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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 10:55:01 -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/2012/Nov/05/t13521309196vjx5xkj7xyof35.htm/, Retrieved Fri, 03 Feb 2023 09:32:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186132, Retrieved Fri, 03 Feb 2023 09:32:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Concern, tut] [2010-12-02 14:07:40] [e73e9643c012a54583c6a406017b2645]
-    D  [Multiple Regression] [WS7 - Eerste Regr...] [2011-11-21 15:46:36] [b8fde34a99ee6a7d49500940cae4da2a]
-    D      [Multiple Regression] [ws7] [2012-11-05 15:55:01] [b262fb17dc370e47870f909f9ce3690a] [Current]
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Dataseries X:
1	1910	61	17	56	84	4	21	51
2	2598	74	19	73	47	3	15	48
3	2144	57	18	62	63	3	17	46
4	1331	50	15	42	28	3	20	42
5	1431	48	15	59	22	2	12	38
6	7334	2	12	27	18	6	4	38
7	1133	41	15	59	20	5	9	36
8	1195	31	20	78	27	5	11	35
9	1522	61	14	56	37	5	12	35
10	1551	12	12	47	23	6	7	35
11	2108	46	13	51	67	5	14	34
12	1335	31	17	47	28	4	11	34
13	1065	33	12	48	28	5	9	31
14	842	49	10	35	45	3	14	31
15	1539	15	13	47	15	5	4	31
16	1508	59	15	55	36	5	11	31
17	1598	28	12	42	12	2	10	30
18	1219	55	16	55	30	6	9	30
19	1443	35	13	47	28	9	8	30
20	1546	44	15	54	27	2	14	30
21	914	41	15	60	43	5	13	30
22	1370	26	13	51	10	3	10	28
23	1318	28	12	47	22	4	9	27
24	1313	40	15	52	27	4	11	27
25	1743	28	12	38	21	11	7	27
26	1102	67	12	12	24	5	10	26
27	1275	56	12	48	52	3	15	26
28	1253	54	12	48	24	5	7	26
29	1487	25	8	32	19	5	10	26
30	1098	19	9	27	12	0	4	26
31	1176	36	12	47	21	3	10	25
32	903	42	16	58	71	4	13	25
33	1290	19	14	47	19	4	5	25
34	1050	57	13	46	24	5	10	25
35	930	28	15	60	12	2	10	25
36	821	32	15	56	29	5	11	24
37	826	10	12	41	13	3	7	24
38	1402	28	12	45	22	11	6	24
39	1495	41	12	48	27	5	8	24
40	1064	48	15	60	36	5	10	24
41	1469	57	12	48	27	3	9	24
42	1493	35	13	42	21	5	8	24
43	1239	30	12	47	28	4	11	24
44	1317	39	12	41	17	3	5	23
45	708	17	15	49	15	8	5	23
46	872	33	12	39	26	3	10	23
47	853	55	12	39	19	3	8	23
48	1174	30	12	42	34	11	9	23
49	982	22	13	50	21	4	7	23
50	1202	42	12	41	32	6	8	23
51	873	49	15	52	14	14	5	23
52	1000	13	9	36	17	6	5	22
53	1131	15	13	45	16	3	7	22
54	793	24	12	46	18	5	10	22
55	1106	3	13	55	8	8	2	22
56	1205	35	13	49	30	8	5	22
57	1671	37	13	48	31	3	13	22
58	1374	28	13	39	19	3	10	21
59	775	19	12	48	10	3	5	21
60	804	38	15	45	24	5	10	21
61	1224	29	14	52	28	6	8	21
62	1233	38	15	51	27	3	7	20
63	1170	35	14	41	16	3	10	20
64	913	23	9	32	17	3	5	20
65	613	27	14	52	30	3	9	20
66	1204	32	16	54	20	4	6	19
67	933	7	9	27	10	5	6	18
68	861	57	12	41	30	3	9	18
69	932	39	12	45	34	5	11	18
70	705	18	13	52	13	13	6	18




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186132&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186132&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Rang[t] = + 95.7151231150161 + 0.000811260866225314Pageviews[t] -0.0967248435916385Blogs[t] + 1.54079952985966PR[t] -0.123726061471697LFM[t] + 0.142466830241946KCS[t] + 0.0742760351352671SPR[t] + 0.284731988242071CH[t] -2.96193342808934`Hours\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Rang[t] =  +  95.7151231150161 +  0.000811260866225314Pageviews[t] -0.0967248435916385Blogs[t] +  1.54079952985966PR[t] -0.123726061471697LFM[t] +  0.142466830241946KCS[t] +  0.0742760351352671SPR[t] +  0.284731988242071CH[t] -2.96193342808934`Hours\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186132&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Rang[t] =  +  95.7151231150161 +  0.000811260866225314Pageviews[t] -0.0967248435916385Blogs[t] +  1.54079952985966PR[t] -0.123726061471697LFM[t] +  0.142466830241946KCS[t] +  0.0742760351352671SPR[t] +  0.284731988242071CH[t] -2.96193342808934`Hours\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186132&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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
Rang[t] = + 95.7151231150161 + 0.000811260866225314Pageviews[t] -0.0967248435916385Blogs[t] + 1.54079952985966PR[t] -0.123726061471697LFM[t] + 0.142466830241946KCS[t] + 0.0742760351352671SPR[t] + 0.284731988242071CH[t] -2.96193342808934`Hours\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)95.71512311501617.19395413.304900
Pageviews0.0008112608662253140.0017390.46650.6425290.321265
Blogs-0.09672484359163850.093794-1.03120.3064970.153248
PR1.540799529859660.8745561.76180.0831120.041556
LFM-0.1237260614716970.183882-0.67290.503580.25179
KCS0.1424668302419460.1205351.18190.2418130.120906
SPR0.07427603513526710.4656260.15950.8737870.436894
CH0.2847319882420710.5739760.49610.6216280.310814
`Hours\r`-2.961933428089340.248093-11.938800

\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) & 95.7151231150161 & 7.193954 & 13.3049 & 0 & 0 \tabularnewline
Pageviews & 0.000811260866225314 & 0.001739 & 0.4665 & 0.642529 & 0.321265 \tabularnewline
Blogs & -0.0967248435916385 & 0.093794 & -1.0312 & 0.306497 & 0.153248 \tabularnewline
PR & 1.54079952985966 & 0.874556 & 1.7618 & 0.083112 & 0.041556 \tabularnewline
