Free Statistics

of Irreproducible Research!

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, 06 Dec 2012 06:38:26 -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/Dec/06/t1354793951j8es73xr2l44x6p.htm/, Retrieved Fri, 29 Mar 2024 12:47:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197021, Retrieved Fri, 29 Mar 2024 12:47:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-11-04 18:57:02] [235928acca9c96310100390b3cde8f3b]
-   P     [Multiple Regression] [] [2012-12-06 11:38:26] [c138fbd6e7c7784b8fd4dab04951100b] [Current]
Feedback Forum

Post a new message
Dataseries X:
1	1901	61	17	56	84	4	21	51	9
2	2509	74	19	73	47	3	15	45	9
3	2114	57	18	62	63	3	17	44	9
4	1331	50	15	42	28	3	20	42	9
5	1399	48	15	59	22	2	12	38	9
6	7333	2	12	27	18	6	4	38	9
7	1170	31	20	78	27	5	11	35	9
8	1507	61	14	56	37	5	12	35	9
9	1107	36	15	59	20	5	9	34	9
10	2051	46	13	51	67	5	14	33	9
11	1290	30	17	47	28	4	11	32	9
12	820	49	10	35	45	3	14	31	9
13	1502	14	13	47	15	5	4	30	9
14	1451	12	12	47	23	6	7	30	9
15	1178	54	16	55	30	6	9	30	9
16	1514	44	15	54	27	2	14	29	9
17	883	40	15	60	43	5	13	29	9
18	1405	57	15	55	36	5	11	29	9
19	927	29	12	48	28	5	9	28	9
20	1352	32	13	47	28	9	8	27	9
21	1314	28	12	47	22	4	9	27	9
22	1307	40	15	52	27	4	11	27	9
23	1243	54	12	48	24	5	7	26	9
24	1232	56	12	48	52	3	15	26	9
25	1097	19	9	27	12	0	4	26	9
26	1100	67	12	12	24	5	10	26	9
27	1316	25	13	51	10	3	10	26	9
28	903	42	16	58	71	4	13	25	9
29	929	28	15	60	12	2	10	25	9
30	1049	57	13	46	24	5	10	25	9
31	1372	28	12	45	22	11	6	24	9
32	1470	35	13	42	21	5	8	24	9
33	821	10	12	41	13	3	7	24	9
34	1239	30	12	47	28	4	11	24	9
35	1384	23	8	32	19	5	10	24	9
36	820	32	15	56	29	5	11	24	9
37	1462	24	12	42	12	2	10	24	9
38	1202	42	12	41	32	6	8	23	9
39	1091	33	12	47	21	3	10	23	9
40	1228	19	14	47	19	4	5	23	9
41	707	17	15	49	15	8	5	23	9
42	868	49	15	52	14	14	5	23	9
43	1165	30	12	42	34	11	9	22	9
44	1106	3	13	55	8	8	2	22	9
45	1429	56	12	48	27	3	9	22	9
46	1671	37	13	48	31	3	13	22	9
47	1579	26	12	38	21	11	7	22	9
48	774	19	12	48	10	3	5	21	10
49	934	22	13	50	21	4	7	21	10
50	825	53	12	39	19	3	8	21	10
51	1375	35	12	48	27	5	8	21	10
52	968	12	9	36	17	6	5	21	10
53	1156	34	13	49	30	8	5	21	10
54	1374	28	13	39	19	3	10	21	10
55	1224	38	12	41	17	3	5	21	10
56	804	38	15	45	24	5	10	21	10
57	998	45	15	60	36	5	10	21	10
58	1112	15	13	45	16	3	7	21	10
59	1153	35	14	41	16	3	10	20	10
60	613	27	14	52	30	3	9	20	10
61	729	23	12	46	18	5	10	20	10
62	813	33	12	39	26	3	10	20	10
63	912	23	9	32	17	3	5	20	10
64	1178	26	14	52	28	6	8	20	10
65	1201	32	16	54	20	4	6	19	10
66	1165	35	15	51	27	3	7	19	10
67	705	18	13	52	13	13	6	18	10
68	814	18	16	57	10	5	3	17	10
69	1082	41	12	47	29	6	9	17	10
70	885	39	12	45	34	5	11	17	10
71	837	56	12	41	30	3	9	17	10
72	586	35	12	43	16	4	10	16	10
73	913	37	10	31	22	4	9	16	10
74	547	26	15	32	22	7	7	15	10
75	758	33	12	41	31	4	6	15	10
76	848	7	9	27	10	5	6	15	10
77	634	16	10	40	7	7	5	15	10
78	501	13	13	46	10	3	5	15	10
79	849	54	12	32	55	6	8	15	10
80	733	30	13	9	25	8	7	15	10
81	634	9	16	64	9	5	5	15	10
82	1010	35	15	30	31	5	10	15	10
83	778	0	12	46	0	0	0	15	10
84	480	40	12	37	24	3	10	15	10
85	848	22	12	22	14	5	6	15	10
86	714	29	12	20	11	3	6	14	10
87	871	25	12	21	8	8	4	14	10
88	776	17	14	44	9	9	3	14	10
89	815	32	12	24	18	9	7	14	10
90	811	40	12	33	14	4	5	14	10
91	529	24	12	45	27	2	8	13	10
92	642	18	13	35	10	0	0	13	10
93	562	15	8	31	16	3	5	13	10
94	626	17	16	20	13	7	5	13	10
95	636	28	12	13	10	5	5	13	11
96	935	18	11	33	16	3	5	13	11
97	473	16	15	58	11	3	6	12	11
98	836	28	13	26	8	3	5	12	11
99	938	17	12	36	29	7	6	12	11
100	656	25	13	32	12	4	4	12	11
101	566	2	13	34	1	0	0	12	11
102	765	10	12	15	26	5	8	12	11
103	705	9	12	40	5	5	2	11	11
104	558	7	12	37	5	5	2	11	11
105	582	27	14	26	24	6	8	11	11
106	608	25	12	31	19	6	3	11	11
107	567	16	16	47	10	5	3	11	11
108	434	28	8	21	6	6	3	11	11
109	479	7	8	21	61	0	3	11	11
110	488	0	5	9	25	25	1	10	11
111	507	16	9	28	7	2	2	10	11
112	394	10	11	24	10	5	2	10	11
113	504	0	4	15	3	3	1	9	11
114	368	2	8	19	1	1	2	9	11
115	386	5	13	35	38	5	7	9	11
116	451	36	13	45	13	4	4	9	11
117	580	10	12	20	2	0	1	9	11
118	565	43	13	1	8	4	6	9	11
119	510	14	12	29	30	10	3	9	11
120	495	12	12	33	11	6	2	8	11
121	596	15	10	32	69	23	3	8	11
122	412	8	12	11	2	0	2	8	11
123	338	39	5	10	23	6	5	7	11
124	446	10	13	18	8	4	4	7	11
125	418	0	12	41	0	0	0	7	11
126	335	7	6	0	2	0	0	6	11
127	349	10	9	10	4	2	3	6	11
128	308	3	12	24	4	4	2	5	11
129	466	8	15	28	0	0	0	5	11
130	228	0	11	38	9	9	1	5	11
131	428	8	3	4	5	5	3	5	11
132	242	1	8	25	0	0	0	5	11
133	352	0	12	40	0	0	0	5	11
134	244	8	0	0	13	4	4	5	11
135	269	3	9	23	1	0	1	5	11
136	242	0	4	13	0	0	0	4	11
137	291	0	14	6	39	0	2	4	11
138	213	0	9	31	10	0	0	4	11
139	135	0	0	0	1	0	1	3	11
140	210	3	1	3	3	3	3	3	11




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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 time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -84.0566908132394 -0.000303517913451653X1[t] -0.0754939742248655x2[t] + 0.221797591504753x3[t] -0.192347954739755x4[t] + 0.10431171875053x5[t] -0.243984902971775x6[t] -0.0105994704387667x7[t] -2.39503071941112x8[t] + 20.1648396846171x9[t] + 2.19785317659788M1[t] + 1.1316588093391M2[t] + 2.75119613746961M3[t] + 2.13168415609254M4[t] + 2.55832412088507M5[t] + 2.9171779986621M6[t] + 2.32598949476699M7[t] + 2.98073849591366M8[t] + 3.00379920147777M9[t] + 2.52594848897842M10[t] + 1.53117546268288M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -84.0566908132394 -0.000303517913451653X1[t] -0.0754939742248655x2[t] +  0.221797591504753x3[t] -0.192347954739755x4[t] +  0.10431171875053x5[t] -0.243984902971775x6[t] -0.0105994704387667x7[t] -2.39503071941112x8[t] +  20.1648396846171x9[t] +  2.19785317659788M1[t] +  1.1316588093391M2[t] +  2.75119613746961M3[t] +  2.13168415609254M4[t] +  2.55832412088507M5[t] +  2.9171779986621M6[t] +  2.32598949476699M7[t] +  2.98073849591366M8[t] +  3.00379920147777M9[t] +  2.52594848897842M10[t] +  1.53117546268288M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -84.0566908132394 -0.000303517913451653X1[t] -0.0754939742248655x2[t] +  0.221797591504753x3[t] -0.192347954739755x4[t] +  0.10431171875053x5[t] -0.243984902971775x6[t] -0.0105994704387667x7[t] -2.39503071941112x8[t] +  20.1648396846171x9[t] +  2.19785317659788M1[t] +  1.1316588093391M2[t] +  2.75119613746961M3[t] +  2.13168415609254M4[t] +  2.55832412088507M5[t] +  2.9171779986621M6[t] +  2.32598949476699M7[t] +  2.98073849591366M8[t] +  3.00379920147777M9[t] +  2.52594848897842M10[t] +  1.53117546268288M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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
Y[t] = -84.0566908132394 -0.000303517913451653X1[t] -0.0754939742248655x2[t] + 0.221797591504753x3[t] -0.192347954739755x4[t] + 0.10431171875053x5[t] -0.243984902971775x6[t] -0.0105994704387667x7[t] -2.39503071941112x8[t] + 20.1648396846171x9[t] + 2.19785317659788M1[t] + 1.1316588093391M2[t] + 2.75119613746961M3[t] + 2.13168415609254M4[t] + 2.55832412088507M5[t] + 2.9171779986621M6[t] + 2.32598949476699M7[t] + 2.98073849591366M8[t] + 3.00379920147777M9[t] + 2.52594848897842M10[t] + 1.53117546268288M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-84.056690813239418.758146-4.48111.7e-059e-06
X1-0.0003035179134516530.001353-0.22430.82290.41145
x2-0.07549397422486550.06277-1.20270.2314740.115737
x30.2217975915047530.2908350.76260.4471980.223599
x4-0.1923479547397550.069333-2.77430.0064260.003213
x50.104311718750530.0614061.69870.0919830.045991
x6-0.2439849029717750.188166-1.29660.1972620.098631
x7-0.01059947043876670.348934-0.03040.9758170.487909
x8-2.395030719411120.206107-11.620300
x920.16483968461711.63545412.329800
M12.197853176597883.1056040.70770.4805120.240256
M21.13165880933913.1522940.3590.7202350.360117
M32.751196137469613.0374410.90580.3668930.183446
M42.131684156092543.0601880.69660.487420.24371
M52.558324120885073.0957070.82640.4102250.205113
M62.91717799866213.1568470.92410.3573150.178657
M72.325989494766993.0530880.76180.4476580.223829
M82.980738495913663.1040020.96030.3388570.169428
M93.003799201477773.1174570.96350.337230.168615
M102.525948488978423.1767030.79510.4281110.214055
M111.531175462682883.1355480.48830.6262160.313108

\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) & -84.0566908132394 & 18.758146 & -4.4811 & 1.7e-05 & 9e-06 \tabularnewline
X1 & -0.000303517913451653 & 0.001353 & -0.2243 & 0.8229 & 0.41145 \tabularnewline
x2 & -0.0754939742248655 & 0.06277 & -1.2027 & 0.231474 & 0.115737 \tabularnewline
x3 & 0.221797591504753 & 0.290835 & 0.7626 & 0.447198 & 0.223599 \tabularnewline
x4 & -0.192347954739755 & 0.069333 & -2.7743 & 0.006426 & 0.003213 \tabularnewline
x5 & 0.10431171875053 & 0.061406 & 1.6987 & 0.091983 & 0.045991 \tabularnewline
x6 & -0.243984902971775 & 0.188166 & -1.2966 & 0.197262 & 0.098631 \tabularnewline
x7 & -0.0105994704387667 & 0.348934 & -0.0304 & 0.975817 & 0.487909 \tabularnewline
x8 & -2.39503071941112 & 0.206107 & -11.6203 & 0 & 0 \tabularnewline
x9 & 20.1648396846171 & 1.635454 & 12.3298 & 0 & 0 \tabularnewline
M1 & 2.19785317659788 & 3.105604 & 0.7077 & 0.480512 & 0.240256 \tabularnewline
M2 & 1.1316588093391 & 3.152294 & 0.359 & 0.720235 & 0.360117 \tabularnewline
M3 & 2.75119613746961 & 3.037441 & 0.9058 & 0.366893 & 0.183446 \tabularnewline
M4 & 2.13168415609254 & 3.060188 & 0.6966 & 0.48742 & 0.24371 \tabularnewline
M5 & 2.55832412088507 & 3.095707 & 0.8264 & 0.410225 & 0.205113 \tabularnewline
M6 & 2.9171779986621 & 3.156847 & 0.9241 & 0.357315 & 0.178657 \tabularnewline
M7 & 2.32598949476699 & 3.053088 & 0.7618 & 0.447658 & 0.223829 \tabularnewline
