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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 17 Dec 2014 11:52:03 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418818023h94oqwjrwlfwd8m.htm/, Retrieved Thu, 16 May 2024 14:45:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270098, Retrieved Thu, 16 May 2024 14:45:38 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Fcast 2011] [2014-12-17 11:52:03] [ddb851b9ced255c1d64c58a7ca49fb28] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
11	8	7	18	12	20	4	1.00	0.50	0.67	0.67	0.00	0.50	149	68	12.9
19	18	20	23	20	19	4	0.89	0.50	0.83	0.33	0.50	1.00	139	39	12.2
16	12	9	22	14	18	5	0.89	0.40	1.00	0.67	0.00	1.00	148	32	12.8
24	24	19	22	25	24	4	0.89	0.50	0.83	0.00	0.00	0.00	158	62	7.4
15	16	12	19	15	20	4	0.89	0.70	0.67	0.00	1.00	1.00	128	33	6.7
17	19	16	25	20	20	9	0.78	0.30	0.00	0.00	0.50	0.50	224	52	12.6
19	16	17	28	21	24	8	0.89	0.40	0.83	0.67	0.50	0.00	159	62	14.8
19	15	9	16	15	21	11	1.00	0.40	0.50	0.67	1.00	1.00	105	77	13.3
28	28	28	28	28	28	4	0.89	0.70	0.83	0.00	0.50	0.00	159	76	11.1
26	21	20	21	11	10	4	0.78	0.60	0.33	0.67	0.50	0.50	167	41	8.2
15	18	16	22	22	22	6	1.00	0.60	0.50	1.00	0.00	0.50	165	48	11.4
26	22	22	24	22	19	4	0.78	0.20	0.67	0.00	0.50	0.50	159	63	6.4
16	19	17	24	27	27	8	0.89	0.40	1.00	0.00	0.50	0.50	119	30	10.6
24	22	12	26	24	23	4	0.89	0.40	0.50	0.67	0.00	1.00	176	78	12.0
25	25	18	28	23	24	4	0.89	0.50	0.67	0.33	0.00	0.00	54	19	6.3
22	20	20	24	24	24	11	0.89	0.30	0.17	0.67	0.00	0.50	91	31	11.3
15	16	12	20	21	25	4	0.89	0.40	0.83	0.33	0.50	0.50	163	66	11.9
21	19	16	26	20	24	4	0.67	0.70	0.67	0.33	0.50	1.00	124	35	9.3
22	18	16	21	19	21	6	1.00	0.50	0.67	0.33	0.00	1.00	137	42	9.6
27	26	21	28	25	28	6	0.78	0.20	0.67	0.00	0.00	1.00	121	45	10.0
26	24	15	27	16	28	4	0.78	0.30	0.50	0.67	0.00	0.50	153	21	6.4
26	20	17	23	24	22	8	0.89	0.60	1.00	0.33	0.00	1.00	148	25	13.8
22	19	17	24	21	26	5	0.78	0.60	0.83	0.33	0.00	1.00	221	44	10.8
21	19	17	24	22	26	4	0.89	0.20	0.83	0.33	0.00	1.00	188	69	13.8
22	23	18	22	25	21	9	0.89	0.70	1.00	0.67	1.00	0.00	149	54	11.7
20	18	15	21	23	26	4	0.33	0.20	0.67	0.00	0.00	0.00	244	74	10.9
21	16	20	25	20	23	7	1.00	1.00	1.00	0.33	1.00	1.00	148	80	16.1
20	18	13	20	21	20	10	0.89	0.40	0.83	0.67	0.00	0.50	92	42	13.4
22	21	21	21	22	24	4	0.89	0.40	1.00	1.00	0.00	1.00	150	61	9.9
21	20	12	26	25	25	4	0.67	0.20	0.83	0.67	0.00	0.50	153	41	11.5
8	15	6	23	23	24	7	0.56	0.40	0.67	0.33	0.00	1.00	94	46	8.3
22	19	13	21	19	20	12	0.89	0.40	0.67	0.00	0.50	1.00	156	39	11.7
20	19	19	27	21	24	7	0.89	0.70	1.00	0.67	0.50	0.50	132	34	9.0
24	7	12	25	19	25	5	1.00	0.20	0.67	0.67	0.00	0.50	161	51	9.7
17	20	14	23	25	23	8	0.78	0.60	1.00	1.00	0.00	0.50	105	42	10.8
20	20	13	25	16	21	5	0.78	0.30	1.00	1.00	0.50	0.50	97	31	10.3
23	19	12	23	24	23	4	0.33	0.30	0.50	0.33	0.00	0.00	151	39	10.4
20	19	17	19	24	21	9	0.78	0.20	0.67	0.00	0.50	0.00	131	20	12.7
22	20	19	22	18	18	7	0.89	0.50	0.83	0.67	0.50	0.50	166	49	9.3
19	18	10	24	28	24	4	0.89	0.70	1.00	0.67	0.50	1.00	157	53	11.8
15	14	10	19	15	18	4	0.78	0.60	1.00	0.67	0.50	0.50	111	31	5.9
20	17	11	21	17	21	4	0.89	0.40	1.00	0.67	0.50	1.00	145	39	11.4
22	17	11	27	18	23	4	0.89	0.60	1.00	0.33	0.50	1.00	162	54	13.0
17	8	10	25	26	25	4	1.00	0.40	1.00	1.00	0.00	1.00	163	49	10.8
14	9	7	25	18	22	7	0.67	0.30	0.83	0.67	0.00	1.00	59	34	12.3
24	22	22	23	22	22	4	1.00	0.50	0.83	0.67	0.50	0.50	187	46	11.3
17	20	12	17	19	23	7	0.89	0.20	0.50	0.00	0.00	1.00	109	55	11.8
23	20	18	28	17	24	4	0.89	0.30	0.83	0.00	0.50	1.00	90	42	7.9
25	22	20	25	26	25	4	0.89	0.50	0.17	0.00	0.00	1.00	105	50	12.7
16	22	9	20	21	22	4	0.78	0.70	0.83	1.00	0.50	1.00	83	13	12.3
18	22	16	25	26	24	4	0.89	0.40	1.00	0.67	1.00	0.50	116	37	11.6
20	16	14	21	21	21	8	0.78	0.30	1.00	0.00	0.00	0.50	42	25	6.7
18	14	11	24	12	24	4	0.78	0.20	0.67	0.67	1.00	1.00	148	30	10.9
23	24	20	28	20	25	4	1.00	0.50	1.00	0.00	0.00	0.50	155	28	12.1
24	21	17	20	20	23	4	0.78	0.40	1.00	0.00	0.50	0.00	125	45	13.3
23	20	14	19	24	27	4	1.00	0.60	1.00	0.67	1.00	1.00	116	35	10.1
13	20	8	24	24	27	7	0.78	0.40	0.83	1.00	0.00	1.00	128	28	5.7
20	18	16	21	22	23	12	0.67	0.40	0.33	0.00	0.00	0.50	138	41	14.3
20	14	11	24	21	18	4	0.33	0.20	0.33	0.33	0.00	0.00	49	6	8.0
19	19	10	23	20	20	4	1.00	0.90	1.00	0.67	0.50	1.00	96	45	13.3
22	24	15	18	23	23	4	1.00	0.80	1.00	0.67	1.00	0.50	164	73	9.3
22	19	15	27	19	24	5	0.78	0.80	0.83	0.00	0.50	1.00	162	17	12.5
15	16	10	25	24	26	15	0.67	0.30	1.00	1.00	0.50	1.00	99	40	7.6
17	16	10	20	21	20	5	1.00	0.20	0.83	0.67	0.00	0.50	202	64	15.9
19	16	18	21	16	23	10	0.89	0.40	0.67	0.00	0.50	1.00	186	37	9.2
20	14	10	23	17	22	9	0.89	0.20	0.83	1.00	0.00	1.00	66	25	9.1
22	22	22	27	23	23	8	0.78	0.20	0.67	0.67	0.50	1.00	183	65	11.1
21	21	16	24	20	17	4	1.00	0.10	0.83	0.67	0.00	1.00	214	100	13.0
21	15	10	27	19	20	5	0.56	0.40	0.67	1.00	0.50	0.00	188	28	14.5
16	14	7	24	18	22	4	0.67	0.50	1.00	0.00	0.50	0.50	104	35	12.2
20	15	16	23	18	18	9	0.89	0.80	0.83	0.33	0.50	1.00	177	56	12.3
21	14	16	24	21	19	4	0.89	0.40	0.67	0.67	0.00	0.50	126	29	11.4
20	20	16	21	20	19	10	0.89	0.60	0.83	0.33	0.50	0.50	76	43	8.8
23	21	22	23	17	16	4	0.89	0.50	0.83	0.67	0.50	1.00	99	59	14.6
18	14	5	27	25	26	4	0.78	0.30	0.67	0.00	0.00	0.00	139	50	12.6
22	19	18	24	15	14	6	0.89	0.80	1.00	1.00	0.50	1.00	78	3	NA
16	16	10	25	17	25	7	1.00	0.40	0.33	0.00	0.50	0.00	162	59	13.0
17	13	8	19	17	23	5	1.00	0.60	0.83	0.67	0.50	0.50	108	27	12.6
24	26	16	24	24	18	4	0.89	0.40	1.00	0.33	0.00	0.50	159	61	13.2
13	13	8	25	21	22	4	0.44	0.30	0.83	0.00	0.00	0.00	74	28	9.9
19	18	16	23	22	26	4	0.78	0.80	0.83	0.00	1.00	1.00	110	51	7.7
20	15	14	23	18	25	4	0.89	0.60	0.50	0.33	1.00	1.00	96	35	10.5
22	18	15	25	22	26	4	0.67	0.30	0.50	0.00	0.00	0.00	116	29	13.4
19	21	9	26	20	26	4	0.78	0.50	0.83	0.67	0.50	1.00	87	48	10.9
21	17	21	26	21	24	6	0.78	0.40	1.00	0.33	0.00	1.00	97	25	4.3
15	18	7	16	21	22	10	0.33	0.30	0.33	0.67	0.00	0.00	127	44	10.3
21	20	17	23	20	21	7	0.89	0.70	1.00	0.33	0.00	0.50	106	64	11.8
24	18	18	26	18	22	4	0.89	0.20	0.67	0.33	0.50	0.50	80	32	11.2
22	25	16	25	25	28	4	0.89	0.40	0.83	1.00	0.00	1.00	74	20	11.4