LFM & -0.123726061471697 & 0.183882 & -0.6729 & 0.50358 & 0.25179 \tabularnewline
KCS & 0.142466830241946 & 0.120535 & 1.1819 & 0.241813 & 0.120906 \tabularnewline
SPR & 0.0742760351352671 & 0.465626 & 0.1595 & 0.873787 & 0.436894 \tabularnewline
CH & 0.284731988242071 & 0.573976 & 0.4961 & 0.621628 & 0.310814 \tabularnewline
`Hours\r` & -2.96193342808934 & 0.248093 & -11.9388 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186132&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]95.7151231150161[/C][C]7.193954[/C][C]13.3049[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Pageviews[/C][C]0.000811260866225314[/C][C]0.001739[/C][C]0.4665[/C][C]0.642529[/C][C]0.321265[/C][/ROW]
[ROW][C]Blogs[/C][C]-0.0967248435916385[/C][C]0.093794[/C][C]-1.0312[/C][C]0.306497[/C][C]0.153248[/C][/ROW]
[ROW][C]PR[/C][C]1.54079952985966[/C][C]0.874556[/C][C]1.7618[/C][C]0.083112[/C][C]0.041556[/C][/ROW]
[ROW][C]LFM[/C][C]-0.123726061471697[/C][C]0.183882[/C][C]-0.6729[/C][C]0.50358[/C][C]0.25179[/C][/ROW]
[ROW][C]KCS[/C][C]0.142466830241946[/C][C]0.120535[/C][C]1.1819[/C][C]0.241813[/C][C]0.120906[/C][/ROW]
[ROW][C]SPR[/C][C]0.0742760351352671[/C][C]0.465626[/C][C]0.1595[/C][C]0.873787[/C][C]0.436894[/C][/ROW]
[ROW][C]CH[/C][C]0.284731988242071[/C][C]0.573976[/C][C]0.4961[/C][C]0.621628[/C][C]0.310814[/C][/ROW]
[ROW][C]`Hours\r`[/C][C]-2.96193342808934[/C][C]0.248093[/C][C]-11.9388[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186132&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)95.71512311501617.19395413.304900
Pageviews0.0008112608662253140.0017390.46650.6425290.321265
Blogs-0.09672484359163850.093794-1.03120.3064970.153248
PR1.540799529859660.8745561.76180.0831120.041556
LFM-0.1237260614716970.183882-0.67290.503580.25179
KCS0.1424668302419460.1205351.18190.2418130.120906
SPR0.07427603513526710.4656260.15950.8737870.436894
CH0.2847319882420710.5739760.49610.6216280.310814
`Hours\r`-2.961933428089340.248093-11.938800







Multiple Linear Regression - Regression Statistics
Multiple R0.910480067958452
R-squared0.828973954149627
Adjusted R-squared0.806544308792201
F-TEST (value)36.9588524891755
F-TEST (DF numerator)8
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.95113952383885
Sum Squared Residuals4887.49682528903

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.910480067958452 \tabularnewline
R-squared & 0.828973954149627 \tabularnewline
Adjusted R-squared & 0.806544308792201 \tabularnewline
F-TEST (value) & 36.9588524891755 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 61 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.95113952383885 \tabularnewline
Sum Squared Residuals & 4887.49682528903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186132&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.910480067958452[/C][/ROW]
[ROW][C]R-squared[/C][C]0.828973954149627[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.806544308792201[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.9588524891755[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]61[/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]8.95113952383885[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4887.49682528903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186132&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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 R0.910480067958452
R-squared0.828973954149627
Adjusted R-squared0.806544308792201
F-TEST (value)36.9588524891755
F-TEST (DF numerator)8
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.95113952383885
Sum Squared Residuals4887.49682528903







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11-22.185566722992823.1855667229928
22-20.074726598292322.0747265982923
33-10.205729427637713.2057294276377
44-4.620497348267018.62049734826701
552.191536170353812.80846382964619
668.21596816004925-2.21596816004925
777.63441967373452-0.634419673734519
8818.53383398096-10.53383398096
9911.0839474403475-2.08394744034746
101010.5350073055219-0.535007305521906
111119.8934520511608-8.89345205116076
121220.8906440414712-8.89064404147122
131321.0410425525563-8.04104255255627
141421.5364176063875-7.53641760638745
151521.5554242447723-6.55542424477226
161624.3510999606083-8.35109996060829
171723.4437932080972-6.44379320809721
181827.6562889797842-9.65628897978418
191925.7930806445259-6.79308064452586
202028.2676264041034-8.26762640410337
212129.5202930996286-8.52029309962857
222229.5927496252256-7.59274962522557
232332.7722985269116-9.77229852691159
242436.8931105093952-12.8931105093952
252534.0386203875234-9.03862038752341
262636.761084543193-10.761084543193
272738.7754468572912-11.7754468572912
282832.832673722977-4.83267372297702
292931.7858099074562-2.78580990745624
303031.1329684124402-1.13296841244021
313137.875156714218-6.87515671421804
323249.9273583913974-17.9273583913974
333341.0592442871859-8.05924428718591
343437.982194281977-3.98219428197697
353540.1068675729939-5.10686757299386
363646.0178746459458-10.0178746459458
373741.8164205346795-4.81642053467952
383841.6394331281066-3.6394331281066
