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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 08 Dec 2011 08:59:54 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/08/t1323353193tcn6nuvywpg1s2f.htm/, Retrieved Fri, 03 May 2024 11:00:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152920, Retrieved Fri, 03 May 2024 11:00:22 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Paper: Anova (mod...] [2011-12-08 13:59:54] [e889f2ef2eeddd5259af4a52678400a6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	1	130631	130631	58	58	58	58	30	30	117	117	98956	98956
1	2	2	189326	189326	94	94	108	108	34	34	132	132	106816	106816
1	3	3	65295	65295	27	27	8	8	31	31	117	117	76173	76173
1	4	4	33186	33186	19	19	1	1	18	18	67	67	22807	22807
1	5	5	261949	261949	95	95	86	86	38	38	140	140	72117	72117
1	6	6	190794	190794	94	94	93	93	49	49	186	186	79738	79738
1	7	7	150733	150733	47	47	92	92	25	25	96	96	114029	114029
1	8	8	223226	223226	67	67	119	119	42	42	163	163	124550	124550
1	9	9	384138	384138	86	86	86	86	35	35	128	128	72875	72875
1	10	10	156540	156540	34	34	50	50	25	25	89	89	81964	81964
1	11	11	191441	191441	105	105	81	81	35	35	134	134	96740	96740
1	12	12	259667	259667	53	53	113	113	37	37	142	142	90694	90694
1	13	13	228871	228871	64	64	109	109	40	40	155	155	125369	125369
1	14	14	87485	87485	33	33	38	38	33	33	125	125	65702	65702
1	15	15	322865	322865	82	82	111	111	35	35	135	135	108179	108179
1	16	16	340093	340093	103	103	92	92	37	37	139	139	95066	95066
1	17	17	155363	155363	59	59	66	66	35	35	128	128	68966	68966
1	18	18	174198	174198	65	65	69	69	32	32	107	107	88766	88766
1	19	19	392666	392666	132	132	91	91	34	34	125	125	109249	109249
1	20	20	302674	302674	99	99	129	129	44	44	169	169	146648	146648
1	21	21	164733	164733	50	50	93	93	40	40	145	145	80613	80613
1	22	22	24188	24188	24	24	8	8	4	4	12	12	5950	5950
1	23	23	340411	340411	274	274	79	79	41	41	151	151	131106	131106
1	24	24	65029	65029	17	17	21	21	18	18	67	67	32551	32551
1	25	25	101097	101097	64	64	30	30	14	14	52	52	31701	31701
1	26	26	282220	282220	160	160	106	106	32	32	120	120	143950	143950
1	27	27	273495	273495	118	118	127	127	37	37	135	135	112368	112368
1	28	28	214872	214872	74	74	75	75	32	32	123	123	82124	82124
1	29	29	184474	184474	87	87	55	55	38	38	149	149	55062	55062
1	30	30	205675	205675	60	60	43	43	33	33	125	125	105612	105612
1	31	31	197760	197760	66	66	114	114	32	32	123	123	108461	108461
1	32	32	73566	73566	32	32	22	22	23	23	88	88	22618	22618
1	33	33	177949	177949	48	48	77	77	34	34	120	120	92059	92059
1	34	34	148698	148698	49	49	105	105	34	34	132	132	77993	77993
1	35	35	300103	300103	69	69	119	119	38	38	144	144	104155	104155
1	36	36	251437	251437	78	78	88	88	32	32	124	124	109840	109840
1	37	37	132672	132672	41	41	58	58	25	25	97	97	48194	48194
1	38	38	376465	376465	99	99	132	132	40	40	155	155	134796	134796
1	39	39	135042	135042	47	47	67	67	32	32	120	120	113402	113402
1	40	40	300074	300074	152	152	57	57	33	33	127	127	97967	97967
1	41	41	271757	271757	93	93	95	95	50	50	178	178	74844	74844
1	42	42	150949	150949	95	95	139	139	37	37	141	141	136051	136051
1	43	43	216802	216802	77	77	70	70	33	33	122	122	50548	50548
1	44	44	222599	222599	65	65	98	98	32	32	124	124	59938	59938
1	45	45	261601	261601	70	70	58	58	32	32	124	124	137639	137639
1	46	46	200657	200657	43	43	88	88	31	31	111	111	138599	138599
1	47	47	259084	259084	67	67	142	142	35	35	129	129	174110	174110
1	48	48	159965	159965	156	156	62	62	32	32	122	122	44244	44244
1	49	49	43287	43287	14	14	13	13	19	19	71	71	43750	43750
1	50	50	172212	172212	67	67	89	89	22	22	81	81	48029	48029
1	51	51	227681	227681	43	43	116	116	36	36	139	139	92288	92288
1	52	52	106288	106288	54	54	28	28	23	23	91	91	197426	197426
1	53	53	268905	268905	58	58	72	72	36	36	133	133	139206	139206
1	54	54	266568	266568	77	77	134	134	42	42	155	155	106271	106271
1	55	55	152474	152474	65	65	106	106	32	32	123	123	71764	71764
1	56	56	330910	330910	95	95	120	120	34	34	128	128	101817	101817
1	57	57	259747	259747	103	103	98	98	35	35	132	132	85008	85008
1	58	58	190495	190495	55	55	66	66	40	40	151	151	124254	124254
1	59	59	154984	154984	73	73	44	44	40	40	151	151	105793	105793
1	60	60	38214	38214	34	34	16	16	8	8	27	27	8773	8773
1	61	61	158671	158671	33	33	56	56	35	35	131	131	94747	94747
1	62	62	299775	299775	68	68	112	112	45	45	170	170	107549	107549
1	63	63	172464	172464	31	31	66	66	35	35	135	135	74783	74783
1	64	64	94381	94381	35	35	42	42	32	32	118	118	66089	66089
1	65	65	243875	243875	274	274	70	70	36	36	140	140	95684	95684
1	66	66	334926	334926	117	117	113	113	37	37	136	136	153824	153824
1	67	67	147979	147979	72	72	55	55	34	34	123	123	63995	63995
1	68	68	216638	216638	44	44	100	100	36	36	134	134	84891	84891
1	69	69	192853	192853	71	71	80	80	36	36	129	129	61263	61263
1	70	70	336678	336678	103	103	95	95	33	33	128	128	113587	113587
1	71	71	271773	271773	89	89	128	128	40	40	154	154	119906	119906
1	72	72	203606	203606	73	73	88	88	33	33	122	122	151611	151611
1	73	73	230177	230177	87	87	132	132	39	39	144	144	144645	144645
1	74	74	1	1	0	0	0	0	0	0	0	0	0	0
1	75	75	14688	14688	10	10	4	4	0	0	0	0	6023	6023
1	76	76	455	455	2	2	0	0	0	0	0	0	0	0
1	77	77	0	0	0	0	0	0	0	0	0	0	0	0
1	78	78	195765	195765	75	75	56	56	33	33	120	120	77457	77457
1	79	79	306514	306514	117	117	111	111	42	42	168	168	62464	62464
1	80	80	203	203	4	4	0	0	0	0	0	0	0	0
1	81	81	7199	7199	5	5	7	7	0	0	0	0	1644	1644
1	82	82	17547	17547	5	5	0	0	1	1	4	4	3926	3926
1	83	83	105044	105044	37	37	37	37	38	38	133	133	42087	42087
1	84	84	969	969	2	2	0	0	0	0	0	0	0	0
2	85	0	0	236496	0	61	0	85	0	34	0	131	0	124252
2	86	0	0	198514	0	62	0	62	0	38	0	146	0	98073
2	87	0	0	137449	0	43	0	55	0	25	0	80	0	41449
2	88	0	0	439387	0	103	0	134	0	29	0	112	0	177551
2	89	0	0	174859	0	50	0	63	0	30	0	116	0	126938
2	90	0	0	186657	0	38	0	77	0	29	0	107	0	61680
2	91	0	0	138866	0	57	0	44	0	33	0	109	0	57793
2	92	0	0	296878	0	65	0	106	0	46	0	159	0	91677
2	93	0	0	192648	0	71	0	63	0	38	0	146	0	64631
2	94	0	0	333348	0	161	0	160	0	52	0	201	0	106385
2	95	0	0	242212	0	57	0	104	0	32	0	124	0	161961
2	96	0	0	263451	0	130	0	86	0	35	0	131	0	112669
2	97	0	0	240028	0	63	0	107	0	40	0	151	0	105416
2	98	0	0	148421	0	43	0	92	0	46	0	184	0	104880
2	99	0	0	176502	0	96	0	123	0	36	0	136	0	76302
2	100	0	0	249735	0	120	0	93	0	38	0	146	0	93071
2	101	0	0	236812	0	76	0	113	0	35	0	130	0	78912
2	102	0	0	142329	0	45	0	52	0	28	0	105	0	35224
2	103	0	0	176054	0	66	0	44	0	42	0	154	0	80849
2	104	0	0	286683	0	79	0	123	0	44	0	169	0	104434
2	105	0	0	247013	0	50	0	77	0	37	0	139	0	63583
2	106	0	0	191653	0	72	0	74	0	32	0	124	0	62486
2	107	0	0	114673	0	31	0	33	0	17	0	55	0	31081
2	108	0	0	284210	0	160	0	105	0	34	0	131	0	94584
2	109	0	0	284195	0	72	0	108	0	33	0	125	0	87408
2	110	0	0	142986	0	48	0	62	0	35	0	130	0	57139
2	111	0	0	140319	0	73	0	50	0	45	0	73	0	90586
2	112	0	0	78800	0	42	0	20	0	26	0	82	0	33032
2	113	0	0	201970	0	69	0	101	0	45	0	173	0	96056
2	114	0	0	194221	0	68	0	89	0	33	0	134	0	87026
2	115	0	0	243889	0	45	0	86	0	33	0	121	0	91072
2	116	0	0	273003	0	74	0	116	0	49	0	186	0	159803
2	117	0	0	333165	0	122	0	138	0	41	0	158	0	144068
2	118	0	0	260981	0	105	0	114	0	25	0	90	0	162627
2	119	0	0	222366	0	76	0	67	0	34	0	131	0	95329
2	120	0	0	201345	0	60	0	88	0	28	0	110	0	62853
2	121	0	0	163043	0	110	0	67	0	31	0	121	0	125976
2	122	0	0	204250	0	128	0	75	0	40	0	151	0	79146
2	123	0	0	127260	0	57	0	119	0	25	0	92	0	99971
2	124	0	0	216092	0	59	0	86	0	42	0	162	0	77826
2	125	0	0	213198	0	67	0	67	0	42	0	163	0	84892
2	126	0	0	191971	0	99	0	75	0	37	0	140	0	238712
2	127	0	0	154651	0	53	0	112	0	34	0	132	0	67486
2	128	0	0	155473	0	56	0	66	0	33	0	122	0	68007
2	129	0	0	145869	0	65	0	30	0	26	0	99	0	38692
2	130	0	0	223666	0	85	0	100	0	40	0	106	0	93587
2	131	0	0	80953	0	25	0	49	0	8	0	28	0	56622
2	132	0	0	130789	0	46	0	26	0	27	0	101	0	15986
2	133	0	0	197389	0	67	0	134	0	34	0	127	0	112215
2	134	0	0	156583	0	56	0	37	0	28	0	102	0	59591
2	135	0	0	178489	0	35	0	78	0	32	0	124	0	143372
2	136	0	0	302789	0	129	0	127	0	52	0	199	0	135062
2	137	0	0	342025	0	98	0	139	0	27	0	102	0	175681
2	138	0	0	246440	0	104	0	108	0	45	0	174	0	130307
2	139	0	0	251306	0	56	0	128	0	37	0	141	0	139141
2	140	0	0	181781	0	117	0	83	0	35	0	131	0	95216
2	141	0	0	260464	0	80	0	157	0	36	0	137	0	94588
2	142	0	0	109632	0	76	0	83	0	36	0	142	0	151244
2	143	0	0	23623	0	11	0	12	0	1	0	0	0	1168
2	144	0	0	61857	0	25	0	23	0	11	0	32	0	25162
2	145	0	0	144889	0	43	0	83	0	40	0	149	0	45635
2	146	0	0	21054	0	16	0	4	0	0	0	0	0	855