20	20	16	23	23	22	7	0.89	0.60	1.00	0.67	0.50	0.50	91	28	8.6
21	19	14	26	21	26	4	0.56	0.60	0.83	0.00	0.00	1.00	133	34	13.2
19	18	15	22	20	20	8	0.67	0.60	0.83	0.67	0.50	0.50	74	31	12.6
14	12	8	20	21	24	11	0.67	0.40	1.00	0.33	0.50	1.00	114	26	5.6
25	22	22	27	20	21	6	0.78	0.60	0.83	0.00	0.00	1.00	140	58	9.9
11	16	5	20	22	23	14	0.78	0.50	1.00	0.33	0.50	1.00	95	23	8.8
17	18	13	22	15	23	5	0.78	0.50	0.83	0.00	0.00	1.00	98	21	7.7
22	23	22	24	24	23	4	0.89	0.60	0.67	0.00	0.00	1.00	121	21	9.0
20	20	18	21	22	22	8	1.00	0.80	0.83	0.33	0.50	1.00	126	33	7.3
22	20	15	24	21	23	9	0.89	0.50	0.83	0.67	1.00	0.50	98	16	11.4
15	16	11	26	17	21	4	0.89	0.60	0.83	0.67	0.50	1.00	95	20	13.6
23	22	19	24	23	27	4	0.78	0.40	0.83	0.67	0.50	1.00	110	37	7.9
20	19	19	24	22	23	5	1.00	0.30	0.67	0.67	0.50	1.00	70	35	10.7
22	23	21	27	23	26	4	0.78	0.30	0.83	1.00	0.00	0.50	102	33	10.3
16	6	4	25	16	27	5	0.67	0.20	0.00	0.00	0.00	0.00	86	27	8.3
25	19	17	27	18	27	4	0.78	0.40	0.83	0.00	0.00	0.50	130	41	9.6
18	24	10	19	25	23	4	0.89	0.50	1.00	0.00	0.00	0.50	96	40	14.2
19	19	13	22	18	23	7	0.67	0.30	0.17	0.00	0.50	0.00	102	35	8.5
25	15	15	22	14	23	10	0.22	0.40	0.17	0.00	0.50	0.00	100	28	13.5
21	18	11	25	20	28	4	0.44	0.50	0.50	1.00	0.00	0.00	94	32	4.9
22	18	20	23	19	24	5	0.89	0.30	0.50	0.67	0.00	1.00	52	22	6.4
21	22	13	24	18	20	4	0.67	0.50	1.00	0.00	0.00	0.50	98	44	9.6
22	23	18	24	22	23	4	0.89	0.40	0.67	0.67	0.00	0.50	118	27	11.6
23	18	20	23	21	22	4	0.67	0.40	0.83	0.67	0.00	1.00	99	17	11.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 6.76155 + 0.0996572AMS.I1[t] -0.0375443AMS.I2[t] -0.153098AMS.I3[t] + 0.0998723AMS.E1[t] + 0.108527AMS.E2[t] -0.185196AMS.E3[t] + 0.0342312AMS.A[t] + 2.65346Calculation[t] + 0.611809Algebraic_Reasoning[t] -0.431151Graphical_Interpretation[t] -0.224449Proportionality_and_Ratio[t] + 0.222538Probability_and_Sampling[t] -0.400959Estimation[t] + 0.0110173LFM[t] + 0.0266158CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  6.76155 +  0.0996572AMS.I1[t] -0.0375443AMS.I2[t] -0.153098AMS.I3[t] +  0.0998723AMS.E1[t] +  0.108527AMS.E2[t] -0.185196AMS.E3[t] +  0.0342312AMS.A[t] +  2.65346Calculation[t] +  0.611809Algebraic_Reasoning[t] -0.431151Graphical_Interpretation[t] -0.224449Proportionality_and_Ratio[t] +  0.222538Probability_and_Sampling[t] -0.400959Estimation[t] +  0.0110173LFM[t] +  0.0266158CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  6.76155 +  0.0996572AMS.I1[t] -0.0375443AMS.I2[t] -0.153098AMS.I3[t] +  0.0998723AMS.E1[t] +  0.108527AMS.E2[t] -0.185196AMS.E3[t] +  0.0342312AMS.A[t] +  2.65346Calculation[t] +  0.611809Algebraic_Reasoning[t] -0.431151Graphical_Interpretation[t] -0.224449Proportionality_and_Ratio[t] +  0.222538Probability_and_Sampling[t] -0.400959Estimation[t] +  0.0110173LFM[t] +  0.0266158CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 6.76155 + 0.0996572AMS.I1[t] -0.0375443AMS.I2[t] -0.153098AMS.I3[t] + 0.0998723AMS.E1[t] + 0.108527AMS.E2[t] -0.185196AMS.E3[t] + 0.0342312AMS.A[t] + 2.65346Calculation[t] + 0.611809Algebraic_Reasoning[t] -0.431151Graphical_Interpretation[t] -0.224449Proportionality_and_Ratio[t] + 0.222538Probability_and_Sampling[t] -0.400959Estimation[t] + 0.0110173LFM[t] + 0.0266158CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.761553.270922.0670.04140940.0207047
AMS.I10.09965720.09884391.0080.3158790.15794
AMS.I2-0.03754430.0839155-0.44740.655590.327795
AMS.I3-0.1530980.0806592-1.8980.06068930.0303446
AMS.E10.09987230.09898941.0090.315550.157775
AMS.E20.1085270.08987081.2080.2301740.115087
AMS.E3-0.1851960.0940545-1.9690.05183290.0259165
AMS.A0.03423120.09883560.34630.7298420.364921
Calculation2.653461.706011.5550.123150.0615749
Algebraic_Reasoning0.6118091.494110.40950.6830990.341549
Graphical_Interpretation-0.4311511.12611-0.38290.7026640.351332
Proportionality_and_Ratio-0.2244490.694527-0.32320.7472710.373636
Probability_and_Sampling0.2225380.7843280.28370.7772280.388614
Estimation-0.4009590.68686-0.58380.5607530.280377
LFM0.01101730.007024611.5680.1200810.0600403
CH0.02661580.01708261.5580.1225080.0612539

\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) & 6.76155 & 3.27092 & 2.067 & 0.0414094 & 0.0207047 \tabularnewline
AMS.I1 & 0.0996572 & 0.0988439 & 1.008 & 0.315879 & 0.15794 \tabularnewline
AMS.I2 & -0.0375443 & 0.0839155 & -0.4474 & 0.65559 & 0.327795 \tabularnewline
AMS.I3 & -0.153098 & 0.0806592 & -1.898 & 0.0606893 & 0.0303446 \tabularnewline
AMS.E1 & 0.0998723 & 0.0989894 & 1.009 & 0.31555 & 0.157775 \tabularnewline
AMS.E2 & 0.108527 & 0.0898708 & 1.208 & 0.230174 & 0.115087 \tabularnewline
AMS.E3 & -0.185196 & 0.0940545 & -1.969 & 0.0518329 & 0.0259165 \tabularnewline
AMS.A & 0.0342312 & 0.0988356 & 0.3463 & 0.729842 & 0.364921 \tabularnewline
Calculation & 2.65346 & 1.70601 & 1.555 & 0.12315 & 0.0615749 \tabularnewline
Algebraic_Reasoning & 0.611809 & 1.49411 & 0.4095 & 0.683099 & 0.341549 \tabularnewline
Graphical_Interpretation & -0.431151 & 1.12611 & -0.3829 & 0.702664 & 0.351332 \tabularnewline
Proportionality_and_Ratio & -0.224449 & 0.694527 & -0.3232 & 0.747271 & 0.373636 \tabularnewline
Probability_and_Sampling & 0.222538 & 0.784328 & 0.2837 & 0.777228 & 0.388614 \tabularnewline
Estimation & -0.400959 & 0.68686 & -0.5838 & 0.560753 & 0.280377 \tabularnewline
LFM & 0.0110173 & 0.00702461 & 1.568 & 0.120081 & 0.0600403 \tabularnewline
CH & 0.0266158 & 0.0170826 & 1.558 & 0.122508 & 0.0612539 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&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]6.76155[/C][C]3.27092[/C][C]2.067[/C][C]0.0414094[/C][C]0.0207047[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0996572[/C][C]0.0988439[/C][C]1.008[/C][C]0.315879[/C][C]0.15794[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0375443[/C][C]0.0839155[/C][C]-0.4474[/C][C]0.65559[/C][C]0.327795[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.153098[/C][C]0.0806592[/C][C]-1.898[/C][C]0.0606893[/C][C]0.0303446[/C][/ROW]