393940.9224211544414-1.92242115444144
404044.8850451165371-4.88504511653713
414139.48991079242491.5100892075751
424242.929502611497-0.929502611496984
434342.82482447350040.175175526499634
444442.37206592841161.62793407158837
454547.7249912328943-2.7249912328943
464645.54471744082250.45528255917755
474741.83462513717045.16537486282964
484847.1579255035580.842074496442043
494945.38548818848563.61451181151442
505045.2026229217824.797377078218
515144.69566547704366.30433452295639
525243.81073541847558.18926458152455
535348.97739351179754.02260648820255
545447.45581985083786.54418014916217
555546.68853509105498.31146490894508
565648.4044775207587.595522479242
575750.76174401920966.2382559807904
585852.90299318797055.09700681202952
595947.927376024933511.0726239750665
606054.67345497067515.32654502932494
616153.55250554627187.44749445372819
626256.6657153972745.33428460272602
636355.88830241039887.11169758960118
646448.968850283857115.0311497161429
656556.55904581560098.44095418439907
666660.14636890243685.85363109756324
676756.51118640900410.488813590996
686858.06174767532379.93825232467631
696960.65337350331098.34662649668915
707059.353901006450810.6460989935492

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & -22.1855667229928 & 23.1855667229928 \tabularnewline
2 & 2 & -20.0747265982923 & 22.0747265982923 \tabularnewline
3 & 3 & -10.2057294276377 & 13.2057294276377 \tabularnewline
4 & 4 & -4.62049734826701 & 8.62049734826701 \tabularnewline
5 & 5 & 2.19153617035381 & 2.80846382964619 \tabularnewline
6 & 6 & 8.21596816004925 & -2.21596816004925 \tabularnewline
7 & 7 & 7.63441967373452 & -0.634419673734519 \tabularnewline
8 & 8 & 18.53383398096 & -10.53383398096 \tabularnewline
9 & 9 & 11.0839474403475 & -2.08394744034746 \tabularnewline
10 & 10 & 10.5350073055219 & -0.535007305521906 \tabularnewline
11 & 11 & 19.8934520511608 & -8.89345205116076 \tabularnewline
12 & 12 & 20.8906440414712 & -8.89064404147122 \tabularnewline
13 & 13 & 21.0410425525563 & -8.04104255255627 \tabularnewline
14 & 14 & 21.5364176063875 & -7.53641760638745 \tabularnewline
15 & 15 & 21.5554242447723 & -6.55542424477226 \tabularnewline
16 & 16 & 24.3510999606083 & -8.35109996060829 \tabularnewline
17 & 17 & 23.4437932080972 & -6.44379320809721 \tabularnewline
18 & 18 & 27.6562889797842 & -9.65628897978418 \tabularnewline
19 & 19 & 25.7930806445259 & -6.79308064452586 \tabularnewline
20 & 20 & 28.2676264041034 & -8.26762640410337 \tabularnewline
21 & 21 & 29.5202930996286 & -8.52029309962857 \tabularnewline
22 & 22 & 29.5927496252256 & -7.59274962522557 \tabularnewline
23 & 23 & 32.7722985269116 & -9.77229852691159 \tabularnewline
24 & 24 & 36.8931105093952 & -12.8931105093952 \tabularnewline
25 & 25 & 34.0386203875234 & -9.03862038752341 \tabularnewline
26 & 26 & 36.761084543193 & -10.761084543193 \tabularnewline
27 & 27 & 38.7754468572912 & -11.7754468572912 \tabularnewline
28 & 28 & 32.832673722977 & -4.83267372297702 \tabularnewline
29 & 29 & 31.7858099074562 & -2.78580990745624 \tabularnewline
30 & 30 & 31.1329684124402 & -1.13296841244021 \tabularnewline
31 & 31 & 37.875156714218 & -6.87515671421804 \tabularnewline
32 & 32 & 49.9273583913974 & -17.9273583913974 \tabularnewline
33 & 33 & 41.0592442871859 & -8.05924428718591 \tabularnewline
34 & 34 & 37.982194281977 & -3.98219428197697 \tabularnewline
35 & 35 & 40.1068675729939 & -5.10686757299386 \tabularnewline
36 & 36 & 46.0178746459458 & -10.0178746459458 \tabularnewline
37 & 37 & 41.8164205346795 & -4.81642053467952 \tabularnewline
38 & 38 & 41.6394331281066 & -3.6394331281066 \tabularnewline
39 & 39 & 40.9224211544414 & -1.92242115444144 \tabularnewline
40 & 40 & 44.8850451165371 & -4.88504511653713 \tabularnewline
41 & 41 & 39.4899107924249 & 1.5100892075751 \tabularnewline
42 & 42 & 42.929502611497 & -0.929502611496984 \tabularnewline
43 & 43 & 42.8248244735004 & 0.175175526499634 \tabularnewline
44 & 44 & 42.3720659284116 & 1.62793407158837 \tabularnewline
45 & 45 & 47.7249912328943 & -2.7249912328943 \tabularnewline
46 & 46 & 45.5447174408225 & 0.45528255917755 \tabularnewline
47 & 47 & 41.8346251371704 & 5.16537486282964 \tabularnewline
48 & 48 & 47.157925503558 & 0.842074496442043 \tabularnewline
49 & 49 & 45.3854881884856 & 3.61451181151442 \tabularnewline
50 & 50 & 45.202622921782 & 4.797377078218 \tabularnewline
51 & 51 & 44.6956654770436 & 6.30433452295639 \tabularnewline
52 & 52 & 43.8107354184755 & 8.18926458152455 \tabularnewline
53 & 53 & 48.9773935117975 & 4.02260648820255 \tabularnewline
54 & 54 & 47.4558198508378 & 6.54418014916217 \tabularnewline
55 & 55 & 46.6885350910549 & 8.31146490894508 \tabularnewline
56 & 56 & 48.404477520758 & 7.595522479242 \tabularnewline
57 & 57 & 50.7617440192096 & 6.2382559807904 \tabularnewline
58 & 58 & 52.9029931879705 & 5.09700681202952 \tabularnewline
59 & 59 & 47.9273760249335 & 11.0726239750665 \tabularnewline
60 & 60 & 54.6734549706751 & 5.32654502932494 \tabularnewline
61 & 61 & 53.5525055462718 & 7.44749445372819 \tabularnewline