M8 & 2.98073849591366 & 3.104002 & 0.9603 & 0.338857 & 0.169428 \tabularnewline
M9 & 3.00379920147777 & 3.117457 & 0.9635 & 0.33723 & 0.168615 \tabularnewline
M10 & 2.52594848897842 & 3.176703 & 0.7951 & 0.428111 & 0.214055 \tabularnewline
M11 & 1.53117546268288 & 3.135548 & 0.4883 & 0.626216 & 0.313108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&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]-84.0566908132394[/C][C]18.758146[/C][C]-4.4811[/C][C]1.7e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]X1[/C][C]-0.000303517913451653[/C][C]0.001353[/C][C]-0.2243[/C][C]0.8229[/C][C]0.41145[/C][/ROW]
[ROW][C]x2[/C][C]-0.0754939742248655[/C][C]0.06277[/C][C]-1.2027[/C][C]0.231474[/C][C]0.115737[/C][/ROW]
[ROW][C]x3[/C][C]0.221797591504753[/C][C]0.290835[/C][C]0.7626[/C][C]0.447198[/C][C]0.223599[/C][/ROW]
[ROW][C]x4[/C][C]-0.192347954739755[/C][C]0.069333[/C][C]-2.7743[/C][C]0.006426[/C][C]0.003213[/C][/ROW]
[ROW][C]x5[/C][C]0.10431171875053[/C][C]0.061406[/C][C]1.6987[/C][C]0.091983[/C][C]0.045991[/C][/ROW]
[ROW][C]x6[/C][C]-0.243984902971775[/C][C]0.188166[/C][C]-1.2966[/C][C]0.197262[/C][C]0.098631[/C][/ROW]
[ROW][C]x7[/C][C]-0.0105994704387667[/C][C]0.348934[/C][C]-0.0304[/C][C]0.975817[/C][C]0.487909[/C][/ROW]
[ROW][C]x8[/C][C]-2.39503071941112[/C][C]0.206107[/C][C]-11.6203[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x9[/C][C]20.1648396846171[/C][C]1.635454[/C][C]12.3298[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]2.19785317659788[/C][C]3.105604[/C][C]0.7077[/C][C]0.480512[/C][C]0.240256[/C][/ROW]
[ROW][C]M2[/C][C]1.1316588093391[/C][C]3.152294[/C][C]0.359[/C][C]0.720235[/C][C]0.360117[/C][/ROW]
[ROW][C]M3[/C][C]2.75119613746961[/C][C]3.037441[/C][C]0.9058[/C][C]0.366893[/C][C]0.183446[/C][/ROW]
[ROW][C]M4[/C][C]2.13168415609254[/C][C]3.060188[/C][C]0.6966[/C][C]0.48742[/C][C]0.24371[/C][/ROW]
[ROW][C]M5[/C][C]2.55832412088507[/C][C]3.095707[/C][C]0.8264[/C][C]0.410225[/C][C]0.205113[/C][/ROW]
[ROW][C]M6[/C][C]2.9171779986621[/C][C]3.156847[/C][C]0.9241[/C][C]0.357315[/C][C]0.178657[/C][/ROW]
[ROW][C]M7[/C][C]2.32598949476699[/C][C]3.053088[/C][C]0.7618[/C][C]0.447658[/C][C]0.223829[/C][/ROW]
[ROW][C]M8[/C][C]2.98073849591366[/C][C]3.104002[/C][C]0.9603[/C][C]0.338857[/C][C]0.169428[/C][/ROW]
[ROW][C]M9[/C][C]3.00379920147777[/C][C]3.117457[/C][C]0.9635[/C][C]0.33723[/C][C]0.168615[/C][/ROW]
[ROW][C]M10[/C][C]2.52594848897842[/C][C]3.176703[/C][C]0.7951[/C][C]0.428111[/C][C]0.214055[/C][/ROW]
[ROW][C]M11[/C][C]1.53117546268288[/C][C]3.135548[/C][C]0.4883[/C][C]0.626216[/C][C]0.313108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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)-84.056690813239418.758146-4.48111.7e-059e-06
X1-0.0003035179134516530.001353-0.22430.82290.41145
x2-0.07549397422486550.06277-1.20270.2314740.115737
x30.2217975915047530.2908350.76260.4471980.223599
x4-0.1923479547397550.069333-2.77430.0064260.003213
x50.104311718750530.0614061.69870.0919830.045991
x6-0.2439849029717750.188166-1.29660.1972620.098631
x7-0.01059947043876670.348934-0.03040.9758170.487909
x8-2.395030719411120.206107-11.620300
x920.16483968461711.63545412.329800
M12.197853176597883.1056040.70770.4805120.240256
M21.13165880933913.1522940.3590.7202350.360117
M32.751196137469613.0374410.90580.3668930.183446
M42.131684156092543.0601880.69660.487420.24371
M52.558324120885073.0957070.82640.4102250.205113
M62.91717799866213.1568470.92410.3573150.178657
M72.325989494766993.0530880.76180.4476580.223829
M82.980738495913663.1040020.96030.3388570.169428
M93.003799201477773.1174570.96350.337230.168615
M102.525948488978423.1767030.79510.4281110.214055
M111.531175462682883.1355480.48830.6262160.313108







Multiple Linear Regression - Regression Statistics
Multiple R0.986482074141833
R-squared0.973146882603172
Adjusted R-squared0.968633753628915
F-TEST (value)215.625763888882
F-TEST (DF numerator)20
F-TEST (DF denominator)119
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.18313826126398
Sum Squared Residuals6140.09955837171

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.986482074141833 \tabularnewline
R-squared & 0.973146882603172 \tabularnewline
Adjusted R-squared & 0.968633753628915 \tabularnewline
F-TEST (value) & 215.625763888882 \tabularnewline
F-TEST (DF numerator) & 20 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.18313826126398 \tabularnewline
Sum Squared Residuals & 6140.09955837171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.986482074141833[/C][/ROW]
[ROW][C]R-squared[/C][C]0.973146882603172[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.968633753628915[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]215.625763888882[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]20[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/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]7.18313826126398[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6140.09955837171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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 R0.986482074141833
R-squared0.973146882603172
Adjusted R-squared0.968633753628915
F-TEST (value)215.625763888882
F-TEST (DF numerator)20
F-TEST (DF denominator)119
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.18313826126398
Sum Squared Residuals6140.09955837171







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11-27.14123767214528.141237672145
22-21.381480194970423.3814801949704
33-12.421806540029115.4218065400291
44-7.9862869840812211.9862869840812
55-1.41618028790636.4161802879063
664.795672159571221.20432784042878
774.144060580956612.85593941904339
886.365091990836731.63490800916327
998.695192856657010.304807143342989
101015.5157540950114-5.51575409501138
111116.219100975857-5.21910097585698
121218.5323021683933-6.53230216839332
131320.406366598864-7.406366598864
141419.8435524378524-5.84355243785239
151518.432593057388-3.43259305738799
161621.4416269854276-5.44162698542761
171722.155307183601-5.15530718360099
181821.3250838320561-3.32508383205614
191925.2555869880356-6.2555869880356
202027.398695077498-7.39869507749802
212128.0969225010849-7.09692250108493
222226.9192813769719-4.91928137697194
232326.8715254443768-3.87152544437677
242428.5166028977404-4.51660289774038
252533.5987024477154-8.59870244771543
262632.4267181470482-6.42671814704822
272728.899275623408-1.89927562340796
282834.9229383187051-6.92293831870505
292930.1574857668903-1.15748576689027
303031.0596543419701-1.05965434197009
313133.2952009136207-2.29520091362067
323235.5289875536016-3.52898755360163
333337.2710066302306-4.2710066302306
343435.2806112135037-1.2806112135037
353535.5961239631213-0.596123963121266
363631.5254367794474.47456322055299
373735.12911680184521.87088319815475
383836.50181793418821.49818206581177
393937.24373065207911.75626934792091
404037.68353655043782.31646344956223
414136.86321249172444.13678750827563
424232.61212780943959.38787219056046
434339.7940886899873.20591131001301
444438.32040304645955.67959695354046
454538.4965358017826.50346419821802
464639.97626584925166.02373415074841
474738.6101325547138.38986744528697
484859.3137869469594-11.3137869469594
494961.9559420791622-12.9559420791622
505060.5013098691633-10.5013098691633
515161.9281962323459-10.9281962323459
525263.5560564521176-11.5560564521176
535361.5195171084497-8.51951710844967
545464.2081457302502-10.2081457302502
555562.1454255848327-7.14542558483268
565663.0128679383006-7.01286793830061
575760.8151096529911-3.81510965299108
585863.4426351054403-5.44263510544028
595964.2799600787545-5.27996007875449
606062.8717721139433-2.87177211394332
616164.2965757535206-3.29657575352061
626265.1188453784093-3.11884537840931
636367.2585089674599-4.25850896745987
646463.97748394718290.0225160528170822
656566.0727841443748-1.07278414437482
666667.5348964808628-1.53489648086275
676766.23619773885360.76380226114644
686870.6272894864995-2.62728948649952
696971.5432760961209-2.5432760961209
707072.4052468263476-2.40524682634765
717171.0029587888531-0.00295878885305984
727271.42872615518240.571273844817625
737375.8773910824413-2.87739108244134
747478.3536329421367-4.35363294213672
757578.6655054518917-3.66550545189168
767679.5744677832599-3.57446778325987
777776.31758350163880.682416498361166
787877.74346699879390.256533001206045
797980.3527493161922-1.3527493161922
808083.8936404291945-3.89364042919449
818174.70254428052466.29745571947539
828280.70762083268571.29237916731434
838376.7748489478116.22515105218901
848475.71102628370968.28897371629041
858580.55260661360744.44739338639261
868681.95338710575974.04661289424035
878782.1232393334054.87676066659497
888878.02703185781749.97296814218255
898981.60919650939777.39080349060232
909080.45805765310619.54194234689391
919183.05944378932047.94055621067962
929285.07760260119326.92239739880679
939384.85274877533738.14725122466269
949486.8058184339647.19418156603604
9595105.776696163529-10.7766961635288
9696101.954792019119-5.95479201911936
9797102.385222572926-5.38522257292647
9898105.712127195236-7.71212719523618
9999107.189869285201-8.18986928520112
100100106.023041403116-6.02304140311609
101101107.699572065198-6.69957206519802
102102112.129960323344-10.1299603233441
103103107.09185944827-4.09185944826964
104104108.519257395363-4.51925739536271
105105111.258917802309-6.25891780230916
106106109.050267374243-3.0502673742427
107107105.8621868748221.13781312517759
108108106.0308859171521.96911408284808
109109117.001508195476-8.00150819547648
110110110.665207879909-0.665207879908682