[ROW][C]AMS.E1[/C][C]0.0998723[/C][C]0.0989894[/C][C]1.009[/C][C]0.31555[/C][C]0.157775[/C][/ROW]
[ROW][C]AMS.E2[/C][C]0.108527[/C][C]0.0898708[/C][C]1.208[/C][C]0.230174[/C][C]0.115087[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.185196[/C][C]0.0940545[/C][C]-1.969[/C][C]0.0518329[/C][C]0.0259165[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.0342312[/C][C]0.0988356[/C][C]0.3463[/C][C]0.729842[/C][C]0.364921[/C][/ROW]
[ROW][C]Calculation[/C][C]2.65346[/C][C]1.70601[/C][C]1.555[/C][C]0.12315[/C][C]0.0615749[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]0.611809[/C][C]1.49411[/C][C]0.4095[/C][C]0.683099[/C][C]0.341549[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-0.431151[/C][C]1.12611[/C][C]-0.3829[/C][C]0.702664[/C][C]0.351332[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]-0.224449[/C][C]0.694527[/C][C]-0.3232[/C][C]0.747271[/C][C]0.373636[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.222538[/C][C]0.784328[/C][C]0.2837[/C][C]0.777228[/C][C]0.388614[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.400959[/C][C]0.68686[/C][C]-0.5838[/C][C]0.560753[/C][C]0.280377[/C][/ROW]
[ROW][C]LFM[/C][C]0.0110173[/C][C]0.00702461[/C][C]1.568[/C][C]0.120081[/C][C]0.0600403[/C][/ROW]
[ROW][C]CH[/C][C]0.0266158[/C][C]0.0170826[/C][C]1.558[/C][C]0.122508[/C][C]0.0612539[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.761553.270922.0670.04140940.0207047
AMS.I10.09965720.09884391.0080.3158790.15794
AMS.I2-0.03754430.0839155-0.44740.655590.327795
AMS.I3-0.1530980.0806592-1.8980.06068930.0303446
AMS.E10.09987230.09898941.0090.315550.157775
AMS.E20.1085270.08987081.2080.2301740.115087
AMS.E3-0.1851960.0940545-1.9690.05183290.0259165
AMS.A0.03423120.09883560.34630.7298420.364921
Calculation2.653461.706011.5550.123150.0615749
Algebraic_Reasoning0.6118091.494110.40950.6830990.341549
Graphical_Interpretation-0.4311511.12611-0.38290.7026640.351332
Proportionality_and_Ratio-0.2244490.694527-0.32320.7472710.373636
Probability_and_Sampling0.2225380.7843280.28370.7772280.388614
Estimation-0.4009590.68686-0.58380.5607530.280377
LFM0.01101730.007024611.5680.1200810.0600403
CH0.02661580.01708261.5580.1225080.0612539






Multiple Linear Regression - Regression Statistics
Multiple R0.454494
R-squared0.206565
Adjusted R-squared0.0825907
F-TEST (value)1.66619
F-TEST (DF numerator)15
F-TEST (DF denominator)96
p-value0.0709681
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36709
Sum Squared Residuals537.897

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.454494 \tabularnewline
R-squared & 0.206565 \tabularnewline
Adjusted R-squared & 0.0825907 \tabularnewline
F-TEST (value) & 1.66619 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value & 0.0709681 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.36709 \tabularnewline
Sum Squared Residuals & 537.897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.454494[/C][/ROW]
[ROW][C]R-squared[/C][C]0.206565[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0825907[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.66619[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C]0.0709681[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.36709[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]537.897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.454494
R-squared0.206565
Adjusted R-squared0.0825907
F-TEST (value)1.66619
F-TEST (DF numerator)15
F-TEST (DF denominator)96
p-value0.0709681
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36709
Sum Squared Residuals537.897






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.78991.11014
212.210.51841.68162
312.811.18791.61207
47.411.6465-4.24653
56.710.3881-3.6881
612.612.58030.019746
714.811.96762.83243
813.311.98731.31268
911.111.3184-0.218397
108.212.0644-3.86439
1111.411.32240.0776391
126.411.8038-5.40377
1310.69.836250.763748
141213.4234-1.42338
156.39.94855-3.64855
1611.310.02321.27679
1711.911.23970.660304
189.310.002-0.702037
199.611.1269-1.52695
20109.823620.176382
216.49.47091-3.07091
2213.811.21882.58122
2310.810.8799-0.0798877
2413.811.20352.59649
2511.712.1737-0.473741
2610.911.0646-0.16462
2716.112.18063.9194
2813.411.03262.36743
299.99.95356-0.0535601
3011.511.05150.448473
318.39.89187-1.59187
3211.711.901-0.201039
33910.3814-1.38135
349.712.4207-2.7207
3510.810.44140.358572
3610.39.931020.368985
3710.410.7534-0.353443
3812.710.34992.35009
399.311.5541-2.25412
4011.812.6351-0.835148
415.910.3427-4.44274
4211.410.99310.406941
431312.31490.685052
4410.812.3915-1.59154
4512.39.969892.33011
4611.311.3529-0.052947
4711.810.13981.66022
487.99.88762-1.98762
4912.710.87141.82858
5012.39.377752.92225
5111.610.60020.999812
526.79.52146-2.82146
5310.99.740591.15941
5412.110.34531.75471
5513.310.3772.92295
5610.110.2684-0.168415
575.79.80895-4.10895
5814.310.55693.74307
5989.50559-1.50559
6013.311.89961.40044
619.312.2609-2.96094
6212.510.57811.92191
637.610.096-2.49596
6415.913.06382.8362
659.210.2769-1.07687
669.19.9671-0.867101
6711.111.1762-0.0762078
681313.9972-0.997157
6914.512.13112.36888
7012.210.81251.38749
7112.312.537-0.237029
7211.411.30.100022
738.810.835-2.03501
7414.610.74353.8565
7512.612.8224-0.222389
76NANA0.601826
771311.12861.87136
7812.612.20850.391496
7913.213.03650.163549
809.912.3283-2.42834
817.77.255550.444453
8210.57.083623.41638
8313.412.89150.508486
8410.915.3798-4.47979
854.33.990760.309237
8610.39.77430.5257
8711.810.69941.10058
8811.28.497892.70211
8911.413.1185-1.71847
908.65.059663.54034
9113.210.40832.79174
9212.616.8654-4.2654
935.66.70632-1.10632
949.911.4358-1.53585
958.89.88181-1.08181
967.78.23219-0.532194
97912.3203-3.32029
987.36.218081.08192
9911.47.970773.42923
10013.614.7617-1.1617
1017.96.839491.06051
10210.79.262481.43752
10310.312.4888-2.1888
1048.38.70323-0.40323
1059.66.169653.43035
10614.215.8369-1.63686
1078.53.906154.59385
10813.517.634-4.13399
1094.96.73843-1.83843
1106.47.32441-0.924413
1119.67.981911.61809
11211.69.111182.48882