2	147	0	0	223718	0	44	0	71	0	27	0	99	0	100174
2	148	0	0	31414	0	19	0	18	0	8	0	25	0	14116
2	149	0	0	112933	0	45	0	29	0	28	0	103	0	117129
2	150	0	0	172783	0	54	0	46	0	43	0	165	0	97392
2	151	0	0	348678	0	69	0	129	0	41	0	159	0	126893
2	152	0	0	266701	0	89	0	139	0	43	0	167	0	118850
2	153	0	0	358933	0	99	0	136	0	47	0	178	0	234853
2	154	0	0	382487	0	153	0	97	0	42	0	158	0	139537
2	155	0	0	111853	0	39	0	49	0	35	0	132	0	144253
2	156	0	0	173710	0	103	0	29	0	27	0	107	0	106221
2	157	0	0	212961	0	75	0	114	0	35	0	129	0	113864
2	158	0	0	173260	0	63	0	41	0	21	0	79	0	37238
2	159	0	0	127096	0	51	0	142	0	47	0	180	0	135096
2	160	0	0	98	0	1	0	0	0	0	0	0	0	0
2	161	0	0	0	0	0	0	0	0	0	0	0	0	0
2	162	0	0	0	0	0	0	0	0	0	0	0	0	0
2	163	0	0	46660	0	20	0	12	0	5	0	15	0	6179
2	164	0	0	165838	0	56	0	46	0	28	0	101	0	87656

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Total_Time_spent_in_RFC[t] = + 41463.1112030523 -42867.955154984Pop[t] + 254.842499150433t -259.857604047942pop_t[t] + 0.564692768987317Total_Time_spent_in_RFC_p[t] + 770.117740316727Number_of_Logins[t] -476.060634317905Number_of_Logins_p[t] + 978.843745312095Total_Number_of_Blogged_Computations[t] -498.535912155065Total_Number_of_Blogged_Computations_p[t] + 1335.71894278921Total_Number_of_Reviewed_Compendiums[t] + 242.091542645528Total_Number_of_Reviewed_Compendiums_p[t] -117.997591741228Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -128.469136507496Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p[t] + 0.284158479960969`Compendium_Writing:_total_number_of_characters`[t] -0.184815303183236`Compendium_Writing:_total_number_of_characters_p`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Time_spent_in_RFC[t] =  +  41463.1112030523 -42867.955154984Pop[t] +  254.842499150433t -259.857604047942pop_t[t] +  0.564692768987317Total_Time_spent_in_RFC_p[t] +  770.117740316727Number_of_Logins[t] -476.060634317905Number_of_Logins_p[t] +  978.843745312095Total_Number_of_Blogged_Computations[t] -498.535912155065Total_Number_of_Blogged_Computations_p[t] +  1335.71894278921Total_Number_of_Reviewed_Compendiums[t] +  242.091542645528Total_Number_of_Reviewed_Compendiums_p[t] -117.997591741228Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -128.469136507496Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p[t] +  0.284158479960969`Compendium_Writing:_total_number_of_characters`[t] -0.184815303183236`Compendium_Writing:_total_number_of_characters_p`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC[t] =  +  41463.1112030523 -42867.955154984Pop[t] +  254.842499150433t -259.857604047942pop_t[t] +  0.564692768987317Total_Time_spent_in_RFC_p[t] +  770.117740316727Number_of_Logins[t] -476.060634317905Number_of_Logins_p[t] +  978.843745312095Total_Number_of_Blogged_Computations[t] -498.535912155065Total_Number_of_Blogged_Computations_p[t] +  1335.71894278921Total_Number_of_Reviewed_Compendiums[t] +  242.091542645528Total_Number_of_Reviewed_Compendiums_p[t] -117.997591741228Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -128.469136507496Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p[t] +  0.284158479960969`Compendium_Writing:_total_number_of_characters`[t] -0.184815303183236`Compendium_Writing:_total_number_of_characters_p`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Total_Time_spent_in_RFC[t] = + 41463.1112030523 -42867.955154984Pop[t] + 254.842499150433t -259.857604047942pop_t[t] + 0.564692768987317Total_Time_spent_in_RFC_p[t] + 770.117740316727Number_of_Logins[t] -476.060634317905Number_of_Logins_p[t] + 978.843745312095Total_Number_of_Blogged_Computations[t] -498.535912155065Total_Number_of_Blogged_Computations_p[t] + 1335.71894278921Total_Number_of_Reviewed_Compendiums[t] + 242.091542645528Total_Number_of_Reviewed_Compendiums_p[t] -117.997591741228Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -128.469136507496Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p[t] + 0.284158479960969`Compendium_Writing:_total_number_of_characters`[t] -0.184815303183236`Compendium_Writing:_total_number_of_characters_p`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)41463.111203052327488.4837161.50840.1335740.066787
Pop-42867.95515498421733.781269-1.97240.0504140.025207
t254.842499150433123.8120422.05830.0413030.020652
pop_t-259.857604047942166.522762-1.56050.1207650.060382
Total_Time_spent_in_RFC_p0.5646927689873170.04061713.902800
Number_of_Logins770.117740316727123.716286.224900
Number_of_Logins_p-476.060634317905110.300884-4.3162.9e-051.4e-05
Total_Number_of_Blogged_Computations978.843745312095152.1540366.433200
Total_Number_of_Blogged_Computations_p-498.535912155065111.327681-4.47811.5e-057e-06
Total_Number_of_Reviewed_Compendiums1335.718942789212652.6541910.50350.6153280.307664
Total_Number_of_Reviewed_Compendiums_p242.091542645528816.7554240.29640.7673320.383666
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews-117.997591741228713.251521-0.16540.8688250.434412
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p-128.469136507496213.650575-0.60130.5485510.274275
`Compendium_Writing:_total_number_of_characters`0.2841584799609690.1176132.4160.01690.00845
`Compendium_Writing:_total_number_of_characters_p`-0.1848153031832360.079076-2.33720.0207610.010381

\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) & 41463.1112030523 & 27488.483716 & 1.5084 & 0.133574 & 0.066787 \tabularnewline
Pop & -42867.955154984 & 21733.781269 & -1.9724 & 0.050414 & 0.025207 \tabularnewline
t & 254.842499150433 & 123.812042 & 2.0583 & 0.041303 & 0.020652 \tabularnewline
pop_t & -259.857604047942 & 166.522762 & -1.5605 & 0.120765 & 0.060382 \tabularnewline
Total_Time_spent_in_RFC_p & 0.564692768987317 & 0.040617 & 13.9028 & 0 & 0 \tabularnewline
Number_of_Logins & 770.117740316727 & 123.71628 & 6.2249 & 0 & 0 \tabularnewline
Number_of_Logins_p & -476.060634317905 & 110.300884 & -4.316 & 2.9e-05 & 1.4e-05 \tabularnewline
Total_Number_of_Blogged_Computations & 978.843745312095 & 152.154036 & 6.4332 & 0 & 0 \tabularnewline
Total_Number_of_Blogged_Computations_p & -498.535912155065 & 111.327681 & -4.4781 & 1.5e-05 & 7e-06 \tabularnewline
Total_Number_of_Reviewed_Compendiums & 1335.71894278921 & 2652.654191 & 0.5035 & 0.615328 & 0.307664 \tabularnewline
Total_Number_of_Reviewed_Compendiums_p & 242.091542645528 & 816.755424 & 0.2964 & 0.767332 & 0.383666 \tabularnewline
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews & -117.997591741228 & 713.251521 & -0.1654 & 0.868825 & 0.434412 \tabularnewline
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p & -128.469136507496 & 213.650575 & -0.6013 & 0.548551 & 0.274275 \tabularnewline
`Compendium_Writing:_total_number_of_characters` & 0.284158479960969 & 0.117613 & 2.416 & 0.0169 & 0.00845 \tabularnewline
`Compendium_Writing:_total_number_of_characters_p` & -0.184815303183236 & 0.079076 & -2.3372 & 0.020761 & 0.010381 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&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]41463.1112030523[/C][C]27488.483716[/C][C]1.5084[/C][C]0.133574[/C][C]0.066787[/C][/ROW]
[ROW][C]Pop[/C][C]-42867.955154984[/C][C]21733.781269[/C][C]-1.9724[/C][C]0.050414[/C][C]0.025207[/C][/ROW]
[ROW][C]t[/C][C]254.842499150433[/C][C]123.812042[/C][C]2.0583[/C][C]0.041303[/C][C]0.020652[/C][/ROW]
[ROW][C]pop_t[/C][C]-259.857604047942[/C][C]166.522762[/C][C]-1.5605[/C][C]0.120765[/C][C]0.060382[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC_p[/C][C]0.564692768987317[/C][C]0.040617[/C][C]13.9028[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Number_of_Logins[/C][C]770.117740316727[/C][C]123.71628[/C][C]6.2249[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Number_of_Logins_p[/C][C]-476.060634317905[/C][C]110.300884[/C][C]-4.316[/C][C]2.9e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Total_Number_of_Blogged_Computations[/C][C]978.843745312095[/C][C]152.154036[/C][C]6.4332[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Blogged_Computations_p[/C][C]-498.535912155065[/C][C]111.327681[/C][C]-4.4781[/C][C]1.5e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]Total_Number_of_Reviewed_Compendiums[/C][C]1335.71894278921[/C][C]2652.654191[/C][C]0.5035[/C][C]0.615328[/C][C]0.307664[/C][/ROW]
[ROW][C]Total_Number_of_Reviewed_Compendiums_p[/C][C]242.091542645528[/C][C]816.755424[/C][C]0.2964[/C][C]0.767332[/C][C]0.383666[/C][/ROW]
