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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 13:16:05 -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/20/t1356027395qs1ozqaqasz6qeb.htm/, Retrieved Thu, 28 Mar 2024 18:40:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202987, Retrieved Thu, 28 Mar 2024 18:40:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Multiple Regressi...] [2009-12-15 19:20:51] [1eab65e90adf64584b8e6f0da23ff414]
- R           [Multiple Regression] [multiple regressi...] [2012-12-20 18:16:05] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
103.34	98.60	96.33
102.60	96.90	96.33
100.69	95.10	95.05
105.67	97.00	96.84
123.61	112.70	96.92
113.08	102.90	97.44
106.46	97.40	97.78
123.38	111.40	97.69
109.87	87.40	96.67
95.74	96.80	98.29
123.06	114.10	98.20
123.39	110.30	98.71
120.28	103.90	98.54
115.33	101.60	98.20
110.40	94.60	100.80
114.49	95.90	101.33
132.03	104.70	101.88
123.16	102.80	101.85
118.82	98.10	102.04
128.32	113.90	102.22
112.24	80.90	102.63
104.53	95.70	102.65
132.57	113.20	102.54
122.52	105.90	102.37
131.80	108.80	102.68
124.55	102.30	102.76
120.96	99.00	102.82
122.60	100.70	103.31
145.52	115.50	103.23
118.57	100.70	103.60
134.25	109.90	103.95
136.70	114.60	103.93
121.37	85.40	104.25
111.63	100.50	104.38
134.42	114.80	104.36
137.65	116.50	104.32
137.86	112.90	104.58
119.77	102.00	104.68
130.69	106.00	104.92
128.28	105.30	105.46
147.45	118.80	105.23
128.42	106.10	105.58
136.90	109.30	105.34
143.95	117.20	105.28
135.64	92.50	105.70
122.48	104.20	105.67
136.83	112.50	105.71
153.04	122.40	106.19
142.71	113.30	106.93
123.46	100.00	107.44
144.37	110.70	107.85
146.15	112.80	108.71
147.61	109.80	109.32
158.51	117.30	109.49
147.40	109.10	110.20
165.05	115.90	110.62
154.64	96.00	111.22
126.20	99.80	110.88
157.36	116.80	111.15
154.15	115.70	111.29
123.21	99.40	111.09
113.07	94.30	111.24
110.45	91.00	111.45
113.57	93.20	111.75
122.44	103.10	111.07
114.93	94.10	111.17
111.85	91.80	110.96
126.04	102.70	110.50
121.34	82.60	110.48
124.36	89.10	110.66




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&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]9 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=202987&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Uitvoer[t] = -331.141725919585 + 1.70620646635698TIP[t] + 2.72941331902169`index/cons`[t] + 3.85690559007307M1[t] + 5.18062213837092M2[t] + 7.79403001839284M3[t] + 5.82862661097082M4[t] + 3.69147814714131M5[t] + 4.56365482328584M6[t] + 6.54365000251388M7[t] + 1.06346039581491M8[t] + 32.5649038799904M9[t] + 3.02441373941594M10[t] -1.30786415664453M11[t] -0.303331431381687t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Uitvoer[t] =  -331.141725919585 +  1.70620646635698TIP[t] +  2.72941331902169`index/cons`[t] +  3.85690559007307M1[t] +  5.18062213837092M2[t] +  7.79403001839284M3[t] +  5.82862661097082M4[t] +  3.69147814714131M5[t] +  4.56365482328584M6[t] +  6.54365000251388M7[t] +  1.06346039581491M8[t] +  32.5649038799904M9[t] +  3.02441373941594M10[t] -1.30786415664453M11[t] -0.303331431381687t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Uitvoer[t] =  -331.141725919585 +  1.70620646635698TIP[t] +  2.72941331902169`index/cons`[t] +  3.85690559007307M1[t] +  5.18062213837092M2[t] +  7.79403001839284M3[t] +  5.82862661097082M4[t] +  3.69147814714131M5[t] +  4.56365482328584M6[t] +  6.54365000251388M7[t] +  1.06346039581491M8[t] +  32.5649038799904M9[t] +  3.02441373941594M10[t] -1.30786415664453M11[t] -0.303331431381687t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202987&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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
Uitvoer[t] = -331.141725919585 + 1.70620646635698TIP[t] + 2.72941331902169`index/cons`[t] + 3.85690559007307M1[t] + 5.18062213837092M2[t] + 7.79403001839284M3[t] + 5.82862661097082M4[t] + 3.69147814714131M5[t] + 4.56365482328584M6[t] + 6.54365000251388M7[t] + 1.06346039581491M8[t] + 32.5649038799904M9[t] + 3.02441373941594M10[t] -1.30786415664453M11[t] -0.303331431381687t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-331.14172591958563.565908-5.20943e-061e-06
TIP1.706206466356980.10640216.035500
`index/cons`2.729413319021690.6504764.1961e-045e-05
M13.856905590073073.1315851.23160.2233330.111667
M25.180622138370923.3912121.52770.1323280.066164
M37.794030018392843.3954922.29540.0255480.012774
M45.828626610970823.3448861.74250.0870020.043501
M53.691478147141313.0396821.21440.2297720.114886
M64.563654823285843.2081121.42250.1605180.080259
M76.543650002513883.260362.0070.0496710.024836
M81.063460395814913.0183280.35230.7259340.362967
M932.56490387999044.1460767.854400
M103.024413739415943.4925580.8660.3902740.195137
M11-1.307864156644533.147305-0.41560.6793560.339678
t-0.3033314313816870.153034-1.98210.0524710.026236