11311.1NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.7899 & 1.11014 \tabularnewline
2 & 12.2 & 10.5184 & 1.68162 \tabularnewline
3 & 12.8 & 11.1879 & 1.61207 \tabularnewline
4 & 7.4 & 11.6465 & -4.24653 \tabularnewline
5 & 6.7 & 10.3881 & -3.6881 \tabularnewline
6 & 12.6 & 12.5803 & 0.019746 \tabularnewline
7 & 14.8 & 11.9676 & 2.83243 \tabularnewline
8 & 13.3 & 11.9873 & 1.31268 \tabularnewline
9 & 11.1 & 11.3184 & -0.218397 \tabularnewline
10 & 8.2 & 12.0644 & -3.86439 \tabularnewline
11 & 11.4 & 11.3224 & 0.0776391 \tabularnewline
12 & 6.4 & 11.8038 & -5.40377 \tabularnewline
13 & 10.6 & 9.83625 & 0.763748 \tabularnewline
14 & 12 & 13.4234 & -1.42338 \tabularnewline
15 & 6.3 & 9.94855 & -3.64855 \tabularnewline
16 & 11.3 & 10.0232 & 1.27679 \tabularnewline
17 & 11.9 & 11.2397 & 0.660304 \tabularnewline
18 & 9.3 & 10.002 & -0.702037 \tabularnewline
19 & 9.6 & 11.1269 & -1.52695 \tabularnewline
20 & 10 & 9.82362 & 0.176382 \tabularnewline
21 & 6.4 & 9.47091 & -3.07091 \tabularnewline
22 & 13.8 & 11.2188 & 2.58122 \tabularnewline
23 & 10.8 & 10.8799 & -0.0798877 \tabularnewline
24 & 13.8 & 11.2035 & 2.59649 \tabularnewline
25 & 11.7 & 12.1737 & -0.473741 \tabularnewline
26 & 10.9 & 11.0646 & -0.16462 \tabularnewline
27 & 16.1 & 12.1806 & 3.9194 \tabularnewline
28 & 13.4 & 11.0326 & 2.36743 \tabularnewline
29 & 9.9 & 9.95356 & -0.0535601 \tabularnewline
30 & 11.5 & 11.0515 & 0.448473 \tabularnewline
31 & 8.3 & 9.89187 & -1.59187 \tabularnewline
32 & 11.7 & 11.901 & -0.201039 \tabularnewline
33 & 9 & 10.3814 & -1.38135 \tabularnewline
34 & 9.7 & 12.4207 & -2.7207 \tabularnewline
35 & 10.8 & 10.4414 & 0.358572 \tabularnewline
36 & 10.3 & 9.93102 & 0.368985 \tabularnewline
37 & 10.4 & 10.7534 & -0.353443 \tabularnewline
38 & 12.7 & 10.3499 & 2.35009 \tabularnewline
39 & 9.3 & 11.5541 & -2.25412 \tabularnewline
40 & 11.8 & 12.6351 & -0.835148 \tabularnewline
41 & 5.9 & 10.3427 & -4.44274 \tabularnewline
42 & 11.4 & 10.9931 & 0.406941 \tabularnewline
43 & 13 & 12.3149 & 0.685052 \tabularnewline
44 & 10.8 & 12.3915 & -1.59154 \tabularnewline
45 & 12.3 & 9.96989 & 2.33011 \tabularnewline
46 & 11.3 & 11.3529 & -0.052947 \tabularnewline
47 & 11.8 & 10.1398 & 1.66022 \tabularnewline
48 & 7.9 & 9.88762 & -1.98762 \tabularnewline
49 & 12.7 & 10.8714 & 1.82858 \tabularnewline
50 & 12.3 & 9.37775 & 2.92225 \tabularnewline
51 & 11.6 & 10.6002 & 0.999812 \tabularnewline
52 & 6.7 & 9.52146 & -2.82146 \tabularnewline
53 & 10.9 & 9.74059 & 1.15941 \tabularnewline
54 & 12.1 & 10.3453 & 1.75471 \tabularnewline
55 & 13.3 & 10.377 & 2.92295 \tabularnewline
56 & 10.1 & 10.2684 & -0.168415 \tabularnewline
57 & 5.7 & 9.80895 & -4.10895 \tabularnewline
58 & 14.3 & 10.5569 & 3.74307 \tabularnewline
59 & 8 & 9.50559 & -1.50559 \tabularnewline
60 & 13.3 & 11.8996 & 1.40044 \tabularnewline
61 & 9.3 & 12.2609 & -2.96094 \tabularnewline
62 & 12.5 & 10.5781 & 1.92191 \tabularnewline
63 & 7.6 & 10.096 & -2.49596 \tabularnewline
64 & 15.9 & 13.0638 & 2.8362 \tabularnewline
65 & 9.2 & 10.2769 & -1.07687 \tabularnewline
66 & 9.1 & 9.9671 & -0.867101 \tabularnewline
67 & 11.1 & 11.1762 & -0.0762078 \tabularnewline
68 & 13 & 13.9972 & -0.997157 \tabularnewline
69 & 14.5 & 12.1311 & 2.36888 \tabularnewline
70 & 12.2 & 10.8125 & 1.38749 \tabularnewline
71 & 12.3 & 12.537 & -0.237029 \tabularnewline
72 & 11.4 & 11.3 & 0.100022 \tabularnewline
73 & 8.8 & 10.835 & -2.03501 \tabularnewline
74 & 14.6 & 10.7435 & 3.8565 \tabularnewline
75 & 12.6 & 12.8224 & -0.222389 \tabularnewline
76 & NA & NA & 0.601826 \tabularnewline
77 & 13 & 11.1286 & 1.87136 \tabularnewline
78 & 12.6 & 12.2085 & 0.391496 \tabularnewline
79 & 13.2 & 13.0365 & 0.163549 \tabularnewline
80 & 9.9 & 12.3283 & -2.42834 \tabularnewline
81 & 7.7 & 7.25555 & 0.444453 \tabularnewline
82 & 10.5 & 7.08362 & 3.41638 \tabularnewline
83 & 13.4 & 12.8915 & 0.508486 \tabularnewline
84 & 10.9 & 15.3798 & -4.47979 \tabularnewline
85 & 4.3 & 3.99076 & 0.309237 \tabularnewline
86 & 10.3 & 9.7743 & 0.5257 \tabularnewline
87 & 11.8 & 10.6994 & 1.10058 \tabularnewline
88 & 11.2 & 8.49789 & 2.70211 \tabularnewline
89 & 11.4 & 13.1185 & -1.71847 \tabularnewline
90 & 8.6 & 5.05966 & 3.54034 \tabularnewline
91 & 13.2 & 10.4083 & 2.79174 \tabularnewline
92 & 12.6 & 16.8654 & -4.2654 \tabularnewline
93 & 5.6 & 6.70632 & -1.10632 \tabularnewline
94 & 9.9 & 11.4358 & -1.53585 \tabularnewline
95 & 8.8 & 9.88181 & -1.08181 \tabularnewline
96 & 7.7 & 8.23219 & -0.532194 \tabularnewline
97 & 9 & 12.3203 & -3.32029 \tabularnewline
98 & 7.3 & 6.21808 & 1.08192 \tabularnewline
99 & 11.4 & 7.97077 & 3.42923 \tabularnewline
100 & 13.6 & 14.7617 & -1.1617 \tabularnewline
101 & 7.9 & 6.83949 & 1.06051 \tabularnewline
102 & 10.7 & 9.26248 & 1.43752 \tabularnewline
103 & 10.3 & 12.4888 & -2.1888 \tabularnewline
104 & 8.3 & 8.70323 & -0.40323 \tabularnewline
105 & 9.6 & 6.16965 & 3.43035 \tabularnewline
106 & 14.2 & 15.8369 & -1.63686 \tabularnewline
107 & 8.5 & 3.90615 & 4.59385 \tabularnewline
108 & 13.5 & 17.634 & -4.13399 \tabularnewline
109 & 4.9 & 6.73843 & -1.83843 \tabularnewline
110 & 6.4 & 7.32441 & -0.924413 \tabularnewline
111 & 9.6 & 7.98191 & 1.61809 \tabularnewline
112 & 11.6 & 9.11118 & 2.48882 \tabularnewline