[ROW][C]Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[/C][C]-117.997591741228[/C][C]713.251521[/C][C]-0.1654[/C][C]0.868825[/C][C]0.434412[/C][/ROW]
[ROW][C]Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p[/C][C]-128.469136507496[/C][C]213.650575[/C][C]-0.6013[/C][C]0.548551[/C][C]0.274275[/C][/ROW]
[ROW][C]`Compendium_Writing:_total_number_of_characters`[/C][C]0.284158479960969[/C][C]0.117613[/C][C]2.416[/C][C]0.0169[/C][C]0.00845[/C][/ROW]
[ROW][C]`Compendium_Writing:_total_number_of_characters_p`[/C][C]-0.184815303183236[/C][C]0.079076[/C][C]-2.3372[/C][C]0.020761[/C][C]0.010381[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)41463.111203052327488.4837161.50840.1335740.066787
Pop-42867.95515498421733.781269-1.97240.0504140.025207
t254.842499150433123.8120422.05830.0413030.020652
pop_t-259.857604047942166.522762-1.56050.1207650.060382
Total_Time_spent_in_RFC_p0.5646927689873170.04061713.902800
Number_of_Logins770.117740316727123.716286.224900
Number_of_Logins_p-476.060634317905110.300884-4.3162.9e-051.4e-05
Total_Number_of_Blogged_Computations978.843745312095152.1540366.433200
Total_Number_of_Blogged_Computations_p-498.535912155065111.327681-4.47811.5e-057e-06
Total_Number_of_Reviewed_Compendiums1335.718942789212652.6541910.50350.6153280.307664
Total_Number_of_Reviewed_Compendiums_p242.091542645528816.7554240.29640.7673320.383666
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews-117.997591741228713.251521-0.16540.8688250.434412
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews_p-128.469136507496213.650575-0.60130.5485510.274275
`Compendium_Writing:_total_number_of_characters`0.2841584799609690.1176132.4160.01690.00845
`Compendium_Writing:_total_number_of_characters_p`-0.1848153031832360.079076-2.33720.0207610.010381






Multiple Linear Regression - Regression Statistics
Multiple R0.98219022335076
R-squared0.964697634845815
Adjusted R-squared0.961380634093073
F-TEST (value)290.834312910025
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23319.6445445457
Sum Squared Residuals81027067430.9099

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98219022335076 \tabularnewline
R-squared & 0.964697634845815 \tabularnewline
Adjusted R-squared & 0.961380634093073 \tabularnewline
F-TEST (value) & 290.834312910025 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 23319.6445445457 \tabularnewline
Sum Squared Residuals & 81027067430.9099 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98219022335076[/C][/ROW]
[ROW][C]R-squared[/C][C]0.964697634845815[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.961380634093073[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]290.834312910025[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]23319.6445445457[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]81027067430.9099[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.98219022335076
R-squared0.964697634845815
Adjusted R-squared0.961380634093073
F-TEST (value)290.834312910025
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23319.6445445457
Sum Squared Residuals81027067430.9099






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1130631145597.999278951-14966.9992789508
2189326216734.152111054-27408.1521110544
36529574876.5152596933-9581.51525969335
43318637535.4204851564-4349.42048515641
5261949248348.4737598113600.5262401897
6190794218006.382594285-27212.3825942846
7150733168799.349422721-18066.3494227213
8223226239934.757857842-16708.7578578422
9384138312980.615546771157.3844532998
10156540146607.6317813219932.36821867876
11191441208233.553223857-16792.553223857
12259667247417.40642305612249.5935769442
13228871236309.598281623-7438.59828162256
1487485103669.124021193-16184.1240211932
15322865290963.51670330631901.4832966941
16340093298603.44599930241489.5540006984
17155363165818.875479314-10455.8754793138
18174198182064.479550875-7866.47955087455
19392666336449.42888480556216.5711151954
20302674302823.299554921-149.299554920533
21164733186267.951873261-21534.951873261
222418826988.178750398-2800.178750398
23340411349721.645154138-9310.64515413804
246502965402.6725957346-373.672595734559
25101097101209.76804476-112.768044760377
26282220291008.50230001-8788.50230000966
27273495284867.204124493-11372.2041244934
28214872205907.6981084848964.30189151614
29184474183324.0408549371149.95914506346
30205675184635.78792239721039.2120776033
31197760215225.580022809-17465.5800228092
327356676800.9737075558-3234.97370755584
33177949183229.598001925-5280.59800192473
34148698176094.469282235-27396.4692822346
35300103280144.8720436519958.12795635
36251437236442.72738168514994.2726183153
37132672133568.446440373-896.446440373387
38376465341805.07220515334659.9277948469
39135042152837.759747129-17795.7597471295
40300074270417.22094618229656.7790538178
41271757267279.9919429824477.00805701847
42150949215465.462128757-64516.4621287572
43216802204090.37779512812711.6222048722
44222599216140.9092164726458.09078352848
45261601228137.07787013733463.9221298631
46200657201908.746216324-1251.74621632435
47259084273293.645443316-14209.6454433162
48159965189153.838196517-29188.8381965173
494328749979.5986158956-6692.59861589556
50172212177559.874317809-5347.87431780866
51227681226979.849585803701.150414197279
52106288121156.233588741-14868.2335887407
53268905239665.80955336429239.1904466361
54266568274480.005481018-7912.00548101837
55152474181750.424439442-29276.4244394418
56330910302961.79892705527948.2010729447
57259747253479.3209352326267.67906476783
58190495191988.617349723-1493.6173497234
59154984164823.078517348-9839.07851734767
603821444395.7060734048-6181.70607340484
61158671156839.4177118541831.58228814567
62299775281141.74225065118633.2577493487
63172464165864.0048900886599.99510991152
6494381110007.738077599-15626.7380775994
65243875281978.206678811-38103.2066788112
66334926316214.79376320218711.2062367979
67147979159098.47326554-11119.4732655401
68216638213765.3144927232872.68550727681
69192853197547.520136629-4694.52013662927
70336678296084.95507431340593.044925687
71271773276426.202806127-4653.20280612703
72203606214002.486021063-10396.4860210628
73230177257604.836945523-27427.8369455229
741-1775.397021578421776.39702157842
751468811973.37691798942714.62308201061
76455-940.9425022555721395.94250225557
770-1791.007029039971791.00702903997
78195765187889.1424667427875.85753325776
79306514290071.05527355416442.9447264464
80203-515.191287632785718.191287632785
8171997249.91634002562-50.9163400256177
821754710544.83187835637002.16812164372
83105044117505.772186329-12461.7721863294
84969-690.8112581761611659.81125817616
8507959.12770506532-7959.12770506532
8601635.68374279166-1635.68374279166
870-4260.779576592324260.77957659232
88070251.9144942237-70251.9144942237
890-9160.756482815659160.75648281565
9009464.36388754462-9464.36388754462
910-8431.68770586058431.6877058605
92036795.1284743129-36795.1284743129
9301504.60520200266-1504.60520200266
940-21385.019349971921385.0193499719
950-386.702627048674386.702627048674
960-4980.223696900344980.22369690034
9703456.16879536915-3456.16879536915
980-33707.419071785533707.4190717855
990-49254.010962083149254.0109620831
1000-8014.076869525868014.07686952586
1010-134.777923157212134.777923157212
10201526.06656871648-1526.06656871648
10303478.28237896363-3478.28237896363
104014829.6730241311-14829.6730241311
105039130.8869789514-39130.8869789514
106065.9332904120834-65.9332904120834
10707846.30671411447-7846.30671411447
1080-10853.364660747510853.3646607475
109031645.6934224209-31645.6934224209
1100-8045.046296789068045.04629678906
1110-11653.185562801811653.1855628018
1120-11542.822069899611542.8220698996
1130-13708.569005462413708.5690054624
1140-7596.959193225417596.95919322541
115034072.3821396822-34072.3821396822
11604826.28436151237-4826.28436151237
11709803.94499044076-9803.94499044076
1180-9212.653283459699212.65328345969
119015822.8137683806-15822.8137683806
12008602.32996144177-8602.32996144177
1210-38458.449794280138458.4497942801
1220-20512.040641776420512.0406417764
1230-51768.642299742651768.6422997426
124013364.0036450125-13364.0036450125
125016213.9484583164-16213.9484583164
1260-41424.12755444141424.127554441
1270-26843.995198034926843.9951980349
1280-3674.194137126633674.19413712663
129011498.0960913611-11498.0960913611