62 & 62 & 56.665715397274 & 5.33428460272602 \tabularnewline
63 & 63 & 55.8883024103988 & 7.11169758960118 \tabularnewline
64 & 64 & 48.9688502838571 & 15.0311497161429 \tabularnewline
65 & 65 & 56.5590458156009 & 8.44095418439907 \tabularnewline
66 & 66 & 60.1463689024368 & 5.85363109756324 \tabularnewline
67 & 67 & 56.511186409004 & 10.488813590996 \tabularnewline
68 & 68 & 58.0617476753237 & 9.93825232467631 \tabularnewline
69 & 69 & 60.6533735033109 & 8.34662649668915 \tabularnewline
70 & 70 & 59.3539010064508 & 10.6460989935492 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186132&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]1[/C][C]-22.1855667229928[/C][C]23.1855667229928[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]-20.0747265982923[/C][C]22.0747265982923[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]-10.2057294276377[/C][C]13.2057294276377[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]-4.62049734826701[/C][C]8.62049734826701[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]2.19153617035381[/C][C]2.80846382964619[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]8.21596816004925[/C][C]-2.21596816004925[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]7.63441967373452[/C][C]-0.634419673734519[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]18.53383398096[/C][C]-10.53383398096[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]11.0839474403475[/C][C]-2.08394744034746[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]10.5350073055219[/C][C]-0.535007305521906[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]19.8934520511608[/C][C]-8.89345205116076[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]20.8906440414712[/C][C]-8.89064404147122[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]21.0410425525563[/C][C]-8.04104255255627[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]21.5364176063875[/C][C]-7.53641760638745[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.5554242447723[/C][C]-6.55542424477226[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]24.3510999606083[/C][C]-8.35109996060829[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]23.4437932080972[/C][C]-6.44379320809721[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]27.6562889797842[/C][C]-9.65628897978418[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]25.7930806445259[/C][C]-6.79308064452586[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]28.2676264041034[/C][C]-8.26762640410337[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]29.5202930996286[/C][C]-8.52029309962857[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]29.5927496252256[/C][C]-7.59274962522557[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]32.7722985269116[/C][C]-9.77229852691159[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]36.8931105093952[/C][C]-12.8931105093952[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]34.0386203875234[/C][C]-9.03862038752341[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]36.761084543193[/C][C]-10.761084543193[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]38.7754468572912[/C][C]-11.7754468572912[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]32.832673722977[/C][C]-4.83267372297702[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]31.7858099074562[/C][C]-2.78580990745624[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]31.1329684124402[/C][C]-1.13296841244021[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]37.875156714218[/C][C]-6.87515671421804[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]49.9273583913974[/C][C]-17.9273583913974[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]41.0592442871859[/C][C]-8.05924428718591[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]37.982194281977[/C][C]-3.98219428197697[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]40.1068675729939[/C][C]-5.10686757299386[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]46.0178746459458[/C][C]-10.0178746459458[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]41.8164205346795[/C][C]-4.81642053467952[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]41.6394331281066[/C][C]-3.6394331281066[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]40.9224211544414[/C][C]-1.92242115444144[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]44.8850451165371[/C][C]-4.88504511653713[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]39.4899107924249[/C][C]1.5100892075751[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]42.929502611497[/C][C]-0.929502611496984[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]42.8248244735004[/C][C]0.175175526499634[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]42.3720659284116[/C][C]1.62793407158837[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]47.7249912328943[/C][C]-2.7249912328943[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]45.5447174408225[/C][C]0.45528255917755[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]41.8346251371704[/C][C]5.16537486282964[