113 & 11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&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]12.9[/C][C]11.7899[/C][C]1.11014[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5184[/C][C]1.68162[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.1879[/C][C]1.61207[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6465[/C][C]-4.24653[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.3881[/C][C]-3.6881[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.5803[/C][C]0.019746[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.9676[/C][C]2.83243[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.9873[/C][C]1.31268[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]11.3184[/C][C]-0.218397[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]12.0644[/C][C]-3.86439[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.3224[/C][C]0.0776391[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.8038[/C][C]-5.40377[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.83625[/C][C]0.763748[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.4234[/C][C]-1.42338[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.94855[/C][C]-3.64855[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.0232[/C][C]1.27679[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.2397[/C][C]0.660304[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.002[/C][C]-0.702037[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.1269[/C][C]-1.52695[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.82362[/C][C]0.176382[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]9.47091[/C][C]-3.07091[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.2188[/C][C]2.58122[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.8799[/C][C]-0.0798877[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]11.2035[/C][C]2.59649[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]12.1737[/C][C]-0.473741[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]11.0646[/C][C]-0.16462[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]12.1806[/C][C]3.9194[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]11.0326[/C][C]2.36743[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]9.95356[/C][C]-0.0535601[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.0515[/C][C]0.448473[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]9.89187[/C][C]-1.59187[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]11.901[/C][C]-0.201039[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.3814[/C][C]-1.38135[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]12.4207[/C][C]-2.7207[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.4414[/C][C]0.358572[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]9.93102[/C][C]0.368985[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.7534[/C][C]-0.353443[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.3499[/C][C]2.35009[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]11.5541[/C][C]-2.25412[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]12.6351[/C][C]-0.835148[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.3427[/C][C]-4.44274[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.9931[/C][C]0.406941[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]12.3149[/C][C]0.685052[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]12.3915[/C][C]-1.59154[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]9.96989[/C][C]2.33011[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]11.3529[/C][C]-0.052947[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.1398[/C][C]1.66022[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]9.88762[/C][C]-1.98762[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.8714[/C][C]1.82858[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]9.37775[/C][C]2.92225[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.6002[/C][C]0.999812[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]9.52146[/C][C]-2.82146[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.74059[/C][C]1.15941[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.3453[/C][C]1.75471[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.377[/C][C]2.92295[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.2684[/C][C]-0.168415[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]9.80895[/C][C]-4.10895[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.5569[/C][C]3.74307[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]9.50559[/C][C]-1.50559[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]11.8996[/C][C]1.40044[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]12.2609[/C][C]-2.96094[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.5781[/C][C]1.92191[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]10.096[/C][C]-2.49596[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]13.0638[/C][C]2.8362[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.2769[/C][C]-1.07687[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]9.9671[/C][C]-0.867101[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]11.1762[/C][C]-0.0762078[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.9972[/C][C]-0.997157[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]12.1311[/C][C]2.36888[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.8125[/C][C]1.38749[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]12.537[/C][C]-0.237029[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]11.3[/C][C]0.100022[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.835[/C][C]-2.03501[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]10.7435[/C][C]3.8565[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]12.8224[/C][C]-0.222389[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]0.601826[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.1286[/C][C]1.87136[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]12.2085[/C][C]0.391496[/C][/ROW]
[ROW][C]79[/C][C]13.2[/C][C]13.0365[/C][C]0.163549[/C][/ROW]
[ROW][C]80[/C][C]9.9[/C][C]12.3283[/C][C]-2.42834[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]7.25555[/C][C]0.444453[/C][/ROW]
[ROW][C]82[/C][C]10.5[/C][C]7.08362[/C][C]3.41638[/C][/ROW]
[ROW][C]83[/C][C]13.4[/C][C]12.8915[/C][C]0.508486[/C][/ROW]
[ROW][C]84[/C][C]10.9[/C][C]15.3798[/C][C]-4.47979[/C][/ROW]
[ROW][C]85[/C][C]4.3[/C][C]3.99076[/C][C]0.309237[/C][/ROW]
[ROW][C]86[/C][C]10.3[/C][C]9.7743[/C][C]0.5257[/C][/ROW]
[ROW][C]87[/C][C]11.8[/C][C]10.6994[/C][C]1.10058[/C][/ROW]
[ROW][C]88[/C][C]11.2[/C][C]8.49789[/C][C]2.70211[/C][/ROW]
[ROW][C]89[/C][C]11.4[/C][C]13.1185[/C][C]-1.71847[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]5.05966[/C][C]3.54034[/C][/ROW]
[ROW][C]91[/C][C]13.2[/C][C]10.4083[/C][C]2.79174[/C][/ROW]
