755 & -629.981491208755 \tabularnewline
10 & 912 & 820.793644679549 & 91.2063553204512 \tabularnewline
11 & 101 & 651.132790489304 & -550.132790489304 \tabularnewline
12 & 102 & 673.770201771466 & -571.770201771466 \tabularnewline
13 & 970 & 788.68729351898 & 181.31270648102 \tabularnewline
14 & 985 & 682.049467553252 & 302.950532446748 \tabularnewline
15 & 958 & 865.683298588334 & 92.3167014116655 \tabularnewline
16 & 860 & 939.317016013865 & -79.3170160138652 \tabularnewline
17 & 936 & 831.455340813285 & 104.544659186715 \tabularnewline
18 & 871 & 1055.27917833629 & -184.279178336291 \tabularnewline
19 & 959 & 561.193983828368 & 397.806016171632 \tabularnewline
20 & 941 & 816.293768104562 & 124.706231895438 \tabularnewline
21 & 891 & 953.289945945515 & -62.2899459455154 \tabularnewline
22 & 871 & 833.976722773196 & 37.0232772268036 \tabularnewline
23 & 971 & 749.312745470374 & 221.687254529626 \tabularnewline
24 & 887 & 852.063404490594 & 34.9365955094056 \tabularnewline
25 & 952 & 779.125815998156 & 172.874184001844 \tabularnewline
26 & 968 & 781.552109489621 & 186.447890510379 \tabularnewline
27 & 919 & 952.181523456547 & -33.1815234565473 \tabularnewline
28 & 844 & 826.56895423211 & 17.4310457678903 \tabularnewline
29 & 861 & 831.283807896779 & 29.7161921032207 \tabularnewline
30 & 989 & 697.531396489493 & 291.468603510507 \tabularnewline
31 & 100 & 211.616145819393 & -111.616145819393 \tabularnewline
32 & 861 & 718.59610784849 & 142.403892151511 \tabularnewline
33 & 929 & 913.491129603782 & 15.5088703962183 \tabularnewline
34 & 857 & 1013.42734822636 & -156.427348226358 \tabularnewline
35 & 961 & 649.491986076389 & 311.508013923611 \tabularnewline
36 & 923 & 861.796982764346 & 61.2030172356539 \tabularnewline
37 & 111 & 398.305049407989 & -287.305049407989 \tabularnewline
38 & 994 & 774.360181968579 & 219.639818031421 \tabularnewline
39 & 101 & 451.736312263537 & -350.736312263537 \tabularnewline
40 & 991 & 817.487688609385 & 173.512311390615 \tabularnewline
41 & 893 & 1008.4017197109 & -115.401719710897 \tabularnewline
42 & 938 & 956.222281681493 & -18.2222816814931 \tabularnewline
43 & 946 & 947.945756994411 & -1.94575699441064 \tabularnewline
44 & 102 & 418.752789000392 & -316.752789000392 \tabularnewline
45 & 874 & 827.954637655654 & 46.0453623443457 \tabularnewline
46 & 953 & 634.401153110721 & 318.598846889279 \tabularnewline
47 & 839 & 1036.68622341773 & -197.686223417734 \tabularnewline
48 & 911 & 839.253574216682 & 71.7464257833183 \tabularnewline
49 & 790 & 937.898704716838 & -147.898704716838 \tabularnewline
50 & 899 & 1065.86583583595 & -166.865835835949 \tabularnewline
51 & 904 & 962.699729371848 & -58.6997293718483 \tabularnewline
52 & 950 & 863.093057451586 & 86.9069425484144 \tabularnewline
53 & 972 & 848.871628812539 & 123.128371187461 \tabularnewline
54 & 912 & 929.945971546506 & -17.9459715465057 \tabularnewline
55 & 967 & 528.101883833459 & 438.898116166541 \tabularnewline
56 & 823 & 1019.30079389803 & -196.300793898027 \tabularnewline