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]47.157925503558[/C][C]0.842074496442043[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]45.3854881884856[/C][C]3.61451181151442[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]45.202622921782[/C][C]4.797377078218[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]44.6956654770436[/C][C]6.30433452295639[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]43.8107354184755[/C][C]8.18926458152455[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]48.9773935117975[/C][C]4.02260648820255[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]47.4558198508378[/C][C]6.54418014916217[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]46.6885350910549[/C][C]8.31146490894508[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]48.404477520758[/C][C]7.595522479242[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]50.7617440192096[/C][C]6.2382559807904[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]52.9029931879705[/C][C]5.09700681202952[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]47.9273760249335[/C][C]11.0726239750665[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]54.6734549706751[/C][C]5.32654502932494[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]53.5525055462718[/C][C]7.44749445372819[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]56.665715397274[/C][C]5.33428460272602[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]55.8883024103988[/C][C]7.11169758960118[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]48.9688502838571[/C][C]15.0311497161429[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]56.5590458156009[/C][C]8.44095418439907[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]60.1463689024368[/C][C]5.85363109756324[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]56.511186409004[/C][C]10.488813590996[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]58.0617476753237[/C][C]9.93825232467631[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]60.6533735033109[/C][C]8.34662649668915[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]59.3539010064508[/C][C]10.6460989935492[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186132&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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 IndexActualsInterpolationForecastResidualsPrediction Error
11-22.185566722992823.1855667229928
22-20.074726598292322.0747265982923
33-10.205729427637713.2057294276377
44-4.620497348267018.62049734826701
552.191536170353812.80846382964619
668.21596816004925-2.21596816004925
777.63441967373452-0.634419673734519
8818.53383398096-10.53383398096
9911.0839474403475-2.08394744034746
101010.5350073055219-0.535007305521906
111119.8934520511608-8.89345205116076
121220.8906440414712-8.89064404147122
131321.0410425525563-8.04104255255627
141421.5364176063875-7.53641760638745
151521.5554242447723-6.55542424477226
161624.3510999606083-8.35109996060829
171723.4437932080972-6.44379320809721
181827.6562889797842-9.65628897978418
191925.7930806445259-6.79308064452586
202028.2676264041034-8.26762640410337
212129.5202930996286-8.52029309962857
222229.5927496252256-7.59274962522557
232332.7722985269116-9.77229852691159
242436.8931105093952-12.8931105093952
252534.0386203875234-9.03862038752341
262636.761084543193-10.761084543193
272738.7754468572912-11.7754468572912
282832.832673722977-4.83267372297702
292931.7858099074562-2.78580990745624
303031.1329684124402-1.13296841244021
313137.875156714218-6.87515671421804
323249.9273583913974-17.9273583913974
333341.0592442871859-8.05924428718591
343437.982194281977-3.98219428197697
353540.1068675729939-5.10686757299386
363646.0178746459458-10.0178746459458
373741.8164205346795-4.81642053467952
383841.6394331281066-3.6394331281066
393940.9224211544414-1.92242115444144
404044.8850451165371-4.88504511653713
414139.48991079242491.5100892075751
424242.929502611497-0.929502611496984
434342.82482447350040.175175526499634
444442.37206592841161.62793407158837
454547.7249912328943-2.7249912328943
464645.54471744082250.45528255917755
474741.83462513717045.16537486282964
484847.1579255035580.842074496442043
494945.38548818848563.61451181151442
505045.2026229217824.797377078218
515144.69566547704366.30433452295639
525243.81073541847558.18926458152455
535348.97739351179754.02260648820255
545447.45581985083786.54418014916217
555546.68853509105498.31146490894508
565648.4044775207587.595522479242
575750.76174401920966.2382559807904
585852.90299318797055.09700681202952
595947.927376024933511.0726239750665
606054.67345497067515.32654502932494
616153.55250554627187.44749445372819
626256.6657153972745.33428460272602
636355.88830241039887.11169758960118
646448.968850283857115.0311497161429
656556.55904581560098.44095418439907
666660.14636890243685.85363109756324
676756.51118640900410.488813590996
686858.06174767532379.93825232467631
696960.65337350331098.34662649668915
707059.353901006450810.6460989935492







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.04778714980753670.09557429961507340.952212850192463