[ROW][C]92[/C][C]12.6[/C][C]16.8654[/C][C]-4.2654[/C][/ROW]
[ROW][C]93[/C][C]5.6[/C][C]6.70632[/C][C]-1.10632[/C][/ROW]
[ROW][C]94[/C][C]9.9[/C][C]11.4358[/C][C]-1.53585[/C][/ROW]
[ROW][C]95[/C][C]8.8[/C][C]9.88181[/C][C]-1.08181[/C][/ROW]
[ROW][C]96[/C][C]7.7[/C][C]8.23219[/C][C]-0.532194[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]12.3203[/C][C]-3.32029[/C][/ROW]
[ROW][C]98[/C][C]7.3[/C][C]6.21808[/C][C]1.08192[/C][/ROW]
[ROW][C]99[/C][C]11.4[/C][C]7.97077[/C][C]3.42923[/C][/ROW]
[ROW][C]100[/C][C]13.6[/C][C]14.7617[/C][C]-1.1617[/C][/ROW]
[ROW][C]101[/C][C]7.9[/C][C]6.83949[/C][C]1.06051[/C][/ROW]
[ROW][C]102[/C][C]10.7[/C][C]9.26248[/C][C]1.43752[/C][/ROW]
[ROW][C]103[/C][C]10.3[/C][C]12.4888[/C][C]-2.1888[/C][/ROW]
[ROW][C]104[/C][C]8.3[/C][C]8.70323[/C][C]-0.40323[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]6.16965[/C][C]3.43035[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]15.8369[/C][C]-1.63686[/C][/ROW]
[ROW][C]107[/C][C]8.5[/C][C]3.90615[/C][C]4.59385[/C][/ROW]
[ROW][C]108[/C][C]13.5[/C][C]17.634[/C][C]-4.13399[/C][/ROW]
[ROW][C]109[/C][C]4.9[/C][C]6.73843[/C][C]-1.83843[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]7.32441[/C][C]-0.924413[/C][/ROW]
[ROW][C]111[/C][C]9.6[/C][C]7.98191[/C][C]1.61809[/C][/ROW]
[ROW][C]112[/C][C]11.6[/C][C]9.11118[/C][C]2.48882[/C][/ROW]
[ROW][C]113[/C][C]11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.78991.11014
212.210.51841.68162
312.811.18791.61207
47.411.6465-4.24653
56.710.3881-3.6881
612.612.58030.019746
714.811.96762.83243
813.311.98731.31268
911.111.3184-0.218397
108.212.0644-3.86439
1111.411.32240.0776391
126.411.8038-5.40377
1310.69.836250.763748
141213.4234-1.42338
156.39.94855-3.64855
1611.310.02321.27679
1711.911.23970.660304
189.310.002-0.702037
199.611.1269-1.52695
20109.823620.176382
216.49.47091-3.07091
2213.811.21882.58122
2310.810.8799-0.0798877
2413.811.20352.59649
2511.712.1737-0.473741
2610.911.0646-0.16462
2716.112.18063.9194
2813.411.03262.36743
299.99.95356-0.0535601
3011.511.05150.448473
318.39.89187-1.59187
3211.711.901-0.201039
33910.3814-1.38135
349.712.4207-2.7207
3510.810.44140.358572
3610.39.931020.368985
3710.410.7534-0.353443
3812.710.34992.35009
399.311.5541-2.25412
4011.812.6351-0.835148
415.910.3427-4.44274
4211.410.99310.406941
431312.31490.685052
4410.812.3915-1.59154
4512.39.969892.33011
4611.311.3529-0.052947
4711.810.13981.66022
487.99.88762-1.98762
4912.710.87141.82858
5012.39.377752.92225
5111.610.60020.999812
526.79.52146-2.82146
5310.99.740591.15941
5412.110.34531.75471
5513.310.3772.92295
5610.110.2684-0.168415
575.79.80895-4.10895
5814.310.55693.74307
5989.50559-1.50559
6013.311.89961.40044
619.312.2609-2.96094
6212.510.57811.92191
637.610.096-2.49596
6415.913.06382.8362
659.210.2769-1.07687
669.19.9671-0.867101
6711.111.1762-0.0762078
681313.9972-0.997157
6914.512.13112.36888
7012.210.81251.38749
7112.312.537-0.237029
7211.411.30.100022
738.810.835-2.03501
7414.610.74353.8565
7512.612.8224-0.222389
76NANA0.601826
771311.12861.87136
7812.612.20850.391496
7913.213.03650.163549
809.912.3283-2.42834
817.77.255550.444453
8210.57.083623.41638
8313.412.89150.508486
8410.915.3798-4.47979
854.33.990760.309237
8610.39.77430.5257
8711.810.69941.10058
8811.28.497892.70211
8911.413.1185-1.71847
908.65.059663.54034
9113.210.40832.79174
9212.616.8654-4.2654
935.66.70632-1.10632
949.911.4358-1.53585
958.89.88181-1.08181
967.78.23219-0.532194
97912.3203-3.32029
987.36.218081.08192
9911.47.970773.42923
10013.614.7617-1.1617
1017.96.839491.06051
10210.79.262481.43752
10310.312.4888-2.1888
1048.38.70323-0.40323
1059.66.169653.43035
10614.215.8369-1.63686
1078.53.906154.59385
10813.517.634-4.13399
1094.96.73843-1.83843
1106.47.32441-0.924413
1119.67.981911.61809
11211.69.111182.48882
11311.1NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.4303680.8607360.569632
200.3721040.7442080.627896
210.2538740.5077480.746126
220.2919790.5839580.708021
230.2482410.4964830.751759
240.2230370.4460740.776963
250.1497960.2995930.850204
260.1130580.2261170.886942
270.09640530.1928110.903595
280.06469750.1293950.935303
290.06494090.1298820.935059
300.04739950.0947990.952601
310.07213380.1442680.927866
320.05313530.1062710.946865
330.09526890.1905380.904731
340.2537850.5075690.746215
350.2218710.4437430.778129
360.1774040.3548080.822596
370.2255160.4510330.774484
380.2770770.5541540.722923
390.3000380.6000760.699962
400.243160.486320.75684
410.3400260.6800520.659974
420.3021640.6043290.697836
430.2619370.5238750.738063
440.262940.525880.73706
450.2772090.5544190.722791
460.2432920.4865830.756708
470.2241610.4483220.775839
480.2054170.4108340.794583
490.2580220.5160450.741978
500.3203980.6407960.679602
510.2812430.5624870.718757
520.2841370.5682730.715863
530.236030.4720590.76397
540.2435130.4870250.756487
550.3242240.6484480.675776
560.2759510.5519030.724049
570.4441760.8883510.555824
580.5501060.8997890.449894
590.6041220.7917570.395878
600.574960.8500810.42504
610.6134620.7730760.386538
620.565450.8690990.43455
630.6276380.7447240.372362
640.6640510.6718990.335949
650.6307360.7385290.369264
660.5694280.8611430.430572
670.5168820.9662350.483118
680.4614370.9228730.538563
690.4518810.9037630.548119
700.3931770.7863550.606823
710.3349560.6699120.665044
720.2826420.5652840.717358
730.2532010.5064020.746799
740.2953680.5907370.704632
750.2370390.4740780.762961
760.2391710.4783420.760829
770.2343530.4687050.765647
780.2304720.4609440.769528
790.1788270.3576550.821173
800.1473620.2947240.852638
810.1135220.2270440.886478
820.1444720.2889430.855528
830.116610.233220.88339
840.133520.2670410.86648
850.09645360.1929070.903546
860.1385210.2770420.861479
870.1044860.2089720.895514
880.07746590.1549320.922534
890.0591540.1183080.940846
900.07369790.1473960.926302
910.05135640.1027130.948644
920.07346780.1469360.926532
930.03719670.07439340.962803
940.01905080.03810160.980949

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.430368 & 0.860736 & 0.569632 \tabularnewline