13003610.17697544637-3610.17697544637
1310-13629.649121811413629.6491218114
132018967.9218768532-18967.9218768532
1330-26437.997603559926437.9976035599
134015853.5418923612-15853.5418923612
1350693.879362688639-693.879362688639
1360-1295.465490217651295.46549021765
137028793.3160860025-28793.3160860025
1380-8836.070761884258836.07076188425
13907716.84973057906-7716.84973057906
1400-28975.634708420528975.6347084205
1410-3979.144362753663979.14436275366
1420-61215.383058438661215.3830584386
1430-5683.455101446685683.45510144668
1440-2111.448513354712111.44851335471
1450-5244.240103402715244.24010340271
1460-4945.883554622014945.88355462201
147038482.6064436147-38482.6064436147
1480-10719.497949773210719.4979497732
1490-6510.077763005746510.07776300574
150014095.957059684-14095.957059684
151059992.4394107649-59992.4394107649
1520391.790455467163-391.790455467163
153027580.4538709767-27580.4538709767
154053846.4766268707-53846.4766268707
1550-19749.139839544119749.1398395441
15603242.63259979818-3242.63259979818
1570-5685.755822039545685.75582203954
158031451.9009047361-31451.9009047361
1590-63767.791716869263767.7917168692
1600-3918.719985803623918.71998580362
1610-3243.156743696053243.15674369605
1620-2988.314244545642988.31424454564
16306252.89613058012-6252.89613058012
164019179.8528871262-19179.8528871262

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 130631 & 145597.999278951 & -14966.9992789508 \tabularnewline
2 & 189326 & 216734.152111054 & -27408.1521110544 \tabularnewline
3 & 65295 & 74876.5152596933 & -9581.51525969335 \tabularnewline
4 & 33186 & 37535.4204851564 & -4349.42048515641 \tabularnewline
5 & 261949 & 248348.47375981 & 13600.5262401897 \tabularnewline
6 & 190794 & 218006.382594285 & -27212.3825942846 \tabularnewline
7 & 150733 & 168799.349422721 & -18066.3494227213 \tabularnewline
8 & 223226 & 239934.757857842 & -16708.7578578422 \tabularnewline
9 & 384138 & 312980.6155467 & 71157.3844532998 \tabularnewline
10 & 156540 & 146607.631781321 & 9932.36821867876 \tabularnewline
11 & 191441 & 208233.553223857 & -16792.553223857 \tabularnewline
12 & 259667 & 247417.406423056 & 12249.5935769442 \tabularnewline
13 & 228871 & 236309.598281623 & -7438.59828162256 \tabularnewline
14 & 87485 & 103669.124021193 & -16184.1240211932 \tabularnewline
15 & 322865 & 290963.516703306 & 31901.4832966941 \tabularnewline
16 & 340093 & 298603.445999302 & 41489.5540006984 \tabularnewline
17 & 155363 & 165818.875479314 & -10455.8754793138 \tabularnewline
18 & 174198 & 182064.479550875 & -7866.47955087455 \tabularnewline
19 & 392666 & 336449.428884805 & 56216.5711151954 \tabularnewline
20 & 302674 & 302823.299554921 & -149.299554920533 \tabularnewline
21 & 164733 & 186267.951873261 & -21534.951873261 \tabularnewline
22 & 24188 & 26988.178750398 & -2800.178750398 \tabularnewline
23 & 340411 & 349721.645154138 & -9310.64515413804 \tabularnewline
24 & 65029 & 65402.6725957346 & -373.672595734559 \tabularnewline
25 & 101097 & 101209.76804476 & -112.768044760377 \tabularnewline
26 & 282220 & 291008.50230001 & -8788.50230000966 \tabularnewline
27 & 273495 & 284867.204124493 & -11372.2041244934 \tabularnewline
28 & 214872 & 205907.698108484 & 8964.30189151614 \tabularnewline
29 & 184474 & 183324.040854937 & 1149.95914506346 \tabularnewline
30 & 205675 & 184635.787922397 & 21039.2120776033 \tabularnewline
31 & 197760 & 215225.580022809 & -17465.5800228092 \tabularnewline
32 & 73566 & 76800.9737075558 & -3234.97370755584 \tabularnewline
33 & 177949 & 183229.598001925 & -5280.59800192473 \tabularnewline
34 & 148698 & 176094.469282235 & -27396.4692822346 \tabularnewline
35 & 300103 & 280144.87204365 & 19958.12795635 \tabularnewline
36 & 251437 & 236442.727381685 & 14994.2726183153 \tabularnewline
37 & 132672 & 133568.446440373 & -896.446440373387 \tabularnewline
38 & 376465 & 341805.072205153 & 34659.9277948469 \tabularnewline
39 & 135042 & 152837.759747129 & -17795.7597471295 \tabularnewline
40 & 300074 & 270417.220946182 & 29656.7790538178 \tabularnewline
41 & 271757 & 267279.991942982 & 4477.00805701847 \tabularnewline
42 & 150949 & 215465.462128757 & -64516.4621287572 \tabularnewline
43 & 216802 & 204090.377795128 & 12711.6222048722 \tabularnewline
44 & 222599 & 216140.909216472 & 6458.09078352848 \tabularnewline
45 & 261601 & 228137.077870137 & 33463.9221298631 \tabularnewline
46 & 200657 & 201908.746216324 & -1251.74621632435 \tabularnewline
47 & 259084 & 273293.645443316 & -14209.6454433162 \tabularnewline
48 & 159965 & 189153.838196517 & -29188.8381965173 \tabularnewline
49 & 43287 & 49979.5986158956 & -6692.59861589556 \tabularnewline
50 & 172212 & 177559.874317809 & -5347.87431780866 \tabularnewline
51 & 227681 & 226979.849585803 & 701.150414197279 \tabularnewline
52 & 106288 & 121156.233588741 & -14868.2335887407 \tabularnewline
53 & 268905 & 239665.809553364 & 29239.1904466361 \tabularnewline
54 & 266568 & 274480.005481018 & -7912.00548101837 \tabularnewline
55 & 152474 & 181750.424439442 & -29276.4244394418 \tabularnewline
56 & 330910 & 302961.798927055 & 27948.2010729447 \tabularnewline
57 & 259747 & 253479.320935232 & 6267.67906476783 \tabularnewline
58 & 190495 & 191988.617349723 & -1493.6173497234 \tabularnewline
59 & 154984 & 164823.078517348 & -9839.07851734767 \tabularnewline
60 & 38214 & 44395.7060734048 & -6181.70607340484 \tabularnewline
61 & 158671 & 156839.417711854 & 1831.58228814567 \tabularnewline
62 & 299775 & 281141.742250651 & 18633.2577493487 \tabularnewline
63 & 172464 & 165864.004890088 & 6599.99510991152 \tabularnewline
64 & 94381 & 110007.738077599 & -15626.7380775994 \tabularnewline
65 & 243875 & 281978.206678811 & -38103.2066788112 \tabularnewline
66 & 334926 & 316214.793763202 & 18711.2062367979 \tabularnewline
67 & 147979 & 159098.47326554 & -11119.4732655401 \tabularnewline
68 & 216638 & 213765.314492723 & 2872.68550727681 \tabularnewline
69 & 192853 & 197547.520136629 & -4694.52013662927 \tabularnewline
70 & 336678 & 296084.955074313 & 40593.044925687 \tabularnewline
71 & 271773 & 276426.202806127 & -4653.20280612703 \tabularnewline
72 & 203606 & 214002.486021063 & -10396.4860210628 \tabularnewline
73 & 230177 & 257604.836945523 & -27427.8369455229 \tabularnewline
74 & 1 & -1775.39702157842 & 1776.39702157842 \tabularnewline
75 & 14688 & 11973.3769179894 & 2714.62308201061 \tabularnewline
76 & 455 & -940.942502255572 & 1395.94250225557 \tabularnewline
77 & 0 & -1791.00702903997 & 1791.00702903997 \tabularnewline
78 & 195765 & 187889.142466742 & 7875.85753325776 \tabularnewline
79 & 306514 & 290071.055273554 & 16442.9447264464 \tabularnewline
80 & 203 & -515.191287632785 & 718.191287632785 \tabularnewline
81 & 7199 & 7249.91634002562 & -50.9163400256177 \tabularnewline
82 & 17547 & 10544.8318783563 & 7002.16812164372 \tabularnewline
83 & 105044 & 117505.772186329 & -12461.7721863294 \tabularnewline
84 & 969 & -690.811258176161 & 1659.81125817616 \tabularnewline
85 & 0 & 7959.12770506532 & -7959.12770506532 \tabularnewline
86 & 0 & 1635.68374279166 & -1635.68374279166 \tabularnewline
87 & 0 & -4260.77957659232 & 4260.77957659232 \tabularnewline
88 & 0 & 70251.9144942237 & -70251.9144942237 \tabularnewline
89 & 0 & -9160.75648281565 & 9160.75648281565 \tabularnewline
90 & 0 & 9464.36388754462 & -9464.36388754462 \tabularnewline
91 & 0 & -8431.6877058605 & 8431.6877058605 \tabularnewline
92 & 0 & 36795.1284743129 & -36795.1284743129 \tabularnewline
93 & 0 & 1504.60520200266 & -1504.60520200266 \tabularnewline
94 & 0 & -21385.0193499719 & 21385.0193499719 \tabularnewline
95 & 0 & -386.702627048674 & 386.702627048674 \tabularnewline
96 & 0 & -4980.22369690034 & 4980.22369690034 \tabularnewline
97 & 0 & 3456.16879536915 & -3456.16879536915 \tabularnewline
98 & 0 & -33707.4190717855 & 33707.4190717855 \tabularnewline
99 & 0 & -49254.0109620831 & 49254.0109620831 \tabularnewline
100 & 0 & -8014.07686952586 & 8014.07686952586 \tabularnewline
101 & 0 & -134.777923157212 & 134.777923157212 \tabularnewline
102 & 0 & 1526.06656871648 & -1526.06656871648 \tabularnewline
103 & 0 & 3478.28237896363 & -3478.28237896363 \tabularnewline
104 & 0 & 14829.6730241311 & -14829.6730241311 \tabularnewline
105 & 0 & 39130.8869789514 & -39130.8869789514 \tabularnewline
106 & 0 & 65.9332904120834 & -65.9332904120834 \tabularnewline
107 & 0 & 7846.30671411447 & -7846.30671411447 \tabularnewline