130.0205657660452350.04113153209046990.979434233954765
140.008413962356553910.01682792471310780.991586037643446
150.005849433918460820.01169886783692160.994150566081539
160.007792755321149140.01558551064229830.992207244678851
170.03439027197038630.06878054394077270.965609728029614
180.02820987419713760.05641974839427520.971790125802862
190.05402295542799180.1080459108559840.945977044572008
200.2116953981695490.4233907963390990.788304601830451
210.5082561149168120.9834877701663760.491743885083188
220.6001581352511370.7996837294977260.399841864748863
230.6039086568012060.7921826863975870.396091343198794
240.5616684438407420.8766631123185150.438331556159258
250.5687468490115920.8625063019768150.431253150988408
260.5051058496262190.9897883007475630.494894150373781
270.4592616071594830.9185232143189660.540738392840517
280.5672161541739740.8655676916520520.432783845826026
290.6555245982734990.6889508034530020.344475401726501
300.7738248534910690.4523502930178630.226175146508931
310.8367101373561670.3265797252876650.163289862643833
320.8221243271897410.3557513456205170.177875672810259
330.8884823760497730.2230352479004550.111517623950227
340.9156405397713680.1687189204572640.0843594602286321
350.939450554613360.121098890773280.0605494453866398
360.9767307680600580.04653846387988450.0232692319399423
370.9914651813736540.01706963725269140.00853481862634572
380.9978869451634940.004226109673011450.00211305483650572
390.9990828132631190.001834373473761610.000917186736880804
400.9997133829619950.0005732340760091030.000286617038004551
410.9998228742333950.0003542515332094980.000177125766604749
420.9998520351325620.0002959297348762680.000147964867438134
430.9999009400434990.0001981199130013859.90599565006923e-05
440.9999773550783434.52898433142659e-052.2644921657133e-05
450.9999917360752281.65278495438306e-058.26392477191528e-06
460.9999949931722591.00136554828529e-055.00682774142644e-06
470.9999953551760829.2896478358901e-064.64482391794505e-06
480.9999948696253291.02607493417068e-055.13037467085339e-06
490.9999955713277868.85734442733546e-064.42867221366773e-06
500.9999899793721242.00412557524362e-051.00206278762181e-05
510.9999681000499626.37999000751802e-053.18999500375901e-05
520.9999629902033067.40195933879418e-053.70097966939709e-05
530.9999619193813697.61612372612063e-053.80806186306032e-05
540.9999629659129897.40681740213596e-053.70340870106798e-05
550.9998937773550410.0002124452899176610.00010622264495883
560.9996284905333980.0007430189332044610.00037150946660223
570.9980191729252570.003961654149485360.00198082707474268
580.9908024711234840.01839505775303310.00919752887651653

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0477871498075367 & 0.0955742996150734 & 0.952212850192463 \tabularnewline
13 & 0.020565766045235 & 0.0411315320904699 & 0.979434233954765 \tabularnewline
14 & 0.00841396235655391 & 0.0168279247131078 & 0.991586037643446 \tabularnewline
15 & 0.00584943391846082 & 0.0116988678369216 & 0.994150566081539 \tabularnewline
16 & 0.00779275532114914 & 0.0155855106422983 & 0.992207244678851 \tabularnewline
17 & 0.0343902719703863 & 0.0687805439407727 & 0.965609728029614 \tabularnewline
18 & 0.0282098741971376 & 0.0564197483942752 & 0.971790125802862 \tabularnewline
19 & 0.0540229554279918 & 0.108045910855984 & 0.945977044572008 \tabularnewline
20 & 0.211695398169549 & 0.423390796339099 & 0.788304601830451 \tabularnewline
21 & 0.508256114916812 & 0.983487770166376 & 0.491743885083188 \tabularnewline
22 & 0.600158135251137 & 0.799683729497726 & 0.399841864748863 \tabularnewline
23 & 0.603908656801206 & 0.792182686397587 & 0.396091343198794 \tabularnewline
24 & 0.561668443840742 & 0.876663112318515 & 0.438331556159258 \tabularnewline
25 & 0.568746849011592 & 0.862506301976815 & 0.431253150988408 \tabularnewline
26 & 0.505105849626219 & 0.989788300747563 & 0.494894150373781 \tabularnewline
27 & 0.459261607159483 & 0.918523214318966 & 0.540738392840517 \tabularnewline
28 & 0.567216154173974 & 0.865567691652052 & 0.432783845826026 \tabularnewline
29 & 0.655524598273499 & 0.688950803453002 & 0.344475401726501 \tabularnewline
30 & 0.773824853491069 & 0.452350293017863 & 0.226175146508931 \tabularnewline
31 & 0.836710137356167 & 0.326579725287665 & 0.163289862643833 \tabularnewline
32 & 0.822124327189741 & 0.355751345620517 & 0.177875672810259 \tabularnewline
33 & 0.888482376049773 & 0.223035247900455 & 0.111517623950227 \tabularnewline
34 & 0.915640539771368 & 0.168718920457264 & 0.0843594602286321 \tabularnewline
35 & 0.93945055461336 & 0.12109889077328 & 0.0605494453866398 \tabularnewline
36 & 0.976730768060058 & 0.0465384638798845 & 0.0232692319399423 \tabularnewline
37 & 0.991465181373654 & 0.0170696372526914 & 0.00853481862634572 \tabularnewline
38 & 0.997886945163494 & 0.00422610967301145 & 0.00211305483650572 \tabularnewline
39 & 0.999082813263119 & 0.00183437347376161 & 0.000917186736880804 \tabularnewline
40 & 0.999713382961995 & 0.000573234076009103 & 0.000286617038004551 \tabularnewline