\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) & -331.141725919585 & 63.565908 & -5.2094 & 3e-06 & 1e-06 \tabularnewline
TIP & 1.70620646635698 & 0.106402 & 16.0355 & 0 & 0 \tabularnewline
`index/cons` & 2.72941331902169 & 0.650476 & 4.196 & 1e-04 & 5e-05 \tabularnewline
M1 & 3.85690559007307 & 3.131585 & 1.2316 & 0.223333 & 0.111667 \tabularnewline
M2 & 5.18062213837092 & 3.391212 & 1.5277 & 0.132328 & 0.066164 \tabularnewline
M3 & 7.79403001839284 & 3.395492 & 2.2954 & 0.025548 & 0.012774 \tabularnewline
M4 & 5.82862661097082 & 3.344886 & 1.7425 & 0.087002 & 0.043501 \tabularnewline
M5 & 3.69147814714131 & 3.039682 & 1.2144 & 0.229772 & 0.114886 \tabularnewline
M6 & 4.56365482328584 & 3.208112 & 1.4225 & 0.160518 & 0.080259 \tabularnewline
M7 & 6.54365000251388 & 3.26036 & 2.007 & 0.049671 & 0.024836 \tabularnewline
M8 & 1.06346039581491 & 3.018328 & 0.3523 & 0.725934 & 0.362967 \tabularnewline
M9 & 32.5649038799904 & 4.146076 & 7.8544 & 0 & 0 \tabularnewline
M10 & 3.02441373941594 & 3.492558 & 0.866 & 0.390274 & 0.195137 \tabularnewline
M11 & -1.30786415664453 & 3.147305 & -0.4156 & 0.679356 & 0.339678 \tabularnewline
t & -0.303331431381687 & 0.153034 & -1.9821 & 0.052471 & 0.026236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&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]-331.141725919585[/C][C]63.565908[/C][C]-5.2094[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]TIP[/C][C]1.70620646635698[/C][C]0.106402[/C][C]16.0355[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`index/cons`[/C][C]2.72941331902169[/C][C]0.650476[/C][C]4.196[/C][C]1e-04[/C][C]5e-05[/C][/ROW]
[ROW][C]M1[/C][C]3.85690559007307[/C][C]3.131585[/C][C]1.2316[/C][C]0.223333[/C][C]0.111667[/C][/ROW]
[ROW][C]M2[/C][C]5.18062213837092[/C][C]3.391212[/C][C]1.5277[/C][C]0.132328[/C][C]0.066164[/C][/ROW]
[ROW][C]M3[/C][C]7.79403001839284[/C][C]3.395492[/C][C]2.2954[/C][C]0.025548[/C][C]0.012774[/C][/ROW]
[ROW][C]M4[/C][C]5.82862661097082[/C][C]3.344886[/C][C]1.7425[/C][C]0.087002[/C][C]0.043501[/C][/ROW]
[ROW][C]M5[/C][C]3.69147814714131[/C][C]3.039682[/C][C]1.2144[/C][C]0.229772[/C][C]0.114886[/C][/ROW]
[ROW][C]M6[/C][C]4.56365482328584[/C][C]3.208112[/C][C]1.4225[/C][C]0.160518[/C][C]0.080259[/C][/ROW]
[ROW][C]M7[/C][C]6.54365000251388[/C][C]3.26036[/C][C]2.007[/C][C]0.049671[/C][C]0.024836[/C][/ROW]
[ROW][C]M8[/C][C]1.06346039581491[/C][C]3.018328[/C][C]0.3523[/C][C]0.725934[/C][C]0.362967[/C][/ROW]
[ROW][C]M9[/C][C]32.5649038799904[/C][C]4.146076[/C][C]7.8544[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]3.02441373941594[/C][C]3.492558[/C][C]0.866[/C][C]0.390274[/C][C]0.195137[/C][/ROW]
[ROW][C]M11[/C][C]-1.30786415664453[/C][C]3.147305[/C][C]-0.4156[/C][C]0.679356[/C][C]0.339678[/C][/ROW]
[ROW][C]t[/C][C]-0.303331431381687[/C][C]0.153034[/C][C]-1.9821[/C][C]0.052471[/C][C]0.026236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202987&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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)-331.14172591958563.565908-5.20943e-061e-06
TIP1.706206466356980.10640216.035500
`index/cons`2.729413319021690.6504764.1961e-045e-05
M13.856905590073073.1315851.23160.2233330.111667
M25.180622138370923.3912121.52770.1323280.066164
M37.794030018392843.3954922.29540.0255480.012774
M45.828626610970823.3448861.74250.0870020.043501
M53.691478147141313.0396821.21440.2297720.114886
M64.563654823285843.2081121.42250.1605180.080259
M76.543650002513883.260362.0070.0496710.024836
M81.063460395814913.0183280.35230.7259340.362967
M932.56490387999044.1460767.854400
M103.024413739415943.4925580.8660.3902740.195137
M11-1.307864156644533.147305-0.41560.6793560.339678
t-0.3033314313816870.153034-1.98210.0524710.026236