108 & 0 & -10853.3646607475 & 10853.3646607475 \tabularnewline
109 & 0 & 31645.6934224209 & -31645.6934224209 \tabularnewline
110 & 0 & -8045.04629678906 & 8045.04629678906 \tabularnewline
111 & 0 & -11653.1855628018 & 11653.1855628018 \tabularnewline
112 & 0 & -11542.8220698996 & 11542.8220698996 \tabularnewline
113 & 0 & -13708.5690054624 & 13708.5690054624 \tabularnewline
114 & 0 & -7596.95919322541 & 7596.95919322541 \tabularnewline
115 & 0 & 34072.3821396822 & -34072.3821396822 \tabularnewline
116 & 0 & 4826.28436151237 & -4826.28436151237 \tabularnewline
117 & 0 & 9803.94499044076 & -9803.94499044076 \tabularnewline
118 & 0 & -9212.65328345969 & 9212.65328345969 \tabularnewline
119 & 0 & 15822.8137683806 & -15822.8137683806 \tabularnewline
120 & 0 & 8602.32996144177 & -8602.32996144177 \tabularnewline
121 & 0 & -38458.4497942801 & 38458.4497942801 \tabularnewline
122 & 0 & -20512.0406417764 & 20512.0406417764 \tabularnewline
123 & 0 & -51768.6422997426 & 51768.6422997426 \tabularnewline
124 & 0 & 13364.0036450125 & -13364.0036450125 \tabularnewline
125 & 0 & 16213.9484583164 & -16213.9484583164 \tabularnewline
126 & 0 & -41424.127554441 & 41424.127554441 \tabularnewline
127 & 0 & -26843.9951980349 & 26843.9951980349 \tabularnewline
128 & 0 & -3674.19413712663 & 3674.19413712663 \tabularnewline
129 & 0 & 11498.0960913611 & -11498.0960913611 \tabularnewline
130 & 0 & 3610.17697544637 & -3610.17697544637 \tabularnewline
131 & 0 & -13629.6491218114 & 13629.6491218114 \tabularnewline
132 & 0 & 18967.9218768532 & -18967.9218768532 \tabularnewline
133 & 0 & -26437.9976035599 & 26437.9976035599 \tabularnewline
134 & 0 & 15853.5418923612 & -15853.5418923612 \tabularnewline
135 & 0 & 693.879362688639 & -693.879362688639 \tabularnewline
136 & 0 & -1295.46549021765 & 1295.46549021765 \tabularnewline
137 & 0 & 28793.3160860025 & -28793.3160860025 \tabularnewline
138 & 0 & -8836.07076188425 & 8836.07076188425 \tabularnewline
139 & 0 & 7716.84973057906 & -7716.84973057906 \tabularnewline
140 & 0 & -28975.6347084205 & 28975.6347084205 \tabularnewline
141 & 0 & -3979.14436275366 & 3979.14436275366 \tabularnewline
142 & 0 & -61215.3830584386 & 61215.3830584386 \tabularnewline
143 & 0 & -5683.45510144668 & 5683.45510144668 \tabularnewline
144 & 0 & -2111.44851335471 & 2111.44851335471 \tabularnewline
145 & 0 & -5244.24010340271 & 5244.24010340271 \tabularnewline
146 & 0 & -4945.88355462201 & 4945.88355462201 \tabularnewline
147 & 0 & 38482.6064436147 & -38482.6064436147 \tabularnewline
148 & 0 & -10719.4979497732 & 10719.4979497732 \tabularnewline
149 & 0 & -6510.07776300574 & 6510.07776300574 \tabularnewline
150 & 0 & 14095.957059684 & -14095.957059684 \tabularnewline
151 & 0 & 59992.4394107649 & -59992.4394107649 \tabularnewline
152 & 0 & 391.790455467163 & -391.790455467163 \tabularnewline
153 & 0 & 27580.4538709767 & -27580.4538709767 \tabularnewline
154 & 0 & 53846.4766268707 & -53846.4766268707 \tabularnewline
155 & 0 & -19749.1398395441 & 19749.1398395441 \tabularnewline
156 & 0 & 3242.63259979818 & -3242.63259979818 \tabularnewline
157 & 0 & -5685.75582203954 & 5685.75582203954 \tabularnewline
158 & 0 & 31451.9009047361 & -31451.9009047361 \tabularnewline
159 & 0 & -63767.7917168692 & 63767.7917168692 \tabularnewline
160 & 0 & -3918.71998580362 & 3918.71998580362 \tabularnewline
161 & 0 & -3243.15674369605 & 3243.15674369605 \tabularnewline
162 & 0 & -2988.31424454564 & 2988.31424454564 \tabularnewline
163 & 0 & 6252.89613058012 & -6252.89613058012 \tabularnewline
164 & 0 & 19179.8528871262 & -19179.8528871262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&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]130631[/C][C]145597.999278951[/C][C]-14966.9992789508[/C][/ROW]
[ROW][C]2[/C][C]189326[/C][C]216734.152111054[/C][C]-27408.1521110544[/C][/ROW]
[ROW][C]3[/C][C]65295[/C][C]74876.5152596933[/C][C]-9581.51525969335[/C][/ROW]
[ROW][C]4[/C][C]33186[/C][C]37535.4204851564[/C][C]-4349.42048515641[/C][/ROW]
[ROW][C]5[/C][C]261949[/C][C]248348.47375981[/C][C]13600.5262401897[/C][/ROW]
[ROW][C]6[/C][C]190794[/C][C]218006.382594285[/C][C]-27212.3825942846[/C][/ROW]
[ROW][C]7[/C][C]150733[/C][C]168799.349422721[/C][C]-18066.3494227213[/C][/ROW]
[ROW][C]8[/C][C]223226[/C][C]239934.757857842[/C][C]-16708.7578578422[/C][/ROW]
[ROW][C]9[/C][C]384138[/C][C]312980.6155467[/C][C]71157.3844532998[/C][/ROW]
[ROW][C]10[/C][C]156540[/C][C]146607.631781321[/C][C]9932.36821867876[/C][/ROW]
[ROW][C]11[/C][C]191441[/C][C]208233.553223857[/C][C]-16792.553223857[/C][/ROW]
[ROW][C]12[/C][C]259667[/C][C]247417.406423056[/C][C]12249.5935769442[/C][/ROW]
[ROW][C]13[/C][C]228871[/C][C]236309.598281623[/C][C]-7438.59828162256[/C][/ROW]
[ROW][C]14[/C][C]87485[/C][C]103669.124021193[/C][C]-16184.1240211932[/C][/ROW]
[ROW][C]15[/C][C]322865[/C][C]290963.516703306[/C][C]31901.4832966941[/C][/ROW]
[ROW][C]16[/C][C]340093[/C][C]298603.445999302[/C][C]41489.5540006984[/C][/ROW]
[ROW][C]17[/C][C]155363[/C][C]165818.875479314[/C][C]-10455.8754793138[/C][/ROW]
[ROW][C]18[/C][C]174198[/C][C]182064.479550875[/C][C]-7866.47955087455[/C][/ROW]
[ROW][C]19[/C][C]392666[/C][C]336449.428884805[/C][C]56216.5711151954[/C][/ROW]
[ROW][C]20[/C][C]302674[/C][C]302823.299554921[/C][C]-149.299554920533[/C][/ROW]
[ROW][C]21[/C][C]164733[/C][C]186267.951873261[/C][C]-21534.951873261[/C][/ROW]
[ROW][C]22[/C][C]24188[/C][C]26988.178750398[/C][C]-2800.178750398[/C][/ROW]
[ROW][C]23[/C][C]340411[/C][C]349721.645154138[/C][C]-9310.64515413804[/C][/ROW]
[ROW][C]24[/C][C]65029[/C][C]65402.6725957346[/C][C]-373.672595734559[/C][/ROW]
[ROW][C]25[/C][C]101097[/C][C]101209.76804476[/C][C]-112.768044760377[/C][/ROW]
[ROW][C]26[/C][C]282220[/C][C]291008.50230001[/C][C]-8788.50230000966[/C][/ROW]
[ROW][C]27[/C][C]273495[/C][C]284867.204124493[/C][C]-11372.2041244934[/C][/ROW]
[ROW][C]28[/C][C]214872[/C][C]205907.698108484[/C][C]8964.30189151614[/C][/ROW]
[ROW][C]29[/C][C]184474[/C][C]183324.040854937[/C][C]1149.95914506346[/C][/ROW]
[ROW][C]30[/C][C]205675[/C][C]184635.787922397[/C][C]21039.2120776033[/C][/ROW]
[ROW][C]31[/C][C]197760[/C][C]215225.580022809[/C][C]-17465.5800228092[/C][/ROW]
[ROW][C]32[/C][C]73566[/C][C]76800.9737075558[/C][C]-3234.97370755584[/C][/ROW]
[ROW][C]33[/C][C]177949[/C][C]183229.598001925[/C][C]-5280.59800192473[/C][/ROW]
[ROW][C]34[/C][C]148698[/C][C]176094.469282235[/C][C]-27396.4692822346[/C][/ROW]
[ROW][C]35[/C][C]300103[/C][C]280144.87204365[/C][C]19958.12795635[/C][/ROW]
[ROW][C]36[/C][C]251437[/C][C]236442.727381685[/C][C]14994.2726183153[/C][/ROW]
[ROW][C]37[/C][C]132672[/C][C]133568.446440373[/C][C]-896.446440373387[/C][/ROW]
[ROW][C]38[/C][C]376465[/C][C]341805.072205153[/C][C]34659.9277948469[/C][/ROW]
[ROW][C]39[/C][C]135042[/C][C]152837.759747129[/C][C]-17795.7597471295[/C][/ROW]
[ROW][C]40[/C][C]300074[/C][C]270417.220946182[/C][C]29656.7790538178[/C][/ROW]
[ROW][C]41[/C][C]271757[/C][C]267279.991942982[/C][C]4477.00805701847[/C][/ROW]
[ROW][C]42[/C][C]150949[/C][C]215465.462128757[/C][C]-64516.4621287572[/C][/ROW]
[ROW][C]43[/C][C]216802[/C][C]204090.377795128[/C][C]12711.6222048722[/C][/ROW]
[ROW][C]44[/C][C]222599[/C][C]216140.909216472[/C][C]6458.09078352848[/C][/ROW]
[ROW][C]45[/C][C]261601[/C][C]228137.077870137[/C][C]33463.9221298631[/C][/ROW]
[ROW][C]46[/C][C]200657[/C][C]201908.746216324[/C][C]-1251.74621632435[/C][/ROW]
[ROW][C]47[/C][C]259084[/C][C]273293.645443316[/C][C]-14209.6454433162[/C][/ROW]
[ROW][C]48[/C][C]159965[/C][C]189153.838196517[/C][C]-29188.8381965173[/C][/ROW]
[ROW][C]49[/C][C]43287[/C][C]49979.5986158956[/C][C]-6692.59861589556[/C][/ROW]
[ROW][C]50[/C][C]172212[/C][C]177559.874317809[/C][C]-5347.87431780866[/C][/ROW]
[ROW][C]51[/C][C]227681[/C][C]226979.849585803[/C][C]701.150414197279[/C][/ROW]
[ROW][C]52[/C][C]106288[/C][C]121156.233588741[/C][C]-14868.2335887407[/C][/ROW]
[ROW][C]53[/C][C]268905[/C][C]239665.809553364[/C][C]29239.1904466361[/C][/ROW]
[ROW][C]54[/C][C]266568[/C][C]274480.005481018[/C][C]-7912.00548101837[/C][/ROW]
[ROW][C]55[/C][C]152474[/C][C]181750.424439442[/C][C]-29276.4244394418[/C][/ROW]
[ROW][C]56[/C][C]330910[/C][C]302961.798927055[/C][C]27948.2010729447[/C][/ROW]
[ROW][C]57[/C][C]259747[/C][C]253479.320935232[/C][C]6267.67906476783[/C][/ROW]
[ROW][C]58[/C][C]190495[/C][C]191988.617349723[/C][C]-1493.6173497234[/C][/ROW]