57 & 100 & 799.979014003808 & -699.979014003808 \tabularnewline
58 & 895 & 871.573511024697 & 23.4264889753032 \tabularnewline
59 & 911 & 840.97861269217 & 70.02138730783 \tabularnewline
60 & 954 & 701.565306340964 & 252.434693659036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&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]921[/C][C]907.276909018646[/C][C]13.7230909813539[/C][/ROW]
[ROW][C]2[/C][C]997[/C][C]891.970870973005[/C][C]105.029129026995[/C][/ROW]
[ROW][C]3[/C][C]962[/C][C]952.692015386276[/C][C]9.30798461372387[/C][/ROW]
[ROW][C]4[/C][C]982[/C][C]513.826577947916[/C][C]468.173422052084[/C][/ROW]
[ROW][C]5[/C][C]107[/C][C]309.706868237439[/C][C]-202.706868237439[/C][/ROW]
[ROW][C]6[/C][C]103[/C][C]278.589239115447[/C][C]-175.589239115447[/C][/ROW]
[ROW][C]7[/C][C]934[/C][C]837.751292686365[/C][C]96.248707313635[/C][/ROW]
[ROW][C]8[/C][C]899[/C][C]899.861207581887[/C][C]-0.861207581886657[/C][/ROW]
[ROW][C]9[/C][C]100[/C][C]729.981491208755[/C][C]-629.981491208755[/C][/ROW]
[ROW][C]10[/C][C]912[/C][C]820.793644679549[/C][C]91.2063553204512[/C][/ROW]
[ROW][C]11[/C][C]101[/C][C]651.132790489304[/C][C]-550.132790489304[/C][/ROW]
[ROW][C]12[/C][C]102[/C][C]673.770201771466[/C][C]-571.770201771466[/C][/ROW]
[ROW][C]13[/C][C]970[/C][C]788.68729351898[/C][C]181.31270648102[/C][/ROW]
[ROW][C]14[/C][C]985[/C][C]682.049467553252[/C][C]302.950532446748[/C][/ROW]
[ROW][C]15[/C][C]958[/C][C]865.683298588334[/C][C]92.3167014116655[/C][/ROW]
[ROW][C]16[/C][C]860[/C][C]939.317016013865[/C][C]-79.3170160138652[/C][/ROW]
[ROW][C]17[/C][C]936[/C][C]831.455340813285[/C][C]104.544659186715[/C][/ROW]
[ROW][C]18[/C][C]871[/C][C]1055.27917833629[/C][C]-184.279178336291[/C][/ROW]
[ROW][C]19[/C][C]959[/C][C]561.193983828368[/C][C]397.806016171632[/C][/ROW]
[ROW][C]20[/C][C]941[/C][C]816.293768104562[/C][C]124.706231895438[/C][/ROW]
[ROW][C]21[/C][C]891[/C][C]953.289945945515[/C][C]-62.2899459455154[/C][/ROW]
[ROW][C]22[/C][C]871[/C][C]833.976722773196[/C][C]37.0232772268036[/C][/ROW]
[ROW][C]23[/C][C]971[/C][C]749.312745470374[/C][C]221.687254529626[/C][/ROW]
[ROW][C]24[/C][C]887[/C][C]852.063404490594[/C][C]34.9365955094056[/C][/ROW]
[ROW][C]25[/C][C]952[/C][C]779.125815998156[/C][C]172.874184001844[/C][/ROW]
[ROW][C]26[/C][C]968[/C][C]781.552109489621[/C][C]186.447890510379[/C][/ROW]
[ROW][C]27[/C][C]919[/C][C]952.181523456547[/C][C]-33.1815234565473[/C][/ROW]
[ROW][C]28[/C][C]844[/C][C]826.56895423211[/C][C]17.4310457678903[/C][/ROW]
[ROW][C]29[/C][C]861[/C][C]831.283807896779[/C][C]29.7161921032207[/C][/ROW]
[ROW][C]30[/C][C]989[/C][C]697.531396489493[/C][C]291.468603510507[/C][/ROW]
[ROW][C]31[/C][C]100[/C][C]211.616145819393[/C][C]-111.616145819393[/C][/ROW]
[ROW][C]32[/C][C]861[/C][C]718.59610784849[/C][C]142.403892151511[/C][/ROW]
[ROW][C]33[/C][C]929[/C][C]913.491129603782[/C][C]15.5088703962183[/C][/ROW]
[ROW][C]34[/C][C]857[/C][C]1013.42734822636[/C][C]-156.427348226358[/C][/ROW]
[ROW][C]35[/C][C]961[/C][C]649.491986076389[/C][C]311.508013923611[/C][/ROW]
[ROW][C]36[/C][C]923[/C][C]861.796982764346[/C][C]61.2030172356539[/C][/ROW]