20 & 0.372104 & 0.744208 & 0.627896 \tabularnewline
21 & 0.253874 & 0.507748 & 0.746126 \tabularnewline
22 & 0.291979 & 0.583958 & 0.708021 \tabularnewline
23 & 0.248241 & 0.496483 & 0.751759 \tabularnewline
24 & 0.223037 & 0.446074 & 0.776963 \tabularnewline
25 & 0.149796 & 0.299593 & 0.850204 \tabularnewline
26 & 0.113058 & 0.226117 & 0.886942 \tabularnewline
27 & 0.0964053 & 0.192811 & 0.903595 \tabularnewline
28 & 0.0646975 & 0.129395 & 0.935303 \tabularnewline
29 & 0.0649409 & 0.129882 & 0.935059 \tabularnewline
30 & 0.0473995 & 0.094799 & 0.952601 \tabularnewline
31 & 0.0721338 & 0.144268 & 0.927866 \tabularnewline
32 & 0.0531353 & 0.106271 & 0.946865 \tabularnewline
33 & 0.0952689 & 0.190538 & 0.904731 \tabularnewline
34 & 0.253785 & 0.507569 & 0.746215 \tabularnewline
35 & 0.221871 & 0.443743 & 0.778129 \tabularnewline
36 & 0.177404 & 0.354808 & 0.822596 \tabularnewline
37 & 0.225516 & 0.451033 & 0.774484 \tabularnewline
38 & 0.277077 & 0.554154 & 0.722923 \tabularnewline
39 & 0.300038 & 0.600076 & 0.699962 \tabularnewline
40 & 0.24316 & 0.48632 & 0.75684 \tabularnewline
41 & 0.340026 & 0.680052 & 0.659974 \tabularnewline
42 & 0.302164 & 0.604329 & 0.697836 \tabularnewline
43 & 0.261937 & 0.523875 & 0.738063 \tabularnewline
44 & 0.26294 & 0.52588 & 0.73706 \tabularnewline
45 & 0.277209 & 0.554419 & 0.722791 \tabularnewline
46 & 0.243292 & 0.486583 & 0.756708 \tabularnewline
47 & 0.224161 & 0.448322 & 0.775839 \tabularnewline
48 & 0.205417 & 0.410834 & 0.794583 \tabularnewline
49 & 0.258022 & 0.516045 & 0.741978 \tabularnewline
50 & 0.320398 & 0.640796 & 0.679602 \tabularnewline
51 & 0.281243 & 0.562487 & 0.718757 \tabularnewline
52 & 0.284137 & 0.568273 & 0.715863 \tabularnewline
53 & 0.23603 & 0.472059 & 0.76397 \tabularnewline
54 & 0.243513 & 0.487025 & 0.756487 \tabularnewline
55 & 0.324224 & 0.648448 & 0.675776 \tabularnewline
56 & 0.275951 & 0.551903 & 0.724049 \tabularnewline
57 & 0.444176 & 0.888351 & 0.555824 \tabularnewline
58 & 0.550106 & 0.899789 & 0.449894 \tabularnewline
59 & 0.604122 & 0.791757 & 0.395878 \tabularnewline
60 & 0.57496 & 0.850081 & 0.42504 \tabularnewline
61 & 0.613462 & 0.773076 & 0.386538 \tabularnewline
62 & 0.56545 & 0.869099 & 0.43455 \tabularnewline
63 & 0.627638 & 0.744724 & 0.372362 \tabularnewline
64 & 0.664051 & 0.671899 & 0.335949 \tabularnewline
65 & 0.630736 & 0.738529 & 0.369264 \tabularnewline
66 & 0.569428 & 0.861143 & 0.430572 \tabularnewline
67 & 0.516882 & 0.966235 & 0.483118 \tabularnewline
68 & 0.461437 & 0.922873 & 0.538563 \tabularnewline
69 & 0.451881 & 0.903763 & 0.548119 \tabularnewline
70 & 0.393177 & 0.786355 & 0.606823 \tabularnewline
71 & 0.334956 & 0.669912 & 0.665044 \tabularnewline
72 & 0.282642 & 0.565284 & 0.717358 \tabularnewline
73 & 0.253201 & 0.506402 & 0.746799 \tabularnewline
74 & 0.295368 & 0.590737 & 0.704632 \tabularnewline
75 & 0.237039 & 0.474078 & 0.762961 \tabularnewline
76 & 0.239171 & 0.478342 & 0.760829 \tabularnewline
77 & 0.234353 & 0.468705 & 0.765647 \tabularnewline
78 & 0.230472 & 0.460944 & 0.769528 \tabularnewline
79 & 0.178827 & 0.357655 & 0.821173 \tabularnewline
80 & 0.147362 & 0.294724 & 0.852638 \tabularnewline
81 & 0.113522 & 0.227044 & 0.886478 \tabularnewline
82 & 0.144472 & 0.288943 & 0.855528 \tabularnewline
83 & 0.11661 & 0.23322 & 0.88339 \tabularnewline
84 & 0.13352 & 0.267041 & 0.86648 \tabularnewline
85 & 0.0964536 & 0.192907 & 0.903546 \tabularnewline
86 & 0.138521 & 0.277042 & 0.861479 \tabularnewline
87 & 0.104486 & 0.208972 & 0.895514 \tabularnewline
88 & 0.0774659 & 0.154932 & 0.922534 \tabularnewline
89 & 0.059154 & 0.118308 & 0.940846 \tabularnewline
90 & 0.0736979 & 0.147396 & 0.926302 \tabularnewline
91 & 0.0513564 & 0.102713 & 0.948644 \tabularnewline
92 & 0.0734678 & 0.146936 & 0.926532 \tabularnewline
93 & 0.0371967 & 0.0743934 & 0.962803 \tabularnewline
94 & 0.0190508 & 0.0381016 & 0.980949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]19[/C][C]0.430368[/C][C]0.860736[/C][C]0.569632[/C][/ROW]
[ROW][C]20[/C][C]0.372104[/C][C]0.744208[/C][C]0.627896[/C][/ROW]
[ROW][C]21[/C][C]0.253874[/C][C]0.507748[/C][C]0.746126[/C][/ROW]
[ROW][C]22[/C][C]0.291979[/C][C]0.583958[/C][C]0.708021[/C][/ROW]
[ROW][C]23[/C][C]0.248241[/C][C]0.496483[/C][C]0.751759[/C][/ROW]
[ROW][C]24[/C][C]0.223037[/C][C]0.446074[/C][C]0.776963[/C][/ROW]
[ROW][C]25[/C][C]0.149796[/C][C]0.299593[/C][C]0.850204[/C][/ROW]
[ROW][C]26[/C][C]0.113058[/C][C]0.226117[/C][C]0.886942[/C][/ROW]
[ROW][C]27[/C][C]0.0964053[/C][C]0.192811[/C][C]0.903595[/C][/ROW]
[ROW][C]28[/C][C]0.0646975[/C][C]0.129395[/C][C]0.935303[/C][/ROW]
[ROW][C]29[/C][C]0.0649409[/C][C]0.129882[/C][C]0.935059[/C][/ROW]
[ROW][C]30[/C][C]0.0473995[/C][C]0.094799[/C][C]0.952601[/C][/ROW]
[ROW][C]31[/C][C]0.0721338[/C][C]0.144268[/C][C]0.927866[/C][/ROW]
[ROW][C]32[/C][C]0.0531353[/C][C]0.106271[/C][C]0.946865[/C][/ROW]
[ROW][C]33[/C][C]0.0952689[/C][C]0.190538[/C][C]0.904731[/C][/ROW]
[ROW][C]34[/C][C]0.253785[/C][C]0.507569[/C][C]0.746215[/C][/ROW]
[ROW][C]35[/C][C]0.221871[/C][C]0.443743[/C][C]0.778129[/C][/ROW]
[ROW][C]36[/C][C]0.177404[/C][C]0.354808[/C][C]0.822596[/C][/ROW]
[ROW][C]37[/C][C]0.225516[/C][C]0.451033[/C][C]0.774484[/C][/ROW]
[ROW][C]38[/C][C]0.277077[/C][C]0.554154[/C][C]0.722923[/C][/ROW]
[ROW][C]39[/C][C]0.300038[/C][C]0.600076[/C][C]0.699962[/C][/ROW]
[ROW][C]40[/C][C]0.24316[/C][C]0.48632[/C][C]0.75684[/C][/ROW]
[ROW][C]41[/C][C]0.340026[/C][C]0.680052[/C][C]0.659974[/C][/ROW]
[ROW][C]42[/C][C]0.302164[/C][C]0.604329[/C][C]0.697836[/C][/ROW]
[ROW][C]43[/C][C]0.261937[/C][C]0.523875[/C][C]0.738063[/C][/ROW]
[ROW][C]44[/C][C]0.26294[/C][C]0.52588[/C][C]0.73706[/C][/ROW]
[ROW][C]45[/C][C]0.277209[/C][C]0.554419[/C][C]0.722791[/C][/ROW]
[ROW][C]46[/C][C]0.243292[/C][C]0.486583[/C][C]0.756708[/C][/ROW]
[ROW][C]47[/C][C]0.224161[/C][C]0.448322[/C][C]0.775839[/C][/ROW]
[ROW][C]48[/C][C]0.205417[/C][C]0.410834[/C][C]0.794583[/C][/ROW]
[ROW][C]49[/C][C]0.258022[/C][C]0.516045[/C][C]0.741978[/C][/ROW]