[ROW][C]59[/C][C]154984[/C][C]164823.078517348[/C][C]-9839.07851734767[/C][/ROW]
[ROW][C]60[/C][C]38214[/C][C]44395.7060734048[/C][C]-6181.70607340484[/C][/ROW]
[ROW][C]61[/C][C]158671[/C][C]156839.417711854[/C][C]1831.58228814567[/C][/ROW]
[ROW][C]62[/C][C]299775[/C][C]281141.742250651[/C][C]18633.2577493487[/C][/ROW]
[ROW][C]63[/C][C]172464[/C][C]165864.004890088[/C][C]6599.99510991152[/C][/ROW]
[ROW][C]64[/C][C]94381[/C][C]110007.738077599[/C][C]-15626.7380775994[/C][/ROW]
[ROW][C]65[/C][C]243875[/C][C]281978.206678811[/C][C]-38103.2066788112[/C][/ROW]
[ROW][C]66[/C][C]334926[/C][C]316214.793763202[/C][C]18711.2062367979[/C][/ROW]
[ROW][C]67[/C][C]147979[/C][C]159098.47326554[/C][C]-11119.4732655401[/C][/ROW]
[ROW][C]68[/C][C]216638[/C][C]213765.314492723[/C][C]2872.68550727681[/C][/ROW]
[ROW][C]69[/C][C]192853[/C][C]197547.520136629[/C][C]-4694.52013662927[/C][/ROW]
[ROW][C]70[/C][C]336678[/C][C]296084.955074313[/C][C]40593.044925687[/C][/ROW]
[ROW][C]71[/C][C]271773[/C][C]276426.202806127[/C][C]-4653.20280612703[/C][/ROW]
[ROW][C]72[/C][C]203606[/C][C]214002.486021063[/C][C]-10396.4860210628[/C][/ROW]
[ROW][C]73[/C][C]230177[/C][C]257604.836945523[/C][C]-27427.8369455229[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]-1775.39702157842[/C][C]1776.39702157842[/C][/ROW]
[ROW][C]75[/C][C]14688[/C][C]11973.3769179894[/C][C]2714.62308201061[/C][/ROW]
[ROW][C]76[/C][C]455[/C][C]-940.942502255572[/C][C]1395.94250225557[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-1791.00702903997[/C][C]1791.00702903997[/C][/ROW]
[ROW][C]78[/C][C]195765[/C][C]187889.142466742[/C][C]7875.85753325776[/C][/ROW]
[ROW][C]79[/C][C]306514[/C][C]290071.055273554[/C][C]16442.9447264464[/C][/ROW]
[ROW][C]80[/C][C]203[/C][C]-515.191287632785[/C][C]718.191287632785[/C][/ROW]
[ROW][C]81[/C][C]7199[/C][C]7249.91634002562[/C][C]-50.9163400256177[/C][/ROW]
[ROW][C]82[/C][C]17547[/C][C]10544.8318783563[/C][C]7002.16812164372[/C][/ROW]
[ROW][C]83[/C][C]105044[/C][C]117505.772186329[/C][C]-12461.7721863294[/C][/ROW]
[ROW][C]84[/C][C]969[/C][C]-690.811258176161[/C][C]1659.81125817616[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]7959.12770506532[/C][C]-7959.12770506532[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]1635.68374279166[/C][C]-1635.68374279166[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]-4260.77957659232[/C][C]4260.77957659232[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]70251.9144942237[/C][C]-70251.9144942237[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]-9160.75648281565[/C][C]9160.75648281565[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]9464.36388754462[/C][C]-9464.36388754462[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]-8431.6877058605[/C][C]8431.6877058605[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]36795.1284743129[/C][C]-36795.1284743129[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]1504.60520200266[/C][C]-1504.60520200266[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]-21385.0193499719[/C][C]21385.0193499719[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-386.702627048674[/C][C]386.702627048674[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-4980.22369690034[/C][C]4980.22369690034[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]3456.16879536915[/C][C]-3456.16879536915[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]-33707.4190717855[/C][C]33707.4190717855[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]-49254.0109620831[/C][C]49254.0109620831[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-8014.07686952586[/C][C]8014.07686952586[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]-134.777923157212[/C][C]134.777923157212[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]1526.06656871648[/C][C]-1526.06656871648[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]3478.28237896363[/C][C]-3478.28237896363[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]14829.6730241311[/C][C]-14829.6730241311[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]39130.8869789514[/C][C]-39130.8869789514[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]65.9332904120834[/C][C]-65.9332904120834[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]7846.30671411447[/C][C]-7846.30671411447[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]-10853.3646607475[/C][C]10853.3646607475[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]31645.6934224209[/C][C]-31645.6934224209[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]-8045.04629678906[/C][C]8045.04629678906[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]-11653.1855628018[/C][C]11653.1855628018[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]-11542.8220698996[/C][C]11542.8220698996[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]-13708.5690054624[/C][C]13708.5690054624[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]-7596.95919322541[/C][C]7596.95919322541[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]34072.3821396822[/C][C]-34072.3821396822[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]4826.28436151237[/C][C]-4826.28436151237[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]9803.94499044076[/C][C]-9803.94499044076[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]-9212.65328345969[/C][C]9212.65328345969[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]15822.8137683806[/C][C]-15822.8137683806[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]8602.32996144177[/C][C]-8602.32996144177[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]-38458.4497942801[/C][C]38458.4497942801[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-20512.0406417764[/C][C]20512.0406417764[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]-51768.6422997426[/C][C]51768.6422997426[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]13364.0036450125[/C][C]-13364.0036450125[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]16213.9484583164[/C][C]-16213.9484583164[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]-41424.127554441[/C][C]41424.127554441[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]-26843.9951980349[/C][C]26843.9951980349[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]-3674.19413712663[/C][C]3674.19413712663[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]11498.0960913611[/C][C]-11498.0960913611[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]3610.17697544637[/C][C]-3610.17697544637[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-13629.6491218114[/C][C]13629.6491218114[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]18967.9218768532[/C][C]-18967.9218768532[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]-26437.9976035599[/C][C]26437.9976035599[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]15853.5418923612[/C][C]-15853.5418923612[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]693.879362688639[/C][C]-693.879362688639[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]-1295.46549021765[/C][C]1295.46549021765[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]28793.3160860025[/C][C]-28793.3160860025[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]-8836.07076188425[/C][C]8836.07076188425[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]7716.84973057906[/C][C]-7716.84973057906[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-28975.6347084205[/C][C]28975.6347084205[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]-3979.14436275366[/C][C]3979.14436275366[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]-61215.3830584386[/C][C]61215.3830584386[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-5683.45510144668[/C][C]5683.45510144668[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]-2111.44851335471[/C][C]2111.44851335471[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]-5244.24010340271[/C][C]5244.24010340271[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-4945.88355462201[/C][C]4945.88355462201[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]38482.6064436147[/C][C]-38482.6064436147[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-10719.4979497732[/C][C]10719.4979497732[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-6510.07776300574[/C][C]6510.07776300574[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]14095.957059684[/C][C]-14095.957059684[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]59992.4394107649[/C][C]-59992.4394107649[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]391.790455467163[/C][C]-391.790455467163[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]27580.4538709767[/C][C]-27580.4538709767[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]53846.4766268707[/C][C]-53846.4766268707[/C][/ROW]