[ROW][C]37[/C][C]111[/C][C]398.305049407989[/C][C]-287.305049407989[/C][/ROW]
[ROW][C]38[/C][C]994[/C][C]774.360181968579[/C][C]219.639818031421[/C][/ROW]
[ROW][C]39[/C][C]101[/C][C]451.736312263537[/C][C]-350.736312263537[/C][/ROW]
[ROW][C]40[/C][C]991[/C][C]817.487688609385[/C][C]173.512311390615[/C][/ROW]
[ROW][C]41[/C][C]893[/C][C]1008.4017197109[/C][C]-115.401719710897[/C][/ROW]
[ROW][C]42[/C][C]938[/C][C]956.222281681493[/C][C]-18.2222816814931[/C][/ROW]
[ROW][C]43[/C][C]946[/C][C]947.945756994411[/C][C]-1.94575699441064[/C][/ROW]
[ROW][C]44[/C][C]102[/C][C]418.752789000392[/C][C]-316.752789000392[/C][/ROW]
[ROW][C]45[/C][C]874[/C][C]827.954637655654[/C][C]46.0453623443457[/C][/ROW]
[ROW][C]46[/C][C]953[/C][C]634.401153110721[/C][C]318.598846889279[/C][/ROW]
[ROW][C]47[/C][C]839[/C][C]1036.68622341773[/C][C]-197.686223417734[/C][/ROW]
[ROW][C]48[/C][C]911[/C][C]839.253574216682[/C][C]71.7464257833183[/C][/ROW]
[ROW][C]49[/C][C]790[/C][C]937.898704716838[/C][C]-147.898704716838[/C][/ROW]
[ROW][C]50[/C][C]899[/C][C]1065.86583583595[/C][C]-166.865835835949[/C][/ROW]
[ROW][C]51[/C][C]904[/C][C]962.699729371848[/C][C]-58.6997293718483[/C][/ROW]
[ROW][C]52[/C][C]950[/C][C]863.093057451586[/C][C]86.9069425484144[/C][/ROW]
[ROW][C]53[/C][C]972[/C][C]848.871628812539[/C][C]123.128371187461[/C][/ROW]
[ROW][C]54[/C][C]912[/C][C]929.945971546506[/C][C]-17.9459715465057[/C][/ROW]
[ROW][C]55[/C][C]967[/C][C]528.101883833459[/C][C]438.898116166541[/C][/ROW]
[ROW][C]56[/C][C]823[/C][C]1019.30079389803[/C][C]-196.300793898027[/C][/ROW]
[ROW][C]57[/C][C]100[/C][C]799.979014003808[/C][C]-699.979014003808[/C][/ROW]
[ROW][C]58[/C][C]895[/C][C]871.573511024697[/C][C]23.4264889753032[/C][/ROW]
[ROW][C]59[/C][C]911[/C][C]840.97861269217[/C][C]70.02138730783[/C][/ROW]
[ROW][C]60[/C][C]954[/C][C]701.565306340964[/C][C]252.434693659036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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	921	907.276909018646	13.7230909813539
2	997	891.970870973005	105.029129026995
3	962	952.692015386276	9.30798461372387
4	982	513.826577947916	468.173422052084
5	107	309.706868237439	-202.706868237439
6	103	278.589239115447	-175.589239115447
7	934	837.751292686365	96.248707313635
8	899	899.861207581887	-0.861207581886657
9	100	729.981491208755	-629.981491208755
10	912	820.793644679549	91.2063553204512
11	101	651.132790489304	-550.132790489304
12	102	673.770201771466	-571.770201771466
13	970	788.68729351898	181.31270648102
14	985	682.049467553252	302.950532446748
15	958	865.683298588334	92.3167014116655
16	860	939.317016013865	-79.3170160138652
17	936	831.455340813285	104.544659186715
18	871	1055.27917833629	-184.279178336291
19	959	561.193983828368	397.806016171632
20	941	816.293768104562	124.706231895438
21	891	953.289945945515	-62.2899459455154
22	871	833.976722773196	37.0232772268036
23	971	749.312745470374	221.687254529626
24	887	852.063404490594	34.9365955094056
25	952	779.125815998156	172.874184001844
26	968	781.552109489621	186.447890510379