[ROW][C]50[/C][C]0.320398[/C][C]0.640796[/C][C]0.679602[/C][/ROW]
[ROW][C]51[/C][C]0.281243[/C][C]0.562487[/C][C]0.718757[/C][/ROW]
[ROW][C]52[/C][C]0.284137[/C][C]0.568273[/C][C]0.715863[/C][/ROW]
[ROW][C]53[/C][C]0.23603[/C][C]0.472059[/C][C]0.76397[/C][/ROW]
[ROW][C]54[/C][C]0.243513[/C][C]0.487025[/C][C]0.756487[/C][/ROW]
[ROW][C]55[/C][C]0.324224[/C][C]0.648448[/C][C]0.675776[/C][/ROW]
[ROW][C]56[/C][C]0.275951[/C][C]0.551903[/C][C]0.724049[/C][/ROW]
[ROW][C]57[/C][C]0.444176[/C][C]0.888351[/C][C]0.555824[/C][/ROW]
[ROW][C]58[/C][C]0.550106[/C][C]0.899789[/C][C]0.449894[/C][/ROW]
[ROW][C]59[/C][C]0.604122[/C][C]0.791757[/C][C]0.395878[/C][/ROW]
[ROW][C]60[/C][C]0.57496[/C][C]0.850081[/C][C]0.42504[/C][/ROW]
[ROW][C]61[/C][C]0.613462[/C][C]0.773076[/C][C]0.386538[/C][/ROW]
[ROW][C]62[/C][C]0.56545[/C][C]0.869099[/C][C]0.43455[/C][/ROW]
[ROW][C]63[/C][C]0.627638[/C][C]0.744724[/C][C]0.372362[/C][/ROW]
[ROW][C]64[/C][C]0.664051[/C][C]0.671899[/C][C]0.335949[/C][/ROW]
[ROW][C]65[/C][C]0.630736[/C][C]0.738529[/C][C]0.369264[/C][/ROW]
[ROW][C]66[/C][C]0.569428[/C][C]0.861143[/C][C]0.430572[/C][/ROW]
[ROW][C]67[/C][C]0.516882[/C][C]0.966235[/C][C]0.483118[/C][/ROW]
[ROW][C]68[/C][C]0.461437[/C][C]0.922873[/C][C]0.538563[/C][/ROW]
[ROW][C]69[/C][C]0.451881[/C][C]0.903763[/C][C]0.548119[/C][/ROW]
[ROW][C]70[/C][C]0.393177[/C][C]0.786355[/C][C]0.606823[/C][/ROW]
[ROW][C]71[/C][C]0.334956[/C][C]0.669912[/C][C]0.665044[/C][/ROW]
[ROW][C]72[/C][C]0.282642[/C][C]0.565284[/C][C]0.717358[/C][/ROW]
[ROW][C]73[/C][C]0.253201[/C][C]0.506402[/C][C]0.746799[/C][/ROW]
[ROW][C]74[/C][C]0.295368[/C][C]0.590737[/C][C]0.704632[/C][/ROW]
[ROW][C]75[/C][C]0.237039[/C][C]0.474078[/C][C]0.762961[/C][/ROW]
[ROW][C]76[/C][C]0.239171[/C][C]0.478342[/C][C]0.760829[/C][/ROW]
[ROW][C]77[/C][C]0.234353[/C][C]0.468705[/C][C]0.765647[/C][/ROW]
[ROW][C]78[/C][C]0.230472[/C][C]0.460944[/C][C]0.769528[/C][/ROW]
[ROW][C]79[/C][C]0.178827[/C][C]0.357655[/C][C]0.821173[/C][/ROW]
[ROW][C]80[/C][C]0.147362[/C][C]0.294724[/C][C]0.852638[/C][/ROW]
[ROW][C]81[/C][C]0.113522[/C][C]0.227044[/C][C]0.886478[/C][/ROW]
[ROW][C]82[/C][C]0.144472[/C][C]0.288943[/C][C]0.855528[/C][/ROW]
[ROW][C]83[/C][C]0.11661[/C][C]0.23322[/C][C]0.88339[/C][/ROW]
[ROW][C]84[/C][C]0.13352[/C][C]0.267041[/C][C]0.86648[/C][/ROW]
[ROW][C]85[/C][C]0.0964536[/C][C]0.192907[/C][C]0.903546[/C][/ROW]
[ROW][C]86[/C][C]0.138521[/C][C]0.277042[/C][C]0.861479[/C][/ROW]
[ROW][C]87[/C][C]0.104486[/C][C]0.208972[/C][C]0.895514[/C][/ROW]
[ROW][C]88[/C][C]0.0774659[/C][C]0.154932[/C][C]0.922534[/C][/ROW]
[ROW][C]89[/C][C]0.059154[/C][C]0.118308[/C][C]0.940846[/C][/ROW]
[ROW][C]90[/C][C]0.0736979[/C][C]0.147396[/C][C]0.926302[/C][/ROW]
[ROW][C]91[/C][C]0.0513564[/C][C]0.102713[/C][C]0.948644[/C][/ROW]
[ROW][C]92[/C][C]0.0734678[/C][C]0.146936[/C][C]0.926532[/C][/ROW]
[ROW][C]93[/C][C]0.0371967[/C][C]0.0743934[/C][C]0.962803[/C][/ROW]
[ROW][C]94[/C][C]0.0190508[/C][C]0.0381016[/C][C]0.980949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.4303680.8607360.569632
200.3721040.7442080.627896
210.2538740.5077480.746126
220.2919790.5839580.708021
230.2482410.4964830.751759
240.2230370.4460740.776963
250.1497960.2995930.850204
260.1130580.2261170.886942
270.09640530.1928110.903595
280.06469750.1293950.935303
290.06494090.1298820.935059
300.04739950.0947990.952601
310.07213380.1442680.927866
320.05313530.1062710.946865
330.09526890.1905380.904731
340.2537850.5075690.746215
350.2218710.4437430.778129
360.1774040.3548080.822596
370.2255160.4510330.774484
380.2770770.5541540.722923
390.3000380.6000760.699962
400.243160.486320.75684
410.3400260.6800520.659974
420.3021640.6043290.697836
430.2619370.5238750.738063
440.262940.525880.73706
450.2772090.5544190.722791
460.2432920.4865830.756708
470.2241610.4483220.775839
480.2054170.4108340.794583
490.2580220.5160450.741978
500.3203980.6407960.679602
510.2812430.5624870.718757
520.2841370.5682730.715863
530.236030.4720590.76397
540.2435130.4870250.756487
550.3242240.6484480.675776
560.2759510.5519030.724049
570.4441760.8883510.555824
580.5501060.8997890.449894
590.6041220.7917570.395878
600.574960.8500810.42504
610.6134620.7730760.386538
620.565450.8690990.43455
630.6276380.7447240.372362
640.6640510.6718990.335949
650.6307360.7385290.369264
660.5694280.8611430.430572
670.5168820.9662350.483118
680.4614370.9228730.538563
690.4518810.9037630.548119
700.3931770.7863550.606823
710.3349560.6699120.665044
720.2826420.5652840.717358
730.2532010.5064020.746799
740.2953680.5907370.704632
750.2370390.4740780.762961
760.2391710.4783420.760829
770.2343530.4687050.765647
780.2304720.4609440.769528
790.1788270.3576550.821173
800.1473620.2947240.852638
810.1135220.2270440.886478
820.1444720.2889430.855528
830.116610.233220.88339
840.133520.2670410.86648
850.09645360.1929070.903546
860.1385210.2770420.861479
870.1044860.2089720.895514
880.07746590.1549320.922534
890.0591540.1183080.940846
900.07369790.1473960.926302
910.05135640.1027130.948644
920.07346780.1469360.926532
930.03719670.07439340.962803
940.01905080.03810160.980949






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0131579OK
10% type I error level30.0394737OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0131579 & OK \tabularnewline
10% type I error level & 3 & 0.0394737 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270098&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0131579[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0394737[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270098&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270098&T=6

As an alternative you can also use a QR Code:  
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 level00OK
5% type I error level10.0131579OK
10% type I error level30.0394737OK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 16 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 16 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}