111111112.027096366452-1.02709636645194
112112112.688813203949-0.688813203948884
113113116.178972357175-3.17897235717464
114114116.814662167795-2.81466216779508
115115116.85354573016-1.85354573015959
116116110.8927636640885.10723633591167
117117117.2867242826-0.286724282600402
118118117.7954672693650.204532730635414
119119114.2619194626094.73808053739058
120120113.5165400435966.48345995640405
121121117.0977456265633.90225437343734
122122119.7321256900012.26787430999851
123123120.7634439409882.23655605901164
124124121.4699678667242.53003213327603
125125118.1980892684616.80191073153939
126126127.212812065694-1.21281206569369
127127124.8216608356942.17833916430644
128128125.907493682932.09250631707016
129129125.9810213203623.01897867963798
130130122.1010316232177.89896837678346
131131125.7445467455535.25545325444717
132132122.5981286747579.40187132524254
133133122.84005990002310.1599400999768
134134126.5727556152667.42724438473383
135135125.890347629419.10965237058982
136136128.6213226153447.37867738465597
137137136.6444598909960.355540109003613
138138128.1054604371179.89453956288284
139139132.9501803840786.04981961592151
140140132.4559071340357.54409286596462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & -27.141237672145 & 28.141237672145 \tabularnewline
2 & 2 & -21.3814801949704 & 23.3814801949704 \tabularnewline
3 & 3 & -12.4218065400291 & 15.4218065400291 \tabularnewline
4 & 4 & -7.98628698408122 & 11.9862869840812 \tabularnewline
5 & 5 & -1.4161802879063 & 6.4161802879063 \tabularnewline
6 & 6 & 4.79567215957122 & 1.20432784042878 \tabularnewline
7 & 7 & 4.14406058095661 & 2.85593941904339 \tabularnewline
8 & 8 & 6.36509199083673 & 1.63490800916327 \tabularnewline
9 & 9 & 8.69519285665701 & 0.304807143342989 \tabularnewline
10 & 10 & 15.5157540950114 & -5.51575409501138 \tabularnewline
11 & 11 & 16.219100975857 & -5.21910097585698 \tabularnewline
12 & 12 & 18.5323021683933 & -6.53230216839332 \tabularnewline
13 & 13 & 20.406366598864 & -7.406366598864 \tabularnewline
14 & 14 & 19.8435524378524 & -5.84355243785239 \tabularnewline
15 & 15 & 18.432593057388 & -3.43259305738799 \tabularnewline
16 & 16 & 21.4416269854276 & -5.44162698542761 \tabularnewline
17 & 17 & 22.155307183601 & -5.15530718360099 \tabularnewline
18 & 18 & 21.3250838320561 & -3.32508383205614 \tabularnewline
19 & 19 & 25.2555869880356 & -6.2555869880356 \tabularnewline
20 & 20 & 27.398695077498 & -7.39869507749802 \tabularnewline
21 & 21 & 28.0969225010849 & -7.09692250108493 \tabularnewline
22 & 22 & 26.9192813769719 & -4.91928137697194 \tabularnewline
23 & 23 & 26.8715254443768 & -3.87152544437677 \tabularnewline
24 & 24 & 28.5166028977404 & -4.51660289774038 \tabularnewline
25 & 25 & 33.5987024477154 & -8.59870244771543 \tabularnewline
26 & 26 & 32.4267181470482 & -6.42671814704822 \tabularnewline
27 & 27 & 28.899275623408 & -1.89927562340796 \tabularnewline
28 & 28 & 34.9229383187051 & -6.92293831870505 \tabularnewline
29 & 29 & 30.1574857668903 & -1.15748576689027 \tabularnewline
30 & 30 & 31.0596543419701 & -1.05965434197009 \tabularnewline
31 & 31 & 33.2952009136207 & -2.29520091362067 \tabularnewline
32 & 32 & 35.5289875536016 & -3.52898755360163 \tabularnewline
33 & 33 & 37.2710066302306 & -4.2710066302306 \tabularnewline
34 & 34 & 35.2806112135037 & -1.2806112135037 \tabularnewline
35 & 35 & 35.5961239631213 & -0.596123963121266 \tabularnewline
36 & 36 & 31.525436779447 & 4.47456322055299 \tabularnewline
37 & 37 & 35.1291168018452 & 1.87088319815475 \tabularnewline
38 & 38 & 36.5018179341882 & 1.49818206581177 \tabularnewline
39 & 39 & 37.2437306520791 & 1.75626934792091 \tabularnewline
40 & 40 & 37.6835365504378 & 2.31646344956223 \tabularnewline
41 & 41 & 36.8632124917244 & 4.13678750827563 \tabularnewline
42 & 42 & 32.6121278094395 & 9.38787219056046 \tabularnewline
43 & 43 & 39.794088689987 & 3.20591131001301 \tabularnewline
44 & 44 & 38.3204030464595 & 5.67959695354046 \tabularnewline
45 & 45 & 38.496535801782 & 6.50346419821802 \tabularnewline
46 & 46 & 39.9762658492516 & 6.02373415074841 \tabularnewline
47 & 47 & 38.610132554713 & 8.38986744528697 \tabularnewline
48 & 48 & 59.3137869469594 & -11.3137869469594 \tabularnewline
49 & 49 & 61.9559420791622 & -12.9559420791622 \tabularnewline
50 & 50 & 60.5013098691633 & -10.5013098691633 \tabularnewline
51 & 51 & 61.9281962323459 & -10.9281962323459 \tabularnewline
52 & 52 & 63.5560564521176 & -11.5560564521176 \tabularnewline
53 & 53 & 61.5195171084497 & -8.51951710844967 \tabularnewline
54 & 54 & 64.2081457302502 & -10.2081457302502 \tabularnewline
55 & 55 & 62.1454255848327 & -7.14542558483268 \tabularnewline
56 & 56 & 63.0128679383006 & -7.01286793830061 \tabularnewline
57 & 57 & 60.8151096529911 & -3.81510965299108 \tabularnewline
58 & 58 & 63.4426351054403 & -5.44263510544028 \tabularnewline
59 & 59 & 64.2799600787545 & -5.27996007875449 \tabularnewline
60 & 60 & 62.8717721139433 & -2.87177211394332 \tabularnewline
61 & 61 & 64.2965757535206 & -3.29657575352061 \tabularnewline
62 & 62 & 65.1188453784093 & -3.11884537840931 \tabularnewline
63 & 63 & 67.2585089674599 & -4.25850896745987 \tabularnewline
64 & 64 & 63.9774839471829 & 0.0225160528170822 \tabularnewline
65 & 65 & 66.0727841443748 & -1.07278414437482 \tabularnewline
66 & 66 & 67.5348964808628 & -1.53489648086275 \tabularnewline
67 & 67 & 66.2361977388536 & 0.76380226114644 \tabularnewline
68 & 68 & 70.6272894864995 & -2.62728948649952 \tabularnewline
69 & 69 & 71.5432760961209 & -2.5432760961209 \tabularnewline
70 & 70 & 72.4052468263476 & -2.40524682634765 \tabularnewline
71 & 71 & 71.0029587888531 & -0.00295878885305984 \tabularnewline
72 & 72 & 71.4287261551824 & 0.571273844817625 \tabularnewline
73 & 73 & 75.8773910824413 & -2.87739108244134 \tabularnewline
74 & 74 & 78.3536329421367 & -4.35363294213672 \tabularnewline
75 & 75 & 78.6655054518917 & -3.66550545189168 \tabularnewline
76 & 76 & 79.5744677832599 & -3.57446778325987 \tabularnewline
77 & 77 & 76.3175835016388 & 0.682416498361166 \tabularnewline
78 & 78 & 77.7434669987939 & 0.256533001206045 \tabularnewline
79 & 79 & 80.3527493161922 & -1.3527493161922 \tabularnewline
80 & 80 & 83.8936404291945 & -3.89364042919449 \tabularnewline
81 & 81 & 74.7025442805246 & 6.29745571947539 \tabularnewline
82 & 82 & 80.7076208326857 & 1.29237916731434 \tabularnewline
83 & 83 & 76.774848947811 & 6.22515105218901 \tabularnewline
84 & 84 & 75.7110262837096 & 8.28897371629041 \tabularnewline
85 & 85 & 80.5526066136074 & 4.44739338639261 \tabularnewline
86 & 86 & 81.9533871057597 & 4.04661289424035 \tabularnewline
87 & 87 & 82.123239333405 & 4.87676066659497 \tabularnewline
88 & 88 & 78.0270318578174 & 9.97296814218255 \tabularnewline
89 & 89 & 81.6091965093977 & 7.39080349060232 \tabularnewline
90 & 90 & 80.4580576531061 & 9.54194234689391 \tabularnewline
91 & 91 & 83.0594437893204 & 7.94055621067962 \tabularnewline
92 & 92 & 85.0776026011932 & 6.92239739880679 \tabularnewline
93 & 93 & 84.8527487753373 & 8.14725122466269 \tabularnewline
94 & 94 & 86.805818433964 & 7.19418156603604 \tabularnewline
95 & 95 & 105.776696163529 & -10.7766961635288 \tabularnewline
96 & 96 & 101.954792019119 & -5.95479201911936 \tabularnewline
97 & 97 & 102.385222572926 & -5.38522257292647 \tabularnewline
98 & 98 & 105.712127195236 & -7.71212719523618 \tabularnewline
99 & 99 & 107.189869285201 & -8.18986928520112 \tabularnewline
100 & 100 & 106.023041403116 & -6.02304140311609 \tabularnewline
101 & 101 & 107.699572065198 & -6.69957206519802 \tabularnewline
102 & 102 & 112.129960323344 & -10.1299603233441 \tabularnewline
103 & 103 & 107.09185944827 & -4.09185944826964 \tabularnewline
104 & 104 & 108.519257395363 & -4.51925739536271 \tabularnewline
105 & 105 & 111.258917802309 & -6.25891780230916 \tabularnewline
106 & 106 & 109.050267374243 & -3.0502673742427 \tabularnewline
107 & 107 & 105.862186874822 & 1.13781312517759 \tabularnewline
108 & 108 & 106.030885917152 & 1.96911408284808 \tabularnewline
109 & 109 & 117.001508195476 & -8.00150819547648 \tabularnewline
110 & 110 & 110.665207879909 & -0.665207879908682 \tabularnewline
111 & 111 & 112.027096366452 & -1.02709636645194 \tabularnewline
112 & 112 & 112.688813203949 & -0.688813203948884 \tabularnewline
113 & 113 & 116.178972357175 & -3.17897235717464 \tabularnewline
114 & 114 & 116.814662167795 & -2.81466216779508 \tabularnewline
115 & 115 & 116.85354573016 & -1.85354573015959 \tabularnewline
116 & 116 & 110.892763664088 & 5.10723633591167 \tabularnewline
117 & 117 & 117.2867242826 & -0.286724282600402 \tabularnewline
118 & 118 & 117.795467269365 & 0.204532730635414 \tabularnewline
119 & 119 & 114.261919462609 & 4.73808053739058 \tabularnewline
120 & 120 & 113.516540043596 & 6.48345995640405 \tabularnewline
121 & 121 & 117.097745626563 & 3.90225437343734 \tabularnewline
122 & 122 & 119.732125690001 & 2.26787430999851 \tabularnewline
123 & 123 & 120.763443940988 & 2.23655605901164 \tabularnewline
124 & 124 & 121.469967866724 & 2.53003213327603 \tabularnewline
125 & 125 & 118.198089268461 & 6.80191073153939 \tabularnewline
126 & 126 & 127.212812065694 & -1.21281206569369 \tabularnewline
127 & 127 & 124.821660835694 & 2.17833916430644 \tabularnewline
128 & 128 & 125.90749368293 & 2.09250631707016 \tabularnewline
129 & 129 & 125.981021320362 & 3.01897867963798 \tabularnewline
130 & 130 & 122.101031623217 & 7.89896837678346 \tabularnewline
131 & 131 & 125.744546745553 & 5.25545325444717 \tabularnewline
132 & 132 & 122.598128674757 & 9.40187132524254 \tabularnewline