27	919	952.181523456547	-33.1815234565473
28	844	826.56895423211	17.4310457678903
29	861	831.283807896779	29.7161921032207
30	989	697.531396489493	291.468603510507
31	100	211.616145819393	-111.616145819393
32	861	718.59610784849	142.403892151511
33	929	913.491129603782	15.5088703962183
34	857	1013.42734822636	-156.427348226358
35	961	649.491986076389	311.508013923611
36	923	861.796982764346	61.2030172356539
37	111	398.305049407989	-287.305049407989
38	994	774.360181968579	219.639818031421
39	101	451.736312263537	-350.736312263537
40	991	817.487688609385	173.512311390615
41	893	1008.4017197109	-115.401719710897
42	938	956.222281681493	-18.2222816814931
43	946	947.945756994411	-1.94575699441064
44	102	418.752789000392	-316.752789000392
45	874	827.954637655654	46.0453623443457
46	953	634.401153110721	318.598846889279
47	839	1036.68622341773	-197.686223417734
48	911	839.253574216682	71.7464257833183
49	790	937.898704716838	-147.898704716838
50	899	1065.86583583595	-166.865835835949
51	904	962.699729371848	-58.6997293718483
52	950	863.093057451586	86.9069425484144
53	972	848.871628812539	123.128371187461
54	912	929.945971546506	-17.9459715465057
55	967	528.101883833459	438.898116166541
56	823	1019.30079389803	-196.300793898027
57	100	799.979014003808	-699.979014003808
58	895	871.573511024697	23.4264889753032
59	911	840.97861269217	70.02138730783
60	954	701.565306340964	252.434693659036

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.981699704945462	0.0366005901090767	0.0183002950545383
20	0.962234398034874	0.075531203930252	0.037765601965126
21	0.937277873032351	0.125444253935298	0.0627221269676491
22	0.940984021781788	0.118031956436424	0.0590159782182122
23	0.913529646813593	0.172940706372813	0.0864703531864066
24	0.891364009998625	0.21727198000275	0.108635990001375
25	0.866808847132101	0.266382305735798	0.133191152867899
26	0.838851115987548	0.322297768024904	0.161148884012452
27	0.764244752483654	0.471510495032691	0.235755247516346
28	0.683768644651709	0.632462710696582	0.316231355348291
29	0.655464704722277	0.689070590555446	0.344535295277723
30	0.575482992475101	0.849034015049797	0.424517007524899
31	0.505769308823971	0.988461382352058	0.494230691176029
32	0.622400315635568	0.755199368728864	0.377599684364432
33	0.538280144581992	0.923439710836016	0.461719855418008
34	0.458920404715844	0.917840809431689	0.541079595284156
35	0.441589627533018	0.883179255066036	0.558410372466982
36	0.39519211613507	0.79038423227014	0.60480788386493
37	0.739691008882264	0.520617982235472	0.260308991117736
38	0.715107896140202	0.569784207719596	0.284892103859798
39	0.5939246980435	0.812150603913	0.4060753019565
40	0.635102399494496	0.729795201011007	0.364897600505504
41	0.503691423829674	0.992617152340653	0.496308576170326

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.981699704945462 & 0.0366005901090767 & 0.0183002950545383 \tabularnewline
20 & 0.962234398034874 & 0.075531203930252 & 0.037765601965126 \tabularnewline
21 & 0.937277873032351 & 0.125444253935298 & 0.0627221269676491 \tabularnewline