[ROW][C]155[/C][C]0[/C][C]-19749.1398395441[/C][C]19749.1398395441[/C][/ROW]
[ROW][C]156[/C][C]0[/C][C]3242.63259979818[/C][C]-3242.63259979818[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-5685.75582203954[/C][C]5685.75582203954[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]31451.9009047361[/C][C]-31451.9009047361[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]-63767.7917168692[/C][C]63767.7917168692[/C][/ROW]
[ROW][C]160[/C][C]0[/C][C]-3918.71998580362[/C][C]3918.71998580362[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]-3243.15674369605[/C][C]3243.15674369605[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]-2988.31424454564[/C][C]2988.31424454564[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]6252.89613058012[/C][C]-6252.89613058012[/C][/ROW]
[ROW][C]164[/C][C]0[/C][C]19179.8528871262[/C][C]-19179.8528871262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1130631145597.999278951-14966.9992789508
2189326216734.152111054-27408.1521110544
36529574876.5152596933-9581.51525969335
43318637535.4204851564-4349.42048515641
5261949248348.4737598113600.5262401897
6190794218006.382594285-27212.3825942846
7150733168799.349422721-18066.3494227213
8223226239934.757857842-16708.7578578422
9384138312980.615546771157.3844532998
10156540146607.6317813219932.36821867876
11191441208233.553223857-16792.553223857
12259667247417.40642305612249.5935769442
13228871236309.598281623-7438.59828162256
1487485103669.124021193-16184.1240211932
15322865290963.51670330631901.4832966941
16340093298603.44599930241489.5540006984
17155363165818.875479314-10455.8754793138
18174198182064.479550875-7866.47955087455
19392666336449.42888480556216.5711151954
20302674302823.299554921-149.299554920533
21164733186267.951873261-21534.951873261
222418826988.178750398-2800.178750398
23340411349721.645154138-9310.64515413804
246502965402.6725957346-373.672595734559
25101097101209.76804476-112.768044760377
26282220291008.50230001-8788.50230000966
27273495284867.204124493-11372.2041244934
28214872205907.6981084848964.30189151614
29184474183324.0408549371149.95914506346
30205675184635.78792239721039.2120776033
31197760215225.580022809-17465.5800228092
327356676800.9737075558-3234.97370755584
33177949183229.598001925-5280.59800192473
34148698176094.469282235-27396.4692822346
35300103280144.8720436519958.12795635
36251437236442.72738168514994.2726183153
37132672133568.446440373-896.446440373387
38376465341805.07220515334659.9277948469
39135042152837.759747129-17795.7597471295
40300074270417.22094618229656.7790538178
41271757267279.9919429824477.00805701847
42150949215465.462128757-64516.4621287572
43216802204090.37779512812711.6222048722
44222599216140.9092164726458.09078352848
45261601228137.07787013733463.9221298631
46200657201908.746216324-1251.74621632435
47259084273293.645443316-14209.6454433162
48159965189153.838196517-29188.8381965173
494328749979.5986158956-6692.59861589556
50172212177559.874317809-5347.87431780866
51227681226979.849585803701.150414197279
52106288121156.233588741-14868.2335887407
53268905239665.80955336429239.1904466361
54266568274480.005481018-7912.00548101837
55152474181750.424439442-29276.4244394418
56330910302961.79892705527948.2010729447
57259747253479.3209352326267.67906476783
58190495191988.617349723-1493.6173497234
59154984164823.078517348-9839.07851734767
603821444395.7060734048-6181.70607340484
61158671156839.4177118541831.58228814567
62299775281141.74225065118633.2577493487
63172464165864.0048900886599.99510991152
6494381110007.738077599-15626.7380775994
65243875281978.206678811-38103.2066788112
66334926316214.79376320218711.2062367979
67147979159098.47326554-11119.4732655401
68216638213765.3144927232872.68550727681
69192853197547.520136629-4694.52013662927
70336678296084.95507431340593.044925687
71271773276426.202806127-4653.20280612703
72203606214002.486021063-10396.4860210628
73230177257604.836945523-27427.8369455229
741-1775.397021578421776.39702157842
751468811973.37691798942714.62308201061
76455-940.9425022555721395.94250225557
770-1791.007029039971791.00702903997
78195765187889.1424667427875.85753325776
79306514290071.05527355416442.9447264464
80203-515.191287632785718.191287632785
8171997249.91634002562-50.9163400256177
821754710544.83187835637002.16812164372
83105044117505.772186329-12461.7721863294
84969-690.8112581761611659.81125817616
8507959.12770506532-7959.12770506532
8601635.68374279166-1635.68374279166
870-4260.779576592324260.77957659232
88070251.9144942237-70251.9144942237
890-9160.756482815659160.75648281565
9009464.36388754462-9464.36388754462
910-8431.68770586058431.6877058605
92036795.1284743129-36795.1284743129
9301504.60520200266-1504.60520200266
940-21385.019349971921385.0193499719
950-386.702627048674386.702627048674
960-4980.223696900344980.22369690034
9703456.16879536915-3456.16879536915
980-33707.419071785533707.4190717855
990-49254.010962083149254.0109620831
1000-8014.076869525868014.07686952586
1010-134.777923157212134.777923157212
10201526.06656871648-1526.06656871648
10303478.28237896363-3478.28237896363
104014829.6730241311-14829.6730241311
105039130.8869789514-39130.8869789514
106065.9332904120834-65.9332904120834
10707846.30671411447-7846.30671411447
1080-10853.364660747510853.3646607475
109031645.6934224209-31645.6934224209
1100-8045.046296789068045.04629678906
1110-11653.185562801811653.1855628018
1120-11542.822069899611542.8220698996
1130-13708.569005462413708.5690054624
1140-7596.959193225417596.95919322541
115034072.3821396822-34072.3821396822
11604826.28436151237-4826.28436151237
11709803.94499044076-9803.94499044076
1180-9212.653283459699212.65328345969
119015822.8137683806-15822.8137683806
12008602.32996144177-8602.32996144177
1210-38458.449794280138458.4497942801
1220-20512.040641776420512.0406417764
1230-51768.642299742651768.6422997426
124013364.0036450125-13364.0036450125
125016213.9484583164-16213.9484583164
1260-41424.12755444141424.127554441
1270-26843.995198034926843.9951980349
1280-3674.194137126633674.19413712663
129011498.0960913611-11498.0960913611
13003610.17697544637-3610.17697544637
1310-13629.649121811413629.6491218114
132018967.9218768532-18967.9218768532
1330-26437.997603559926437.9976035599
134015853.5418923612-15853.5418923612
1350693.879362688639-693.879362688639
1360-1295.465490217651295.46549021765
137028793.3160860025-28793.3160860025
1380-8836.070761884258836.07076188425
13907716.84973057906-7716.84973057906
1400-28975.634708420528975.6347084205
1410-3979.144362753663979.14436275366
1420-61215.383058438661215.3830584386
1430-5683.455101446685683.45510144668
1440-2111.448513354712111.44851335471
1450-5244.240103402715244.24010340271
1460-4945.883554622014945.88355462201
147038482.6064436147-38482.6064436147
1480-10719.497949773210719.4979497732
1490-6510.077763005746510.07776300574
150014095.957059684-14095.957059684
151059992.4394107649-59992.4394107649
1520391.790455467163-391.790455467163
153027580.4538709767-27580.4538709767
154053846.4766268707-53846.4766268707
1550-19749.139839544119749.1398395441
15603242.63259979818-3242.63259979818
1570-5685.755822039545685.75582203954
158031451.9009047361-31451.9009047361
1590-63767.791716869263767.7917168692
1600-3918.719985803623918.71998580362
1610-3243.156743696053243.15674369605
1620-2988.314244545642988.31424454564
16306252.89613058012-6252.89613058012
164019179.8528871262-19179.8528871262






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
181.12955807507259e-452.25911615014518e-451
191.03437312741765e-622.0687462548353e-621
201.62110941773435e-763.2422188354687e-761
211.88124135817405e-913.76248271634811e-911
221.75822135489345e-1083.5164427097869e-1081
231.42654519657891e-1232.85309039315782e-1231
241.13805669458067e-1402.27611338916135e-1401
251.04826023344106e-1502.09652046688211e-1501
262.90698567574145e-1675.8139713514829e-1671
275.50762055480565e-1841.10152411096113e-1831