133 & 133 & 122.840059900023 & 10.1599400999768 \tabularnewline
134 & 134 & 126.572755615266 & 7.42724438473383 \tabularnewline
135 & 135 & 125.89034762941 & 9.10965237058982 \tabularnewline
136 & 136 & 128.621322615344 & 7.37867738465597 \tabularnewline
137 & 137 & 136.644459890996 & 0.355540109003613 \tabularnewline
138 & 138 & 128.105460437117 & 9.89453956288284 \tabularnewline
139 & 139 & 132.950180384078 & 6.04981961592151 \tabularnewline
140 & 140 & 132.455907134035 & 7.54409286596462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&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]-27.141237672145[/C][C]28.141237672145[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]-21.3814801949704[/C][C]23.3814801949704[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]-12.4218065400291[/C][C]15.4218065400291[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]-7.98628698408122[/C][C]11.9862869840812[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]-1.4161802879063[/C][C]6.4161802879063[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]4.79567215957122[/C][C]1.20432784042878[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]4.14406058095661[/C][C]2.85593941904339[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]6.36509199083673[/C][C]1.63490800916327[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]8.69519285665701[/C][C]0.304807143342989[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]15.5157540950114[/C][C]-5.51575409501138[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]16.219100975857[/C][C]-5.21910097585698[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]18.5323021683933[/C][C]-6.53230216839332[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]20.406366598864[/C][C]-7.406366598864[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]19.8435524378524[/C][C]-5.84355243785239[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]18.432593057388[/C][C]-3.43259305738799[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]21.4416269854276[/C][C]-5.44162698542761[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]22.155307183601[/C][C]-5.15530718360099[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]21.3250838320561[/C][C]-3.32508383205614[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]25.2555869880356[/C][C]-6.2555869880356[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]27.398695077498[/C][C]-7.39869507749802[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]28.0969225010849[/C][C]-7.09692250108493[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]26.9192813769719[/C][C]-4.91928137697194[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]26.8715254443768[/C][C]-3.87152544437677[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]28.5166028977404[/C][C]-4.51660289774038[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]33.5987024477154[/C][C]-8.59870244771543[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]32.4267181470482[/C][C]-6.42671814704822[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]28.899275623408[/C][C]-1.89927562340796[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]34.9229383187051[/C][C]-6.92293831870505[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]30.1574857668903[/C][C]-1.15748576689027[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]31.0596543419701[/C][C]-1.05965434197009[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.2952009136207[/C][C]-2.29520091362067[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]35.5289875536016[/C][C]-3.52898755360163[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]37.2710066302306[/C][C]-4.2710066302306[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.2806112135037[/C][C]-1.2806112135037[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]35.5961239631213[/C][C]-0.596123963121266[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]31.525436779447[/C][C]4.47456322055299[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]35.1291168018452[/C][C]1.87088319815475[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]36.5018179341882[/C][C]1.49818206581177[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]37.2437306520791[/C][C]1.75626934792091[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]37.6835365504378[/C][C]2.31646344956223[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]36.8632124917244[/C][C]4.13678750827563[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]32.6121278094395[/C][C]9.38787219056046[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]39.794088689987[/C][C]3.20591131001301[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]38.3204030464595[/C][C]5.67959695354046[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]38.496535801782[/C][C]6.50346419821802[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]39.9762658492516[/C][C]6.02373415074841[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]38.610132554713[/C][C]8.38986744528697[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]59.3137869469594[/C][C]-11.3137869469594[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]61.9559420791622[/C][C]-12.9559420791622[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]60.5013098691633[/C][C]-10.5013098691633[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]61.9281962323459[/C][C]-10.9281962323459[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]63.5560564521176[/C][C]-11.5560564521176[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]61.5195171084497[/C][C]-8.51951710844967[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]64.2081457302502[/C][C]-10.2081457302502[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]62.1454255848327[/C][C]-7.14542558483268[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]63.0128679383006[/C][C]-7.01286793830061[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]60.8151096529911[/C][C]-3.81510965299108[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]63.4426351054403[/C][C]-5.44263510544028[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]64.2799600787545[/C][C]-5.27996007875449[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]62.8717721139433[/C][C]-2.87177211394332[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]64.2965757535206[/C][C]-3.29657575352061[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]65.1188453784093[/C][C]-3.11884537840931[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]67.2585089674599[/C][C]-4.25850896745987[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]63.9774839471829[/C][C]0.0225160528170822[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]66.0727841443748[/C][C]-1.07278414437482[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]67.5348964808628[/C][C]-1.53489648086275[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]66.2361977388536[/C][C]0.76380226114644[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]70.6272894864995[/C][C]-2.62728948649952[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]71.5432760961209[/C][C]-2.5432760961209[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]72.4052468263476[/C][C]-2.40524682634765[/C][/ROW]
[ROW][C]71[/C][C]71[/C][C]71.0029587888531[/C][C]-0.00295878885305984[/C][/ROW]
[ROW][C]72[/C][C]72[/C][C]71.4287261551824[/C][C]0.571273844817625[/C][/ROW]
[ROW][C]73[/C][C]73[/C][C]75.8773910824413[/C][C]-2.87739108244134[/C][/ROW]
[ROW][C]74[/C][C]74[/C][C]78.3536329421367[/C][C]-4.35363294213672[/C][/ROW]
[ROW][C]75[/C][C]75[/C][C]78.6655054518917[/C][C]-3.66550545189168[/C][/ROW]
[ROW][C]76[/C][C]76[/C][C]79.5744677832599[/C][C]-3.57446778325987[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]76.3175835016388[/C][C]0.682416498361166[/C][/ROW]
[ROW][C]78[/C][C]78[/C][C]77.7434669987939[/C][C]0.256533001206045[/C][/ROW]
[ROW][C]79[/C][C]79[/C][C]80.3527493161922[/C][C]-1.3527493161922[/C][/ROW]
[ROW][C]80[/C][C]80[/C][C]83.8936404291945[/C][C]-3.89364042919449[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]74.7025442805246[/C][C]6.29745571947539[/C][/ROW]
[ROW][C]82[/C][C]82[/C][C]80.7076208326857[/C][C]1.29237916731434[/C][/ROW]
[ROW][C]83[/C][C]83[/C][C]76.774848947811[/C][C]6.22515105218901[/C][/ROW]
[ROW][C]84[/C][C]84[/C][C]75.7110262837096[/C][C]8.28897371629041[/C][/ROW]
[ROW][C]85[/C][C]85[/C][C]80.5526066136074[/C][C]4.44739338639261[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]81.9533871057597[/C][C]4.04661289424035[/C][/ROW]
[ROW][C]87[/C][C]87[/C][C]82.123239333405[/C][C]4.87676066659497[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]78.0270318578174[/C][C]9.97296814218255[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]81.6091965093977[/C][C]7.39080349060232[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]80.4580576531061[/C][C]9.54194234689391[/C][/ROW]
[ROW][C]91[/C][C]91[/C][C]83.0594437893204[/C][C]7.94055621067962[/C][/ROW]
[ROW][C]92[/C][C]92[/C][C]85.0776026011932[/C][C]6.92239739880679[/C][/ROW]
[ROW][C]93[/C][C]93[/C][C]84.8527487753373[/C][C]8.14725122466269[/C][/ROW]
[ROW][C]94[/C][C]94[/C][C]86.805818433964[/C][C]7.19418156603604[/C][/ROW]
[ROW][C]95[/C][C]95[/C][C]105.776696163529[/C][C]-10.7766961635288[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]101.954792019119[/C][C]-5.95479201911936[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]102.385222572926[/C][C]-5.38522257292647[/C][/ROW]
[ROW][C]98[/C][C]98[/C][C]105.712127195236[/C][C]-7.71212719523618[/C][/ROW]