22 & 0.940984021781788 & 0.118031956436424 & 0.0590159782182122 \tabularnewline
23 & 0.913529646813593 & 0.172940706372813 & 0.0864703531864066 \tabularnewline
24 & 0.891364009998625 & 0.21727198000275 & 0.108635990001375 \tabularnewline
25 & 0.866808847132101 & 0.266382305735798 & 0.133191152867899 \tabularnewline
26 & 0.838851115987548 & 0.322297768024904 & 0.161148884012452 \tabularnewline
27 & 0.764244752483654 & 0.471510495032691 & 0.235755247516346 \tabularnewline
28 & 0.683768644651709 & 0.632462710696582 & 0.316231355348291 \tabularnewline
29 & 0.655464704722277 & 0.689070590555446 & 0.344535295277723 \tabularnewline
30 & 0.575482992475101 & 0.849034015049797 & 0.424517007524899 \tabularnewline
31 & 0.505769308823971 & 0.988461382352058 & 0.494230691176029 \tabularnewline
32 & 0.622400315635568 & 0.755199368728864 & 0.377599684364432 \tabularnewline
33 & 0.538280144581992 & 0.923439710836016 & 0.461719855418008 \tabularnewline
34 & 0.458920404715844 & 0.917840809431689 & 0.541079595284156 \tabularnewline
35 & 0.441589627533018 & 0.883179255066036 & 0.558410372466982 \tabularnewline
36 & 0.39519211613507 & 0.79038423227014 & 0.60480788386493 \tabularnewline
37 & 0.739691008882264 & 0.520617982235472 & 0.260308991117736 \tabularnewline
38 & 0.715107896140202 & 0.569784207719596 & 0.284892103859798 \tabularnewline
39 & 0.5939246980435 & 0.812150603913 & 0.4060753019565 \tabularnewline
40 & 0.635102399494496 & 0.729795201011007 & 0.364897600505504 \tabularnewline
41 & 0.503691423829674 & 0.992617152340653 & 0.496308576170326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&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]19[/C][C]0.981699704945462[/C][C]0.0366005901090767[/C][C]0.0183002950545383[/C][/ROW]
[ROW][C]20[/C][C]0.962234398034874[/C][C]0.075531203930252[/C][C]0.037765601965126[/C][/ROW]
[ROW][C]21[/C][C]0.937277873032351[/C][C]0.125444253935298[/C][C]0.0627221269676491[/C][/ROW]
[ROW][C]22[/C][C]0.940984021781788[/C][C]0.118031956436424[/C][C]0.0590159782182122[/C][/ROW]
[ROW][C]23[/C][C]0.913529646813593[/C][C]0.172940706372813[/C][C]0.0864703531864066[/C][/ROW]
[ROW][C]24[/C][C]0.891364009998625[/C][C]0.21727198000275[/C][C]0.108635990001375[/C][/ROW]
[ROW][C]25[/C][C]0.866808847132101[/C][C]0.266382305735798[/C][C]0.133191152867899[/C][/ROW]
[ROW][C]26[/C][C]0.838851115987548[/C][C]0.322297768024904[/C][C]0.161148884012452[/C][/ROW]
[ROW][C]27[/C][C]0.764244752483654[/C][C]0.471510495032691[/C][C]0.235755247516346[/C][/ROW]
[ROW][C]28[/C][C]0.683768644651709[/C][C]0.632462710696582[/C][C]0.316231355348291[/C][/ROW]
[ROW][C]29[/C][C]0.655464704722277[/C][C]0.689070590555446[/C][C]0.344535295277723[/C][/ROW]
[ROW][C]30[/C][C]0.575482992475101[/C][C]0.849034015049797[/C][C]0.424517007524899[/C][/ROW]
[ROW][C]31[/C][C]0.505769308823971[/C][C]0.988461382352058[/C][C]0.494230691176029[/C][/ROW]
[ROW][C]32[/C][C]0.622400315635568[/C][C]0.755199368728864[/C][C]0.377599684364432[/C][/ROW]
[ROW][C]33[/C][C]0.538280144581992[/C][C]0.923439710836016[/C][C]0.461719855418008[/C][/ROW]