41 & 0.999822874233395 & 0.000354251533209498 & 0.000177125766604749 \tabularnewline
42 & 0.999852035132562 & 0.000295929734876268 & 0.000147964867438134 \tabularnewline
43 & 0.999900940043499 & 0.000198119913001385 & 9.90599565006923e-05 \tabularnewline
44 & 0.999977355078343 & 4.52898433142659e-05 & 2.2644921657133e-05 \tabularnewline
45 & 0.999991736075228 & 1.65278495438306e-05 & 8.26392477191528e-06 \tabularnewline
46 & 0.999994993172259 & 1.00136554828529e-05 & 5.00682774142644e-06 \tabularnewline
47 & 0.999995355176082 & 9.2896478358901e-06 & 4.64482391794505e-06 \tabularnewline
48 & 0.999994869625329 & 1.02607493417068e-05 & 5.13037467085339e-06 \tabularnewline
49 & 0.999995571327786 & 8.85734442733546e-06 & 4.42867221366773e-06 \tabularnewline
50 & 0.999989979372124 & 2.00412557524362e-05 & 1.00206278762181e-05 \tabularnewline
51 & 0.999968100049962 & 6.37999000751802e-05 & 3.18999500375901e-05 \tabularnewline
52 & 0.999962990203306 & 7.40195933879418e-05 & 3.70097966939709e-05 \tabularnewline
53 & 0.999961919381369 & 7.61612372612063e-05 & 3.80806186306032e-05 \tabularnewline
54 & 0.999962965912989 & 7.40681740213596e-05 & 3.70340870106798e-05 \tabularnewline
55 & 0.999893777355041 & 0.000212445289917661 & 0.00010622264495883 \tabularnewline
56 & 0.999628490533398 & 0.000743018933204461 & 0.00037150946660223 \tabularnewline
57 & 0.998019172925257 & 0.00396165414948536 & 0.00198082707474268 \tabularnewline
58 & 0.990802471123484 & 0.0183950577530331 & 0.00919752887651653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186132&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]12[/C][C]0.0477871498075367[/C][C]0.0955742996150734[/C][C]0.952212850192463[/C][/ROW]
[ROW][C]13[/C][C]0.020565766045235[/C][C]0.0411315320904699[/C][C]0.979434233954765[/C][/ROW]
[ROW][C]14[/C][C]0.00841396235655391[/C][C]0.0168279247131078[/C][C]0.991586037643446[/C][/ROW]
[ROW][C]15[/C][C]0.00584943391846082[/C][C]0.0116988678369216[/C][C]0.994150566081539[/C][/ROW]
[ROW][C]16[/C][C]0.00779275532114914[/C][C]0.0155855106422983[/C][C]0.992207244678851[/C][/ROW]
[ROW][C]17[/C][C]0.0343902719703863[/C][C]0.0687805439407727[/C][C]0.965609728029614[/C][/ROW]
[ROW][C]18[/C][C]0.0282098741971376[/C][C]0.0564197483942752[/C][C]0.971790125802862[/C][/ROW]
[ROW][C]19[/C][C]0.0540229554279918[/C][C]0.108045910855984[/C][C]0.945977044572008[/C][/ROW]
[ROW][C]20[/C][C]0.211695398169549[/C][C]0.423390796339099[/C][C]0.788304601830451[/C][/ROW]
[ROW][C]21[/C][C]0.508256114916812[/C][C]0.983487770166376[/C][C]0.491743885083188[/C][/ROW]
[ROW][C]22[/C][C]0.600158135251137[/C][C]0.799683729497726[/C][C]0.399841864748863[/C][/ROW]
[ROW][C]23[/C][C]0.603908656801206[/C][C]0.792182686397587[/C][C]0.396091343198794[/C][/ROW]
[ROW][C]24[/C][C]0.561668443840742[/C][C]0.876663112318515[/C][C]0.438331556159258[/C][/ROW]
[ROW][C]25[/C][C]0.568746849011592[/C][C]0.862506301976815[/C][C]0.431253150988408[/C][/ROW]
[ROW][C]26[/C][C]0.505105849626219[/C][C]0.989788300747563[/C][C]0.494894150373781[/C][/ROW]
[ROW][C]27[/C][C]0.459261607159483[/C][C]0.918523214318966[/C][C]0.540738392840517[/C][/ROW]
[ROW][C]28[/C][C]0.567216154173974[/C][C]0.865567691652052[/C][C]0.432783845826026[/C][/ROW]
[ROW][C]29[/C][C]0.655524598273499[/C][C]0.688950803453002[/C][C]0.344475401726501[/C][/ROW]
[ROW][C]30[/C][C]0.773824853491069[/C][C]0.452350293017863[/C][C]0.226175146508931[/C][/ROW]
[ROW][C]31[/C][C]0.836710137356167[/C][C]0.326579725287665[/C][C]0.163289862643833[/C][/ROW]
[ROW][C]32[/C][C]0.822124327189741[/C][C]0.355751345620517[/C][C]0.177875672810259[/C][/ROW]
[ROW][C]33[/C][C]0.888482376049773[/C][C]0.223035247900455[/C][C]0.111517623950227[/C][/ROW]
[ROW][C]34[/C][C]0.915640539771368[/C][C]0.168718920457264[/C][C]0.0843594602286321[/C][/ROW]
[ROW][C]35[/C][C]0.93945055461336[/C][C]0.12109889077328[/C][C]0.0605494453866398[/C][/ROW]
[ROW][C]36[/C][C]0.976730768060058[/C][C]0.0465384638798845[/C][C]0.0232692319399423[/C][/ROW]
[ROW][C]37[/C][C]0.991465181373654[/C][C]0.0170696372526914[/C][C]0.00853481862634572[/C][/ROW]
[ROW][C]38[/C][C]0.997886945163494[/C][C]0.00422610967301145[/C][C]0.00211305483650572[/C][/ROW]
[ROW][C]39[/C][C]0.999082813263119[/C][C]0.00183437347376161[/C][C]0.000917186736880804[/C][/ROW]
[ROW][C]40[/C][C]0.999713382961995[/C][C]0.000573234076009103[/C][C]0.000286617038004551[/C][/ROW]
[ROW][C]41[/C][C]0.999822874233395[/C][C]0.000354251533209498[/C][C]0.000177125766604749[/C][/ROW]
[ROW][C]42[/C][C]0.999852035132562[/C][C]0.000295929734876268[/C][C]0.000147964867438134[/C][/ROW]
[ROW][C]43[/C][C]0.999900940043499[/C][C]0.000198119913001385[/C][C]9.90599565006923e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999977355078343[/C][C]4.52898433142659e-05[/C][C]2.2644921657133e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999991736075228[/C][C]1.65278495438306e-05[/C][C]8.26392477191528e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999994993172259[/C][C]1.00136554828529e-05[/C][C]5.00682774142644e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999995355176082[/C][C]9.2896478358901e-06[/C][C]4.64482391794505e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999994869625329[/C][C]1.02607493417068e-05[/C][C]5.13037467085339e-06[/C][/ROW]