[ROW][C]99[/C][C]99[/C][C]107.189869285201[/C][C]-8.18986928520112[/C][/ROW]
[ROW][C]100[/C][C]100[/C][C]106.023041403116[/C][C]-6.02304140311609[/C][/ROW]
[ROW][C]101[/C][C]101[/C][C]107.699572065198[/C][C]-6.69957206519802[/C][/ROW]
[ROW][C]102[/C][C]102[/C][C]112.129960323344[/C][C]-10.1299603233441[/C][/ROW]
[ROW][C]103[/C][C]103[/C][C]107.09185944827[/C][C]-4.09185944826964[/C][/ROW]
[ROW][C]104[/C][C]104[/C][C]108.519257395363[/C][C]-4.51925739536271[/C][/ROW]
[ROW][C]105[/C][C]105[/C][C]111.258917802309[/C][C]-6.25891780230916[/C][/ROW]
[ROW][C]106[/C][C]106[/C][C]109.050267374243[/C][C]-3.0502673742427[/C][/ROW]
[ROW][C]107[/C][C]107[/C][C]105.862186874822[/C][C]1.13781312517759[/C][/ROW]
[ROW][C]108[/C][C]108[/C][C]106.030885917152[/C][C]1.96911408284808[/C][/ROW]
[ROW][C]109[/C][C]109[/C][C]117.001508195476[/C][C]-8.00150819547648[/C][/ROW]
[ROW][C]110[/C][C]110[/C][C]110.665207879909[/C][C]-0.665207879908682[/C][/ROW]
[ROW][C]111[/C][C]111[/C][C]112.027096366452[/C][C]-1.02709636645194[/C][/ROW]
[ROW][C]112[/C][C]112[/C][C]112.688813203949[/C][C]-0.688813203948884[/C][/ROW]
[ROW][C]113[/C][C]113[/C][C]116.178972357175[/C][C]-3.17897235717464[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]116.814662167795[/C][C]-2.81466216779508[/C][/ROW]
[ROW][C]115[/C][C]115[/C][C]116.85354573016[/C][C]-1.85354573015959[/C][/ROW]
[ROW][C]116[/C][C]116[/C][C]110.892763664088[/C][C]5.10723633591167[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]117.2867242826[/C][C]-0.286724282600402[/C][/ROW]
[ROW][C]118[/C][C]118[/C][C]117.795467269365[/C][C]0.204532730635414[/C][/ROW]
[ROW][C]119[/C][C]119[/C][C]114.261919462609[/C][C]4.73808053739058[/C][/ROW]
[ROW][C]120[/C][C]120[/C][C]113.516540043596[/C][C]6.48345995640405[/C][/ROW]
[ROW][C]121[/C][C]121[/C][C]117.097745626563[/C][C]3.90225437343734[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]119.732125690001[/C][C]2.26787430999851[/C][/ROW]
[ROW][C]123[/C][C]123[/C][C]120.763443940988[/C][C]2.23655605901164[/C][/ROW]
[ROW][C]124[/C][C]124[/C][C]121.469967866724[/C][C]2.53003213327603[/C][/ROW]
[ROW][C]125[/C][C]125[/C][C]118.198089268461[/C][C]6.80191073153939[/C][/ROW]
[ROW][C]126[/C][C]126[/C][C]127.212812065694[/C][C]-1.21281206569369[/C][/ROW]
[ROW][C]127[/C][C]127[/C][C]124.821660835694[/C][C]2.17833916430644[/C][/ROW]
[ROW][C]128[/C][C]128[/C][C]125.90749368293[/C][C]2.09250631707016[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]125.981021320362[/C][C]3.01897867963798[/C][/ROW]
[ROW][C]130[/C][C]130[/C][C]122.101031623217[/C][C]7.89896837678346[/C][/ROW]
[ROW][C]131[/C][C]131[/C][C]125.744546745553[/C][C]5.25545325444717[/C][/ROW]
[ROW][C]132[/C][C]132[/C][C]122.598128674757[/C][C]9.40187132524254[/C][/ROW]
[ROW][C]133[/C][C]133[/C][C]122.840059900023[/C][C]10.1599400999768[/C][/ROW]
[ROW][C]134[/C][C]134[/C][C]126.572755615266[/C][C]7.42724438473383[/C][/ROW]
[ROW][C]135[/C][C]135[/C][C]125.89034762941[/C][C]9.10965237058982[/C][/ROW]
[ROW][C]136[/C][C]136[/C][C]128.621322615344[/C][C]7.37867738465597[/C][/ROW]
[ROW][C]137[/C][C]137[/C][C]136.644459890996[/C][C]0.355540109003613[/C][/ROW]
[ROW][C]138[/C][C]138[/C][C]128.105460437117[/C][C]9.89453956288284[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]132.950180384078[/C][C]6.04981961592151[/C][/ROW]
[ROW][C]140[/C][C]140[/C][C]132.455907134035[/C][C]7.54409286596462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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
11-27.14123767214528.141237672145
22-21.381480194970423.3814801949704
33-12.421806540029115.4218065400291
44-7.9862869840812211.9862869840812
55-1.41618028790636.4161802879063
664.795672159571221.20432784042878
774.144060580956612.85593941904339
886.365091990836731.63490800916327
998.695192856657010.304807143342989
101015.5157540950114-5.51575409501138
111116.219100975857-5.21910097585698
121218.5323021683933-6.53230216839332
131320.406366598864-7.406366598864
141419.8435524378524-5.84355243785239
151518.432593057388-3.43259305738799
161621.4416269854276-5.44162698542761
171722.155307183601-5.15530718360099
181821.3250838320561-3.32508383205614
191925.2555869880356-6.2555869880356
202027.398695077498-7.39869507749802
212128.0969225010849-7.09692250108493
222226.9192813769719-4.91928137697194
232326.8715254443768-3.87152544437677
242428.5166028977404-4.51660289774038
252533.5987024477154-8.59870244771543
262632.4267181470482-6.42671814704822
272728.899275623408-1.89927562340796
282834.9229383187051-6.92293831870505
292930.1574857668903-1.15748576689027
303031.0596543419701-1.05965434197009
313133.2952009136207-2.29520091362067
323235.5289875536016-3.52898755360163
333337.2710066302306-4.2710066302306
343435.2806112135037-1.2806112135037
353535.5961239631213-0.596123963121266
363631.5254367794474.47456322055299
373735.12911680184521.87088319815475
383836.50181793418821.49818206581177
393937.24373065207911.75626934792091
404037.68353655043782.31646344956223
414136.86321249172444.13678750827563
424232.61212780943959.38787219056046
434339.7940886899873.20591131001301
444438.32040304645955.67959695354046
454538.4965358017826.50346419821802
464639.97626584925166.02373415074841
474738.6101325547138.38986744528697
484859.3137869469594-11.3137869469594
494961.9559420791622-12.9559420791622
505060.5013098691633-10.5013098691633
515161.9281962323459-10.9281962323459
525263.5560564521176-11.5560564521176
535361.5195171084497-8.51951710844967
545464.2081457302502-10.2081457302502
555562.1454255848327-7.14542558483268
565663.0128679383006-7.01286793830061
575760.8151096529911-3.81510965299108
585863.4426351054403-5.44263510544028
595964.2799600787545-5.27996007875449
606062.8717721139433-2.87177211394332
616164.2965757535206-3.29657575352061
626265.1188453784093-3.11884537840931
636367.2585089674599-4.25850896745987
646463.97748394718290.0225160528170822
656566.0727841443748-1.07278414437482
666667.5348964808628-1.53489648086275
676766.23619773885360.76380226114644
686870.6272894864995-2.62728948649952
696971.5432760961209-2.5432760961209
707072.4052468263476-2.40524682634765
717171.0029587888531-0.00295878885305984
727271.42872615518240.571273844817625
737375.8773910824413-2.87739108244134
747478.3536329421367-4.35363294213672
757578.6655054518917-3.66550545189168
767679.5744677832599-3.57446778325987
777776.31758350163880.682416498361166
787877.74346699879390.256533001206045
797980.3527493161922-1.3527493161922
808083.8936404291945-3.89364042919449
818174.70254428052466.29745571947539
828280.70762083268571.29237916731434
838376.7748489478116.22515105218901
848475.71102628370968.28897371629041
858580.55260661360744.44739338639261
868681.95338710575974.04661289424035
878782.1232393334054.87676066659497
888878.02703185781749.97296814218255
898981.60919650939777.39080349060232
909080.45805765310619.54194234689391
919183.05944378932047.94055621067962
929285.07760260119326.92239739880679
939384.85274877533738.14725122466269
949486.8058184339647.19418156603604
9595105.776696163529-10.7766961635288
9696101.954792019119-5.95479201911936
9797102.385222572926-5.38522257292647
9898105.712127195236-7.71212719523618
9999107.189869285201-8.18986928520112
100100106.023041403116-6.02304140311609
101101107.699572065198-6.69957206519802
102102112.129960323344-10.1299603233441
103103107.09185944827-4.09185944826964
104104108.519257395363-4.51925739536271
105105111.258917802309-6.25891780230916
106106109.050267374243-3.0502673742427
107107105.8621868748221.13781312517759
108108106.0308859171521.96911408284808
109109117.001508195476-8.00150819547648
110110110.665207879909-0.665207879908682
111111112.027096366452-1.02709636645194
112112112.688813203949-0.688813203948884
113113116.178972357175-3.17897235717464
114114116.814662167795-2.81466216779508
115115116.85354573016-1.85354573015959
116116110.8927636640885.10723633591167
117117117.2867242826-0.286724282600402
118118117.7954672693650.204532730635414
119119114.2619194626094.73808053739058
120120113.5165400435966.48345995640405
121121117.0977456265633.90225437343734
122122119.7321256900012.26787430999851
123123120.7634439409882.23655605901164
124124121.4699678667242.53003213327603
125125118.1980892684616.80191073153939
126126127.212812065694-1.21281206569369
127127124.8216608356942.17833916430644
128128125.907493682932.09250631707016
129129125.9810213203623.01897867963798
130130122.1010316232177.89896837678346
131131125.7445467455535.25545325444717
132132122.5981286747579.40187132524254
133133122.84005990002310.1599400999768
134134126.5727556152667.42724438473383
135135125.890347629419.10965237058982
136136128.6213226153447.37867738465597
137137136.6444598909960.355540109003613
138138128.1054604371179.89453956288284
139139132.9501803840786.04981961592151
140140132.4559071340357.54409286596462







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
240.6803336616860690.6393326766278630.319666338313931
250.7011033252640550.597793349471890.298896674735945
260.5847147552770830.8305704894458330.415285244722917
270.55851298559610.88297402880780.4414870144039
280.9131600198877840.1736799602244320.0868399801122162
290.9398691228671170.1202617542657650.0601308771328826
300.9468580423212490.1062839153575020.0531419576787509
310.981162895502280.03767420899544050.0188371044977202
320.9915259193148080.01694816137038430.00847408068519216
330.9977144206274390.004571158745121350.00228557937256067
340.9990793716882670.001841256623466550.000920628311733277
350.9985581885409390.00288362291812120.0014418114590606