[ROW][C]34[/C][C]0.458920404715844[/C][C]0.917840809431689[/C][C]0.541079595284156[/C][/ROW]
[ROW][C]35[/C][C]0.441589627533018[/C][C]0.883179255066036[/C][C]0.558410372466982[/C][/ROW]
[ROW][C]36[/C][C]0.39519211613507[/C][C]0.79038423227014[/C][C]0.60480788386493[/C][/ROW]
[ROW][C]37[/C][C]0.739691008882264[/C][C]0.520617982235472[/C][C]0.260308991117736[/C][/ROW]
[ROW][C]38[/C][C]0.715107896140202[/C][C]0.569784207719596[/C][C]0.284892103859798[/C][/ROW]
[ROW][C]39[/C][C]0.5939246980435[/C][C]0.812150603913[/C][C]0.4060753019565[/C][/ROW]
[ROW][C]40[/C][C]0.635102399494496[/C][C]0.729795201011007[/C][C]0.364897600505504[/C][/ROW]
[ROW][C]41[/C][C]0.503691423829674[/C][C]0.992617152340653[/C][C]0.496308576170326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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
19	0.981699704945462	0.0366005901090767	0.0183002950545383
20	0.962234398034874	0.075531203930252	0.037765601965126
21	0.937277873032351	0.125444253935298	0.0627221269676491
22	0.940984021781788	0.118031956436424	0.0590159782182122
23	0.913529646813593	0.172940706372813	0.0864703531864066
24	0.891364009998625	0.21727198000275	0.108635990001375
25	0.866808847132101	0.266382305735798	0.133191152867899
26	0.838851115987548	0.322297768024904	0.161148884012452
27	0.764244752483654	0.471510495032691	0.235755247516346
28	0.683768644651709	0.632462710696582	0.316231355348291
29	0.655464704722277	0.689070590555446	0.344535295277723
30	0.575482992475101	0.849034015049797	0.424517007524899
31	0.505769308823971	0.988461382352058	0.494230691176029
32	0.622400315635568	0.755199368728864	0.377599684364432
33	0.538280144581992	0.923439710836016	0.461719855418008
34	0.458920404715844	0.917840809431689	0.541079595284156
35	0.441589627533018	0.883179255066036	0.558410372466982
36	0.39519211613507	0.79038423227014	0.60480788386493
37	0.739691008882264	0.520617982235472	0.260308991117736
38	0.715107896140202	0.569784207719596	0.284892103859798
39	0.5939246980435	0.812150603913	0.4060753019565
40	0.635102399494496	0.729795201011007	0.364897600505504
41	0.503691423829674	0.992617152340653	0.496308576170326

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	0.0434782608695652	OK
10% type I error level	2	0.0869565217391304	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 & 0.0434782608695652 & OK \tabularnewline
10% type I error level & 2 & 0.0869565217391304 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190661&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]0.0434782608695652[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0869565217391304[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190661&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190661&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	0.0434782608695652	OK
10% type I error level	2	0.0869565217391304	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 16 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 16 ; 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