[ROW][C]49[/C][C]0.999995571327786[/C][C]8.85734442733546e-06[/C][C]4.42867221366773e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999989979372124[/C][C]2.00412557524362e-05[/C][C]1.00206278762181e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999968100049962[/C][C]6.37999000751802e-05[/C][C]3.18999500375901e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999962990203306[/C][C]7.40195933879418e-05[/C][C]3.70097966939709e-05[/C][/ROW]
[ROW][C]53[/C][C]0.999961919381369[/C][C]7.61612372612063e-05[/C][C]3.80806186306032e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999962965912989[/C][C]7.40681740213596e-05[/C][C]3.70340870106798e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999893777355041[/C][C]0.000212445289917661[/C][C]0.00010622264495883[/C][/ROW]
[ROW][C]56[/C][C]0.999628490533398[/C][C]0.000743018933204461[/C][C]0.00037150946660223[/C][/ROW]
[ROW][C]57[/C][C]0.998019172925257[/C][C]0.00396165414948536[/C][C]0.00198082707474268[/C][/ROW]
[ROW][C]58[/C][C]0.990802471123484[/C][C]0.0183950577530331[/C][C]0.00919752887651653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186132&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.04778714980753670.09557429961507340.952212850192463
130.0205657660452350.04113153209046990.979434233954765
140.008413962356553910.01682792471310780.991586037643446
150.005849433918460820.01169886783692160.994150566081539
160.007792755321149140.01558551064229830.992207244678851
170.03439027197038630.06878054394077270.965609728029614
180.02820987419713760.05641974839427520.971790125802862
190.05402295542799180.1080459108559840.945977044572008
200.2116953981695490.4233907963390990.788304601830451
210.5082561149168120.9834877701663760.491743885083188
220.6001581352511370.7996837294977260.399841864748863
230.6039086568012060.7921826863975870.396091343198794
240.5616684438407420.8766631123185150.438331556159258
250.5687468490115920.8625063019768150.431253150988408
260.5051058496262190.9897883007475630.494894150373781
270.4592616071594830.9185232143189660.540738392840517
280.5672161541739740.8655676916520520.432783845826026
290.6555245982734990.6889508034530020.344475401726501
300.7738248534910690.4523502930178630.226175146508931
310.8367101373561670.3265797252876650.163289862643833
320.8221243271897410.3557513456205170.177875672810259
330.8884823760497730.2230352479004550.111517623950227
340.9156405397713680.1687189204572640.0843594602286321
350.939450554613360.121098890773280.0605494453866398
360.9767307680600580.04653846387988450.0232692319399423
370.9914651813736540.01706963725269140.00853481862634572
380.9978869451634940.004226109673011450.00211305483650572
390.9990828132631190.001834373473761610.000917186736880804
400.9997133829619950.0005732340760091030.000286617038004551
410.9998228742333950.0003542515332094980.000177125766604749
420.9998520351325620.0002959297348762680.000147964867438134
430.9999009400434990.0001981199130013859.90599565006923e-05
440.9999773550783434.52898433142659e-052.2644921657133e-05
450.9999917360752281.65278495438306e-058.26392477191528e-06
460.9999949931722591.00136554828529e-055.00682774142644e-06
470.9999953551760829.2896478358901e-064.64482391794505e-06
480.9999948696253291.02607493417068e-055.13037467085339e-06
490.9999955713277868.85734442733546e-064.42867221366773e-06
500.9999899793721242.00412557524362e-051.00206278762181e-05
510.9999681000499626.37999000751802e-053.18999500375901e-05
520.9999629902033067.40195933879418e-053.70097966939709e-05
530.9999619193813697.61612372612063e-053.80806186306032e-05
540.9999629659129897.40681740213596e-053.70340870106798e-05
550.9998937773550410.0002124452899176610.00010622264495883
560.9996284905333980.0007430189332044610.00037150946660223
570.9980191729252570.003961654149485360.00198082707474268
580.9908024711234840.01839505775303310.00919752887651653







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.425531914893617NOK
5% type I error level270.574468085106383NOK
10% type I error level300.638297872340426NOK

\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 & 20 & 0.425531914893617 & NOK \tabularnewline
5% type I error level & 27 & 0.574468085106383 & NOK \tabularnewline
10% type I error level & 30 & 0.638297872340426 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186132&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]20[/C][C]0.425531914893617[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.574468085106383[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.638297872340426[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186132&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186132&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 testsOK/NOK
1% type I error level200.425531914893617NOK
5% type I error level270.574468085106383NOK
10% type I error level300.638297872340426NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}