360.9999454236417230.0001091527165548445.45763582774221e-05
370.999932118101230.0001357637975400086.78818987700042e-05
380.9999404673530890.000119065293822935.95326469114648e-05
390.99992956337480.0001408732504007227.04366252003612e-05
400.9999997403509985.19298003250503e-072.59649001625252e-07
410.9999999566806038.66387937638943e-084.33193968819472e-08
420.9999999571658678.56682661947482e-084.28341330973741e-08
430.9999999389368661.22126268420574e-076.10631342102868e-08
440.999999989103592.179281953153e-081.0896409765765e-08
450.9999999971305225.73895568265828e-092.86947784132914e-09
460.9999999982257493.54850229304579e-091.77425114652289e-09
470.9999999975931384.81372473965954e-092.40686236982977e-09
480.9999999985659232.86815412190954e-091.43407706095477e-09
490.9999999996711056.5779098917742e-103.2889549458871e-10
500.9999999996094067.81187137340641e-103.90593568670321e-10
510.999999999306771.38646004386966e-096.93230021934832e-10
520.9999999992461821.50763569512757e-097.53817847563784e-10
530.9999999986264672.74706674981e-091.373533374905e-09
540.9999999973228315.35433855668263e-092.67716927834132e-09
550.9999999982115343.57693112448144e-091.78846556224072e-09
560.9999999965219246.95615117346193e-093.47807558673096e-09
570.9999999934337841.31324316705463e-086.56621583527314e-09
580.9999999923664731.52670548749083e-087.63352743745417e-09
590.9999999827573193.44853614243898e-081.72426807121949e-08
600.999999976377914.7244179234828e-082.3622089617414e-08
610.999999961548487.69030390926361e-083.84515195463181e-08
620.9999999693309376.13381263676519e-083.0669063183826e-08
630.999999986883482.62330391141823e-081.31165195570912e-08
640.9999999951976719.60465752896907e-094.80232876448454e-09
650.9999999934906971.30186060463573e-086.50930302317865e-09
660.9999999948142191.0371562139988e-085.185781069994e-09
670.9999999891287832.17424343125333e-081.08712171562667e-08
680.999999984400363.11992803116356e-081.55996401558178e-08
690.9999999689126276.21747468108432e-083.10873734054216e-08
700.9999999370120561.25975888707459e-076.29879443537293e-08
710.9999999121279781.7574404447135e-078.78720222356752e-08
720.9999999222582021.55483596412861e-077.77417982064305e-08
730.9999999085069291.8298614269136e-079.14930713456798e-08
740.999999986674852.66502999900167e-081.33251499950084e-08
750.9999999987460472.50790600339939e-091.25395300169969e-09
760.9999999995459839.08034699589232e-104.54017349794616e-10
770.9999999998358983.28203227411888e-101.64101613705944e-10
780.9999999999740995.1801190842497e-112.59005954212485e-11
790.9999999999921081.57840233768706e-117.89201168843528e-12
800.9999999999906191.87630156077485e-119.38150780387423e-12
810.9999999999842643.14724909990646e-111.57362454995323e-11
820.9999999999607767.84486555669169e-113.92243277834584e-11
830.9999999999765744.68525980050402e-112.34262990025201e-11
840.9999999999765454.69106293292317e-112.34553146646158e-11
850.9999999999629987.40038972009078e-113.70019486004539e-11
860.9999999999615477.69066059010869e-113.84533029505435e-11
870.9999999999038391.92321301087699e-109.61606505438494e-11
880.9999999998073283.85343209385454e-101.92671604692727e-10
890.999999999542569.14879289144194e-104.57439644572097e-10
900.9999999994745571.05088610525452e-095.25443052627261e-10
910.9999999992098391.58032226065817e-097.90161130329084e-10
920.9999999987844092.43118251080828e-091.21559125540414e-09
930.9999999978820624.23587530925428e-092.11793765462714e-09
940.9999999931082661.3783467775991e-086.89173388799549e-09
950.999999994398231.12035396026167e-085.60176980130836e-09
960.999999980805353.83892998856556e-081.91946499428278e-08
970.9999999933832971.32334049357086e-086.61670246785432e-09
980.9999999956775488.64490426661281e-094.32245213330641e-09
990.9999999882186392.35627227646732e-081.17813613823366e-08
1000.9999999821412233.57175540534492e-081.78587770267246e-08
1010.9999999360590181.2788196456996e-076.39409822849798e-08
1020.9999997873342994.25331401126296e-072.12665700563148e-07
1030.9999994629286451.07414271061993e-065.37071355309963e-07
1040.999998186290883.62741823906011e-061.81370911953006e-06
1050.9999949550371711.00899256581733e-055.04496282908666e-06
1060.999986213607322.75727853607784e-051.37863926803892e-05
1070.9999674244002556.51511994905576e-053.25755997452788e-05
1080.9999044673989630.0001910652020731929.55326010365961e-05
1090.9997686172268020.0004627655463954620.000231382773197731
1100.9993317533308590.00133649333828150.000668246669140751
1110.9989271293494710.002145741301058710.00107287065052936
1120.9969632568786130.006073486242773140.00303674312138657
1130.9944949274533680.01101014509326370.00550507254663186
1140.9835077666778960.03298446664420820.0164922333221041
1150.9925123614425550.014975277114890.00748763855744499
1160.9774814838449730.04503703231005390.022518516155027

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
24 & 0.680333661686069 & 0.639332676627863 & 0.319666338313931 \tabularnewline
25 & 0.701103325264055 & 0.59779334947189 & 0.298896674735945 \tabularnewline
26 & 0.584714755277083 & 0.830570489445833 & 0.415285244722917 \tabularnewline
27 & 0.5585129855961 & 0.8829740288078 & 0.4414870144039 \tabularnewline
28 & 0.913160019887784 & 0.173679960224432 & 0.0868399801122162 \tabularnewline
29 & 0.939869122867117 & 0.120261754265765 & 0.0601308771328826 \tabularnewline
30 & 0.946858042321249 & 0.106283915357502 & 0.0531419576787509 \tabularnewline
31 & 0.98116289550228 & 0.0376742089954405 & 0.0188371044977202 \tabularnewline
32 & 0.991525919314808 & 0.0169481613703843 & 0.00847408068519216 \tabularnewline
33 & 0.997714420627439 & 0.00457115874512135 & 0.00228557937256067 \tabularnewline
34 & 0.999079371688267 & 0.00184125662346655 & 0.000920628311733277 \tabularnewline
35 & 0.998558188540939 & 0.0028836229181212 & 0.0014418114590606 \tabularnewline
36 & 0.999945423641723 & 0.000109152716554844 & 5.45763582774221e-05 \tabularnewline
37 & 0.99993211810123 & 0.000135763797540008 & 6.78818987700042e-05 \tabularnewline
38 & 0.999940467353089 & 0.00011906529382293 & 5.95326469114648e-05 \tabularnewline
39 & 0.9999295633748 & 0.000140873250400722 & 7.04366252003612e-05 \tabularnewline
40 & 0.999999740350998 & 5.19298003250503e-07 & 2.59649001625252e-07 \tabularnewline
41 & 0.999999956680603 & 8.66387937638943e-08 & 4.33193968819472e-08 \tabularnewline
42 & 0.999999957165867 & 8.56682661947482e-08 & 4.28341330973741e-08 \tabularnewline
43 & 0.999999938936866 & 1.22126268420574e-07 & 6.10631342102868e-08 \tabularnewline
44 & 0.99999998910359 & 2.179281953153e-08 & 1.0896409765765e-08 \tabularnewline
45 & 0.999999997130522 & 5.73895568265828e-09 & 2.86947784132914e-09 \tabularnewline
46 & 0.999999998225749 & 3.54850229304579e-09 & 1.77425114652289e-09 \tabularnewline
47 & 0.999999997593138 & 4.81372473965954e-09 & 2.40686236982977e-09 \tabularnewline
48 & 0.999999998565923 & 2.86815412190954e-09 & 1.43407706095477e-09 \tabularnewline
49 & 0.999999999671105 & 6.5779098917742e-10 & 3.2889549458871e-10 \tabularnewline
50 & 0.999999999609406 & 7.81187137340641e-10 & 3.90593568670321e-10 \tabularnewline
51 & 0.99999999930677 & 1.38646004386966e-09 & 6.93230021934832e-10 \tabularnewline
52 & 0.999999999246182 & 1.50763569512757e-09 & 7.53817847563784e-10 \tabularnewline
53 & 0.999999998626467 & 2.74706674981e-09 & 1.373533374905e-09 \tabularnewline
54 & 0.999999997322831 & 5.35433855668263e-09 & 2.67716927834132e-09 \tabularnewline
55 & 0.999999998211534 & 3.57693112448144e-09 & 1.78846556224072e-09 \tabularnewline
56 & 0.999999996521924 & 6.95615117346193e-09 & 3.47807558673096e-09 \tabularnewline
57 & 0.999999993433784 & 1.31324316705463e-08 & 6.56621583527314e-09 \tabularnewline
58 & 0.999999992366473 & 1.52670548749083e-08 & 7.63352743745417e-09 \tabularnewline
59 & 0.999999982757319 & 3.44853614243898e-08 & 1.72426807121949e-08 \tabularnewline
60 & 0.99999997637791 & 4.7244179234828e-08 & 2.3622089617414e-08 \tabularnewline
61 & 0.99999996154848 & 7.69030390926361e-08 & 3.84515195463181e-08 \tabularnewline
62 & 0.999999969330937 & 6.13381263676519e-08 & 3.0669063183826e-08 \tabularnewline
63 & 0.99999998688348 & 2.62330391141823e-08 & 1.31165195570912e-08 \tabularnewline
64 & 0.999999995197671 & 9.60465752896907e-09 & 4.80232876448454e-09 \tabularnewline
65 & 0.999999993490697 & 1.30186060463573e-08 & 6.50930302317865e-09 \tabularnewline
66 & 0.999999994814219 & 1.0371562139988e-08 & 5.185781069994e-09 \tabularnewline
67 & 0.999999989128783 & 2.17424343125333e-08 & 1.08712171562667e-08 \tabularnewline
68 & 0.99999998440036 & 3.11992803116356e-08 & 1.55996401558178e-08 \tabularnewline
69 & 0.999999968912627 & 6.21747468108432e-08 & 3.10873734054216e-08 \tabularnewline
70 & 0.999999937012056 & 1.25975888707459e-07 & 6.29879443537293e-08 \tabularnewline
71 & 0.999999912127978 & 1.7574404447135e-07 & 8.78720222356752e-08 \tabularnewline
72 & 0.999999922258202 & 1.55483596412861e-07 & 7.77417982064305e-08 \tabularnewline
73 & 0.999999908506929 & 1.8298614269136e-07 & 9.14930713456798e-08 \tabularnewline
74 & 0.99999998667485 & 2.66502999900167e-08 & 1.33251499950084e-08 \tabularnewline
75 & 0.999999998746047 & 2.50790600339939e-09 & 1.25395300169969e-09 \tabularnewline
76 & 0.999999999545983 & 9.08034699589232e-10 & 4.54017349794616e-10 \tabularnewline
77 & 0.999999999835898 & 3.28203227411888e-10 & 1.64101613705944e-10 \tabularnewline
78 & 0.999999999974099 & 5.1801190842497e-11 & 2.59005954212485e-11 \tabularnewline
79 & 0.999999999992108 & 1.57840233768706e-11 & 7.89201168843528e-12 \tabularnewline