281.5059927075609e-2013.0119854151218e-2011
291.19945465588787e-2112.39890931177574e-2111
301.90824597046463e-2293.81649194092925e-2291
311.17149952504947e-2472.34299905009893e-2471
325.31224796840679e-2601.06244959368136e-2591
331.50042489289988e-2793.00084978579977e-2791
341.09022988946955e-2952.18045977893909e-2951
351.4861159153648e-3062.97223183072959e-3061
361.12646967251804e-3212.25293934503608e-3211
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
772.05276305644827e-194.10552611289654e-191
784.8468065433367e-879.69361308667341e-871
7919.1520776187007e-244.57603880935035e-24
800.9999999999951479.70512174859075e-124.85256087429538e-12
8117.61258939480235e-243.80629469740118e-24
8214.4311831021025e-562.21559155105125e-56
8314.05733128846794e-1662.02866564423397e-166
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 1.12955807507259e-45 & 2.25911615014518e-45 & 1 \tabularnewline
19 & 1.03437312741765e-62 & 2.0687462548353e-62 & 1 \tabularnewline
20 & 1.62110941773435e-76 & 3.2422188354687e-76 & 1 \tabularnewline
21 & 1.88124135817405e-91 & 3.76248271634811e-91 & 1 \tabularnewline
22 & 1.75822135489345e-108 & 3.5164427097869e-108 & 1 \tabularnewline
23 & 1.42654519657891e-123 & 2.85309039315782e-123 & 1 \tabularnewline
24 & 1.13805669458067e-140 & 2.27611338916135e-140 & 1 \tabularnewline
25 & 1.04826023344106e-150 & 2.09652046688211e-150 & 1 \tabularnewline
26 & 2.90698567574145e-167 & 5.8139713514829e-167 & 1 \tabularnewline
27 & 5.50762055480565e-184 & 1.10152411096113e-183 & 1 \tabularnewline
28 & 1.5059927075609e-201 & 3.0119854151218e-201 & 1 \tabularnewline
29 & 1.19945465588787e-211 & 2.39890931177574e-211 & 1 \tabularnewline
30 & 1.90824597046463e-229 & 3.81649194092925e-229 & 1 \tabularnewline
31 & 1.17149952504947e-247 & 2.34299905009893e-247 & 1 \tabularnewline
32 & 5.31224796840679e-260 & 1.06244959368136e-259 & 1 \tabularnewline
33 & 1.50042489289988e-279 & 3.00084978579977e-279 & 1 \tabularnewline
34 & 1.09022988946955e-295 & 2.18045977893909e-295 & 1 \tabularnewline
35 & 1.4861159153648e-306 & 2.97223183072959e-306 & 1 \tabularnewline
36 & 1.12646967251804e-321 & 2.25293934503608e-321 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 2.05276305644827e-19 & 4.10552611289654e-19 & 1 \tabularnewline
78 & 4.8468065433367e-87 & 9.69361308667341e-87 & 1 \tabularnewline
79 & 1 & 9.1520776187007e-24 & 4.57603880935035e-24 \tabularnewline
80 & 0.999999999995147 & 9.70512174859075e-12 & 4.85256087429538e-12 \tabularnewline
81 & 1 & 7.61258939480235e-24 & 3.80629469740118e-24 \tabularnewline
82 & 1 & 4.4311831021025e-56 & 2.21559155105125e-56 \tabularnewline
83 & 1 & 4.05733128846794e-166 & 2.02866564423397e-166 \tabularnewline
84 & 1 & 0 & 0 \tabularnewline
85 & 1 & 0 & 0 \tabularnewline
86 & 1 & 0 & 0 \tabularnewline
87 & 1 & 0 & 0 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 0 & 0 \tabularnewline
131 & 1 & 0 & 0 \tabularnewline
132 & 1 & 0 & 0 \tabularnewline
133 & 1 & 0 & 0 \tabularnewline
134 & 1 & 0 & 0 \tabularnewline
135 & 1 & 0 & 0 \tabularnewline
136 & 1 & 0 & 0 \tabularnewline
137 & 1 & 0 & 0 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 0 & 0 \tabularnewline
140 & 1 & 0 & 0 \tabularnewline
141 & 1 & 0 & 0 \tabularnewline
142 & 1 & 0 & 0 \tabularnewline
143 & 1 & 0 & 0 \tabularnewline
144 & 1 & 0 & 0 \tabularnewline
145 & 1 & 0 & 0 \tabularnewline
146 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&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]18[/C][C]1.12955807507259e-45[/C][C]2.25911615014518e-45[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]1.03437312741765e-62[/C][C]2.0687462548353e-62[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.62110941773435e-76[/C][C]3.2422188354687e-76[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.88124135817405e-91[/C][C]3.76248271634811e-91[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]1.75822135489345e-108[/C][C]3.5164427097869e-108[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.42654519657891e-123[/C][C]2.85309039315782e-123[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]1.13805669458067e-140[/C][C]2.27611338916135e-140[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.04826023344106e-150[/C][C]2.09652046688211e-150[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]2.90698567574145e-167[/C][C]5.8139713514829e-167[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]5.50762055480565e-184[/C][C]1.10152411096113e-183[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.5059927075609e-201[/C][C]3.0119854151218e-201[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.19945465588787e-211[/C][C]2.39890931177574e-211[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.90824597046463e-229[/C][C]3.81649194092925e-229[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.17149952504947e-247[/C][C]2.34299905009893e-247[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]5.31224796840679e-260[/C][C]1.06244959368136e-259[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]1.50042489289988e-279[/C][C]3.00084978579977e-279[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.09022988946955e-295[/C][C]2.18045977893909e-295[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.4861159153648e-306[/C][C]2.97223183072959e-306[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.12646967251804e-321[/C][C]2.25293934503608e-321[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]2.05276305644827e-19[/C][C]4.10552611289654e-19[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]4.8468065433367e-87[/C][C]9.69361308667341e-87[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]9.1520776187007e-24[/C][C]4.57603880935035e-24[/C][/ROW]
[ROW][C]80[/C][C]0.999999999995147[/C][C]9.70512174859075e-12[/C][C]4.85256087429538e-12[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]7.61258939480235e-24[/C][C]3.80629469740118e-24[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]4.4311831021025e-56[/C][C]2.21559155105125e-56[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]4.05733128846794e-166[/C][C]2.02866564423397e-166[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=5

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
181.12955807507259e-452.25911615014518e-451
191.03437312741765e-622.0687462548353e-621
201.62110941773435e-763.2422188354687e-761
211.88124135817405e-913.76248271634811e-911
221.75822135489345e-1083.5164427097869e-1081
231.42654519657891e-1232.85309039315782e-1231
241.13805669458067e-1402.27611338916135e-1401
251.04826023344106e-1502.09652046688211e-1501
262.90698567574145e-1675.8139713514829e-1671
275.50762055480565e-1841.10152411096113e-1831
281.5059927075609e-2013.0119854151218e-2011
291.19945465588787e-2112.39890931177574e-2111
301.90824597046463e-2293.81649194092925e-2291
311.17149952504947e-2472.34299905009893e-2471
325.31224796840679e-2601.06244959368136e-2591
331.50042489289988e-2793.00084978579977e-2791
341.09022988946955e-2952.18045977893909e-2951
351.4861159153648e-3062.97223183072959e-3061
361.12646967251804e-3212.25293934503608e-3211
37001
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772.05276305644827e-194.10552611289654e-191
784.8468065433367e-879.69361308667341e-871
7919.1520776187007e-244.57603880935035e-24
800.9999999999951479.70512174859075e-124.85256087429538e-12
8117.61258939480235e-243.80629469740118e-24
8214.4311831021025e-562.21559155105125e-56
8314.05733128846794e-1662.02866564423397e-166
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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1291NOK
5% type I error level1291NOK
10% type I error level1291NOK

\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 & 129 & 1 & NOK \tabularnewline
5% type I error level & 129 & 1 & NOK \tabularnewline
10% type I error level & 129 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152920&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]129[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]129[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]129[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152920&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152920&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1291NOK
5% type I error level1291NOK
10% type I error level1291NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}