80 & 0.999999999990619 & 1.87630156077485e-11 & 9.38150780387423e-12 \tabularnewline
81 & 0.999999999984264 & 3.14724909990646e-11 & 1.57362454995323e-11 \tabularnewline
82 & 0.999999999960776 & 7.84486555669169e-11 & 3.92243277834584e-11 \tabularnewline
83 & 0.999999999976574 & 4.68525980050402e-11 & 2.34262990025201e-11 \tabularnewline
84 & 0.999999999976545 & 4.69106293292317e-11 & 2.34553146646158e-11 \tabularnewline
85 & 0.999999999962998 & 7.40038972009078e-11 & 3.70019486004539e-11 \tabularnewline
86 & 0.999999999961547 & 7.69066059010869e-11 & 3.84533029505435e-11 \tabularnewline
87 & 0.999999999903839 & 1.92321301087699e-10 & 9.61606505438494e-11 \tabularnewline
88 & 0.999999999807328 & 3.85343209385454e-10 & 1.92671604692727e-10 \tabularnewline
89 & 0.99999999954256 & 9.14879289144194e-10 & 4.57439644572097e-10 \tabularnewline
90 & 0.999999999474557 & 1.05088610525452e-09 & 5.25443052627261e-10 \tabularnewline
91 & 0.999999999209839 & 1.58032226065817e-09 & 7.90161130329084e-10 \tabularnewline
92 & 0.999999998784409 & 2.43118251080828e-09 & 1.21559125540414e-09 \tabularnewline
93 & 0.999999997882062 & 4.23587530925428e-09 & 2.11793765462714e-09 \tabularnewline
94 & 0.999999993108266 & 1.3783467775991e-08 & 6.89173388799549e-09 \tabularnewline
95 & 0.99999999439823 & 1.12035396026167e-08 & 5.60176980130836e-09 \tabularnewline
96 & 0.99999998080535 & 3.83892998856556e-08 & 1.91946499428278e-08 \tabularnewline
97 & 0.999999993383297 & 1.32334049357086e-08 & 6.61670246785432e-09 \tabularnewline
98 & 0.999999995677548 & 8.64490426661281e-09 & 4.32245213330641e-09 \tabularnewline
99 & 0.999999988218639 & 2.35627227646732e-08 & 1.17813613823366e-08 \tabularnewline
100 & 0.999999982141223 & 3.57175540534492e-08 & 1.78587770267246e-08 \tabularnewline
101 & 0.999999936059018 & 1.2788196456996e-07 & 6.39409822849798e-08 \tabularnewline
102 & 0.999999787334299 & 4.25331401126296e-07 & 2.12665700563148e-07 \tabularnewline
103 & 0.999999462928645 & 1.07414271061993e-06 & 5.37071355309963e-07 \tabularnewline
104 & 0.99999818629088 & 3.62741823906011e-06 & 1.81370911953006e-06 \tabularnewline
105 & 0.999994955037171 & 1.00899256581733e-05 & 5.04496282908666e-06 \tabularnewline
106 & 0.99998621360732 & 2.75727853607784e-05 & 1.37863926803892e-05 \tabularnewline
107 & 0.999967424400255 & 6.51511994905576e-05 & 3.25755997452788e-05 \tabularnewline
108 & 0.999904467398963 & 0.000191065202073192 & 9.55326010365961e-05 \tabularnewline
109 & 0.999768617226802 & 0.000462765546395462 & 0.000231382773197731 \tabularnewline
110 & 0.999331753330859 & 0.0013364933382815 & 0.000668246669140751 \tabularnewline
111 & 0.998927129349471 & 0.00214574130105871 & 0.00107287065052936 \tabularnewline
112 & 0.996963256878613 & 0.00607348624277314 & 0.00303674312138657 \tabularnewline
113 & 0.994494927453368 & 0.0110101450932637 & 0.00550507254663186 \tabularnewline
114 & 0.983507766677896 & 0.0329844666442082 & 0.0164922333221041 \tabularnewline
115 & 0.992512361442555 & 0.01497527711489 & 0.00748763855744499 \tabularnewline
116 & 0.977481483844973 & 0.0450370323100539 & 0.022518516155027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&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]24[/C][C]0.680333661686069[/C][C]0.639332676627863[/C][C]0.319666338313931[/C][/ROW]
[ROW][C]25[/C][C]0.701103325264055[/C][C]0.59779334947189[/C][C]0.298896674735945[/C][/ROW]
[ROW][C]26[/C][C]0.584714755277083[/C][C]0.830570489445833[/C][C]0.415285244722917[/C][/ROW]
[ROW][C]27[/C][C]0.5585129855961[/C][C]0.8829740288078[/C][C]0.4414870144039[/C][/ROW]
[ROW][C]28[/C][C]0.913160019887784[/C][C]0.173679960224432[/C][C]0.0868399801122162[/C][/ROW]
[ROW][C]29[/C][C]0.939869122867117[/C][C]0.120261754265765[/C][C]0.0601308771328826[/C][/ROW]
[ROW][C]30[/C][C]0.946858042321249[/C][C]0.106283915357502[/C][C]0.0531419576787509[/C][/ROW]
[ROW][C]31[/C][C]0.98116289550228[/C][C]0.0376742089954405[/C][C]0.0188371044977202[/C][/ROW]
[ROW][C]32[/C][C]0.991525919314808[/C][C]0.0169481613703843[/C][C]0.00847408068519216[/C][/ROW]
[ROW][C]33[/C][C]0.997714420627439[/C][C]0.00457115874512135[/C][C]0.00228557937256067[/C][/ROW]
[ROW][C]34[/C][C]0.999079371688267[/C][C]0.00184125662346655[/C][C]0.000920628311733277[/C][/ROW]
[ROW][C]35[/C][C]0.998558188540939[/C][C]0.0028836229181212[/C][C]0.0014418114590606[/C][/ROW]
[ROW][C]36[/C][C]0.999945423641723[/C][C]0.000109152716554844[/C][C]5.45763582774221e-05[/C][/ROW]
[ROW][C]37[/C][C]0.99993211810123[/C][C]0.000135763797540008[/C][C]6.78818987700042e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999940467353089[/C][C]0.00011906529382293[/C][C]5.95326469114648e-05[/C][/ROW]
[ROW][C]39[/C][C]0.9999295633748[/C][C]0.000140873250400722[/C][C]7.04366252003612e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999999740350998[/C][C]5.19298003250503e-07[/C][C]2.59649001625252e-07[/C][/ROW]
[ROW][C]41[/C][C]0.999999956680603[/C][C]8.66387937638943e-08[/C][C]4.33193968819472e-08[/C][/ROW]
[ROW][C]42[/C][C]0.999999957165867[/C][C]8.56682661947482e-08[/C][C]4.28341330973741e-08[/C][/ROW]
[ROW][C]43[/C][C]0.999999938936866[/C][C]1.22126268420574e-07[/C][C]6.10631342102868e-08[/C][/ROW]
[ROW][C]44[/C][C]0.99999998910359[/C][C]2.179281953153e-08[/C][C]1.0896409765765e-08[/C][/ROW]
[ROW][C]45[/C][C]0.999999997130522[/C][C]5.73895568265828e-09[/C][C]2.86947784132914e-09[/C][/ROW]
[ROW][C]46[/C][C]0.999999998225749[/C][C]3.54850229304579e-09[/C][C]1.77425114652289e-09[/C][/ROW]
[ROW][C]47[/C][C]0.999999997593138[/C][C]4.81372473965954e-09[/C][C]2.40686236982977e-09[/C][/ROW]
[ROW][C]48[/C][C]0.999999998565923[/C][C]2.86815412190954e-09[/C][C]1.43407706095477e-09[/C][/ROW]
[ROW][C]49[/C][C]0.999999999671105[/C][C]6.5779098917742e-10[/C][C]3.2889549458871e-10[/C][/ROW]
[ROW][C]50[/C][C]0.999999999609406[/C][C]7.81187137340641e-10[/C][C]3.90593568670321e-10[/C][/ROW]
[ROW][C]51[/C][C]0.99999999930677[/C][C]1.38646004386966e-09[/C][C]6.93230021934832e-10[/C][/ROW]
[ROW][C]52[/C][C]0.999999999246182[/C][C]1.50763569512757e-09[/C][C]7.53817847563784e-10[/C][/ROW]
[ROW][C]53[/C][C]0.999999998626467[/C][C]2.74706674981e-09[/C][C]1.373533374905e-09[/C][/ROW]
[ROW][C]54[/C][C]0.999999997322831[/C][C]5.35433855668263e-09[/C][C]2.67716927834132e-09[/C][/ROW]
[ROW][C]55[/C][C]0.999999998211534[/C][C]3.57693112448144e-09[/C][C]1.78846556224072e-09[/C][/ROW]
[ROW][C]56[/C][C]0.999999996521924[/C][C]6.95615117346193e-09[/C][C]3.47807558673096e-09[/C][/ROW]
[ROW][C]57[/C][C]0.999999993433784[/C][C]1.31324316705463e-08[/C][C]6.56621583527314e-09[/C][/ROW]
[ROW][C]58[/C][C]0.999999992366473[/C][C]1.52670548749083e-08[/C][C]7.63352743745417e-09[/C][/ROW]
[ROW][C]59[/C][C]0.999999982757319[/C][C]3.44853614243898e-08[/C][C]1.72426807121949e-08[/C][/ROW]
[ROW][C]60[/C][C]0.99999997637791[/C][C]4.7244179234828e-08[/C][C]2.3622089617414e-08[/C][/ROW]
[ROW][C]61[/C][C]0.99999996154848[/C][C]7.69030390926361e-08[/C][C]3.84515195463181e-08[/C][/ROW]
[ROW][C]62[/C][C]0.999999969330937[/C][C]6.13381263676519e-08[/C][C]3.0669063183826e-08[/C][/ROW]
[ROW][C]63[/C][C]0.99999998688348[/C][C]2.62330391141823e-08[/C][C]1.31165195570912e-08[/C][/ROW]
[ROW][C]64[/C][C]0.999999995197671[/C][C]9.60465752896907e-09[/C][C]4.80232876448454e-09[/C][/ROW]
[ROW][C]65[/C][C]0.999999993490697[/C][C]1.30186060463573e-08[/C][C]6.50930302317865e-09[/C][/ROW]
[ROW][C]66[/C][C]0.999999994814219[/C][C]1.0371562139988e-08[/C][C]5.185781069994e-09[/C][/ROW]
[ROW][C]67[/C][C]0.999999989128783[/C][C]2.17424343125333e-08[/C][C]1.08712171562667e-08[/C][/ROW]
[ROW][C]68[/C][C]0.99999998440036[/C][C]3.11992803116356e-08[/C][C]1.55996401558178e-08[/C][/ROW]
[ROW][C]69[/C][C]0.999999968912627[/C][C]6.21747468108432e-08[/C][C]3.10873734054216e-08[/C][/ROW]
[ROW][C]70[/C][C]0.999999937012056[/C][C]1.25975888707459e-07[/C][C]6.29879443537293e-08[/C][/ROW]
[ROW][C]71[/C][C]0.999999912127978[/C][C]1.7574404447135e-07[/C][C]8.78720222356752e-08[/C][/ROW]
[ROW][C]72[/C][C]0.999999922258202[/C][C]1.55483596412861e-07[/C][C]7.77417982064305e-08[/C][/ROW]
[ROW][C]73[/C][C]0.999999908506929[/C][C]1.8298614269136e-07[/C][C]9.14930713456798e-08[/C][/ROW]
[ROW][C]74[/C][C]0.99999998667485[/C][C]2.66502999900167e-08[/C][C]1.33251499950084e-08[/C][/ROW]
[ROW][C]75[/C][C]0.999999998746047[/C][C]2.50790600339939e-09[/C][C]1.25395300169969e-09[/C][/ROW]
[ROW][C]76[/C][C]0.999999999545983[/C][C]9.08034699589232e-10[/C][C]4.54017349794616e-10[/C][/ROW]
[ROW][C]77[/C][C]0.999999999835898[/C][C]3.28203227411888e-10[/C][C]1.64101613705944e-10[/C][/ROW]
[ROW][C]78[/C][C]0.999999999974099[/C][C]5.1801190842497e-11[/C][C]2.59005954212485e-11[/C][/ROW]
[ROW][C]79[/C][C]0.999999999992108[/C][C]1.57840233768706e-11[/C][C]7.89201168843528e-12[/C][/ROW]
[ROW][C]80[/C][C]0.999999999990619[/C][C]1.87630156077485e-11[/C][C]9.38150780387423e-12[/C][/ROW]
[ROW][C]81[/C][C]0.999999999984264[/C][C]3.14724909990646e-11[/C][C]1.57362454995323e-11[/C][/ROW]
[ROW][C]82[/C][C]0.999999999960776[/C][C]7.84486555669169e-11[/C][C]3.92243277834584e-11[/C][/ROW]
[ROW][C]83[/C][C]0.999999999976574[/C][C]4.68525980050402e-11[/C][C]2.34262990025201e-11[/C][/ROW]
[ROW][C]84[/C][C]0.999999999976545[/C][C]4.69106293292317e-11[/C][C]2.34553146646158e-11[/C][/ROW]
[ROW][C]85[/C][C]0.999999999962998[/C][C]7.40038972009078e-11[/C][C]3.70019486004539e-11[/C][/ROW]
[ROW][C]86[/C][C]0.999999999961547[/C][C]7.69066059010869e-11[/C][C]3.84533029505435e-11[/C][/ROW]
[ROW][C]87[/C][C]0.999999999903839[/C][C]1.92321301087699e-10[/C][C]9.61606505438494e-11[/C][/ROW]
[ROW][C]88[/C][C]0.999999999807328[/C][C]3.85343209385454e-10[/C][C]1.92671604692727e-10[/C][/ROW]
[ROW][C]89[/C][C]0.99999999954256[/C][C]9.14879289144194e-10[/C][C]4.57439644572097e-10[/C][/ROW]
[ROW][C]90[/C][C]0.999999999474557[/C][C]1.05088610525452e-09[/C][C]5.25443052627261e-10[/C][/ROW]
[ROW][C]91[/C][C]0.999999999209839[/C][C]1.58032226065817e-09[/C][C]7.90161130329084e-10[/C][/ROW]
[ROW][C]92[/C][C]0.999999998784409[/C][C]2.43118251080828e-09[/C][C]1.21559125540414e-09[/C][/ROW]
[ROW][C]93[/C][C]0.999999997882062[/C][C]4.23587530925428e-09[/C][C]2.11793765462714e-09[/C][/ROW]
[ROW][C]94[/C][C]0.999999993108266[/C][C]1.3783467775991e-08[/C][C]6.89173388799549e-09[/C][/ROW]
[ROW][C]95[/C][C]0.99999999439823[/C][C]1.12035396026167e-08[/C][C]5.60176980130836e-09[/C][/ROW]
[ROW][C]96[/C][C]0.99999998080535[/C][C]3.83892998856556e-08[/C][C]1.91946499428278e-08[/C][/ROW]
[ROW][C]97[/C][C]0.999999993383297[/C][C]1.32334049357086e-08[/C][C]6.61670246785432e-09[/C][/ROW]
[ROW][C]98[/C][C]0.999999995677548[/C][C]8.64490426661281e-09[/C][C]4.32245213330641e-09[/C][/ROW]
[ROW][C]99[/C][C]0.999999988218639[/C][C]2.35627227646732e-08[/C][C]1.17813613823366e-08[/C][/ROW]
[ROW][C]100[/C][C]0.999999982141223[/C][C]3.57175540534492e-08[/C][C]1.78587770267246e-08[/C][/ROW]
[ROW][C]101[/C][C]0.999999936059018[/C][C]1.2788196456996e-07[/C][C]6.39409822849798e-08[/C][/ROW]
[ROW][C]102[/C][C]0.999999787334299[/C][C]4.25331401126296e-07[/C][C]2.12665700563148e-07[/C][/ROW]
[ROW][C]103[/C][C]0.999999462928645[/C][C]1.07414271061993e-06[/C][C]5.37071355309963e-07[/C][/ROW]
[ROW][C]104[/C][C]0.99999818629088[/C][C]3.62741823906011e-06[/C][C]1.81370911953006e-06[/C][/ROW]
[ROW][C]105[/C][C]0.999994955037171[/C][C]1.00899256581733e-05[/C][C]5.04496282908666e-06[/C][/ROW]
[ROW][C]106[/C][C]0.99998621360732[/C][C]2.75727853607784e-05[/C][C]1.37863926803892e-05[/C][/ROW]
[ROW][C]107[/C][C]0.999967424400255[/C][C]6.51511994905576e-05[/C][C]3.25755997452788e-05[/C][/ROW]
[ROW][C]108[/C][C]0.999904467398963[/C][C]0.000191065202073192[/C][C]9.55326010365961e-05[/C][/ROW]
[ROW][C]109[/C][C]0.999768617226802[/C][C]0.000462765546395462[/C][C]0.000231382773197731[/C][/ROW]
[ROW][C]110[/C][C]0.999331753330859[/C][C]0.0013364933382815[/C][C]0.000668246669140751[/C][/ROW]
[ROW][C]111[/C][C]0.998927129349471[/C][C]0.00214574130105871[/C][C]0.00107287065052936[/C][/ROW]
[ROW][C]112[/C][C]0.996963256878613[/C][C]0.00607348624277314[/C][C]0.00303674312138657[/C][/ROW]
[ROW][C]113[/C][C]0.994494927453368[/C][C]0.0110101450932637[/C][C]0.00550507254663186[/C][/ROW]
[ROW][C]114[/C][C]0.983507766677896[/C][C]0.0329844666442082[/C][C]0.0164922333221041[/C][/ROW]
[ROW][C]115[/C][C]0.992512361442555[/C][C]0.01497527711489[/C][C]0.00748763855744499[/C][/ROW]
[ROW][C]116[/C][C]0.977481483844973[/C][C]0.0450370323100539[/C][C]0.022518516155027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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
240.6803336616860690.6393326766278630.319666338313931
250.7011033252640550.597793349471890.298896674735945
260.5847147552770830.8305704894458330.415285244722917
270.55851298559610.88297402880780.4414870144039
280.9131600198877840.1736799602244320.0868399801122162
290.9398691228671170.1202617542657650.0601308771328826
300.9468580423212490.1062839153575020.0531419576787509
310.981162895502280.03767420899544050.0188371044977202
320.9915259193148080.01694816137038430.00847408068519216
330.9977144206274390.004571158745121350.00228557937256067
340.9990793716882670.001841256623466550.000920628311733277
350.9985581885409390.00288362291812120.0014418114590606
360.9999454236417230.0001091527165548445.45763582774221e-05
370.999932118101230.0001357637975400086.78818987700042e-05
380.9999404673530890.000119065293822935.95326469114648e-05
390.99992956337480.0001408732504007227.04366252003612e-05
400.9999997403509985.19298003250503e-072.59649001625252e-07
410.9999999566806038.66387937638943e-084.33193968819472e-08
420.9999999571658678.56682661947482e-084.28341330973741e-08
430.9999999389368661.22126268420574e-076.10631342102868e-08
440.999999989103592.179281953153e-081.0896409765765e-08
450.9999999971305225.73895568265828e-092.86947784132914e-09
460.9999999982257493.54850229304579e-091.77425114652289e-09
470.9999999975931384.81372473965954e-092.40686236982977e-09
480.9999999985659232.86815412190954e-091.43407706095477e-09
490.9999999996711056.5779098917742e-103.2889549458871e-10
500.9999999996094067.81187137340641e-103.90593568670321e-10
510.999999999306771.38646004386966e-096.93230021934832e-10
520.9999999992461821.50763569512757e-097.53817847563784e-10
530.9999999986264672.74706674981e-091.373533374905e-09
540.9999999973228315.35433855668263e-092.67716927834132e-09
550.9999999982115343.57693112448144e-091.78846556224072e-09
560.9999999965219246.95615117346193e-093.47807558673096e-09
570.9999999934337841.31324316705463e-086.56621583527314e-09
580.9999999923664731.52670548749083e-087.63352743745417e-09
590.9999999827573193.44853614243898e-081.72426807121949e-08
600.999999976377914.7244179234828e-082.3622089617414e-08
610.999999961548487.69030390926361e-083.84515195463181e-08
620.9999999693309376.13381263676519e-083.0669063183826e-08
630.999999986883482.62330391141823e-081.31165195570912e-08
640.9999999951976719.60465752896907e-094.80232876448454e-09
650.9999999934906971.30186060463573e-086.50930302317865e-09
660.9999999948142191.0371562139988e-085.185781069994e-09
670.9999999891287832.17424343125333e-081.08712171562667e-08
680.999999984400363.11992803116356e-081.55996401558178e-08
690.9999999689126276.21747468108432e-083.10873734054216e-08
700.9999999370120561.25975888707459e-076.29879443537293e-08
710.9999999121279781.7574404447135e-078.78720222356752e-08
720.9999999222582021.55483596412861e-077.77417982064305e-08
730.9999999085069291.8298614269136e-079.14930713456798e-08
740.999999986674852.66502999900167e-081.33251499950084e-08
750.9999999987460472.50790600339939e-091.25395300169969e-09
760.9999999995459839.08034699589232e-104.54017349794616e-10
770.9999999998358983.28203227411888e-101.64101613705944e-10
780.9999999999740995.1801190842497e-112.59005954212485e-11
790.9999999999921081.57840233768706e-117.89201168843528e-12
800.9999999999906191.87630156077485e-119.38150780387423e-12
810.9999999999842643.14724909990646e-111.57362454995323e-11
820.9999999999607767.84486555669169e-113.92243277834584e-11
830.9999999999765744.68525980050402e-112.34262990025201e-11
840.9999999999765454.69106293292317e-112.34553146646158e-11
850.9999999999629987.40038972009078e-113.70019486004539e-11
860.9999999999615477.69066059010869e-113.84533029505435e-11
870.9999999999038391.92321301087699e-109.61606505438494e-11
880.9999999998073283.85343209385454e-101.92671604692727e-10
890.999999999542569.14879289144194e-104.57439644572097e-10
900.9999999994745571.05088610525452e-095.25443052627261e-10
910.9999999992098391.58032226065817e-097.90161130329084e-10
920.9999999987844092.43118251080828e-091.21559125540414e-09
930.9999999978820624.23587530925428e-092.11793765462714e-09
940.9999999931082661.3783467775991e-086.89173388799549e-09
950.999999994398231.12035396026167e-085.60176980130836e-09
960.999999980805353.83892998856556e-081.91946499428278e-08
970.9999999933832971.32334049357086e-086.61670246785432e-09
980.9999999956775488.64490426661281e-094.32245213330641e-09
990.9999999882186392.35627227646732e-081.17813613823366e-08
1000.9999999821412233.57175540534492e-081.78587770267246e-08
1010.9999999360590181.2788196456996e-076.39409822849798e-08
1020.9999997873342994.25331401126296e-072.12665700563148e-07
1030.9999994629286451.07414271061993e-065.37071355309963e-07
1040.999998186290883.62741823906011e-061.81370911953006e-06
1050.9999949550371711.00899256581733e-055.04496282908666e-06
1060.999986213607322.75727853607784e-051.37863926803892e-05
1070.9999674244002556.51511994905576e-053.25755997452788e-05
1080.9999044673989630.0001910652020731929.55326010365961e-05
1090.9997686172268020.0004627655463954620.000231382773197731
1100.9993317533308590.00133649333828150.000668246669140751
1110.9989271293494710.002145741301058710.00107287065052936
1120.9969632568786130.006073486242773140.00303674312138657
1130.9944949274533680.01101014509326370.00550507254663186
1140.9835077666778960.03298446664420820.0164922333221041
1150.9925123614425550.014975277114890.00748763855744499
1160.9774814838449730.04503703231005390.022518516155027







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level800.860215053763441NOK
5% type I error level860.924731182795699NOK
10% type I error level860.924731182795699NOK

\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 & 80 & 0.860215053763441 & NOK \tabularnewline
5% type I error level & 86 & 0.924731182795699 & NOK \tabularnewline
10% type I error level & 86 & 0.924731182795699 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197021&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]80[/C][C]0.860215053763441[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]86[/C][C]0.924731182795699[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]0.924731182795699[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197021&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197021&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 level800.860215053763441NOK
5% type I error level860.924731182795699NOK
10% type I error level860.924731182795699NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}