Multiple Linear Regression - Regression Statistics
Multiple R0.957608253516359
R-squared0.917013567202651
Adjusted R-squared0.895889747945144
F-TEST (value)43.4113526547414
F-TEST (DF numerator)14
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.97581886189116
Sum Squared Residuals1361.73253404935

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.957608253516359 \tabularnewline
R-squared & 0.917013567202651 \tabularnewline
Adjusted R-squared & 0.895889747945144 \tabularnewline
F-TEST (value) & 43.4113526547414 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.97581886189116 \tabularnewline
Sum Squared Residuals & 1361.73253404935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.957608253516359[/C][/ROW]
[ROW][C]R-squared[/C][C]0.917013567202651[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.895889747945144[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]43.4113526547414[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/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]4.97581886189116[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1361.73253404935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202987&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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 R0.957608253516359
R-squared0.917013567202651
Adjusted R-squared0.895889747945144
F-TEST (value)43.4113526547414
F-TEST (DF numerator)14
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.97581886189116
Sum Squared Residuals1361.73253404935







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.34103.568190843265-0.228190843264547
2102.6101.6880249673740.9119750326261
3100.6997.43328072822373.25671927177625
4105.67103.2919880165472.37801198345283
5123.61127.857302708662-4.24730270866232
6113.08113.124619509018-0.0446195090180228
7106.46106.3451482203680.114851779631629
8123.38124.202870512573-0.822870512573485
9109.87111.668025787398-1.79802578739765
1095.74102.284194576012-6.54419457601223
11123.06126.920309917834-3.86030991783389
12123.39122.8332588636410.55674113635875
13120.28115.0031113734145.27688862658569
14115.33111.1712210892424.15877891075798
15110.4108.634326902841.76567309716024
16114.49110.0302495293824.45975047061834
17132.03124.1055638635747.92443613642618
18123.16121.3507344226881.80926557731227
19118.82115.526816309273.29318369072957
20128.32137.192651837054-8.87265183705398
21112.24113.205009960866-0.965009960866323
22104.53108.667632357374-4.1376323573739
23132.57133.590400726087-1.02040072608655
24122.52121.675625982710.844374017290262
25131.8131.0233170227330.77668297726693
26124.55121.1717131738513.3782868261494
27120.96118.0150730826542.94492691734593
28122.6119.9843017629782.61569823702211
29145.52142.5773245043282.94267549567173
30118.57118.904196975046-0.334196975045805
31134.25137.233254875034-2.98325487503401
32136.7139.414315962451-2.71431596245073
33121.37121.664611459708-0.294611459707655
34111.63117.939331261215-6.30933126121468
35134.42137.647886136297-3.22788613629694
36137.65141.443793321606-3.79379332160576
37137.86139.564671664358-1.70467166435766
38119.77122.260347629885-2.49034762988493
39130.69132.050309140518-1.36030914051828
40128.28130.061112967536-1.78111296753637
41147.45150.026655304769-2.57665530476948
42128.42129.881973088456-1.46197308845622
43136.9136.363438332080.536561667920284
44143.95143.8951835790780.0548164209220742
45135.64134.0963495068431.54365049315655
46122.48124.133261191693-1.65326119169327
47136.83133.7683420677753.06165793222511
48153.04152.9744372031020.0655627968976921
49142.71143.021298374021-0.311298374021209
50123.46122.7411382810910.718861718909419
51144.37144.426683380549-0.0566833805493945
52146.15148.088277575454-1.938277575454
53147.61142.1941204057755.41587959422492
54158.51156.0235144124492.486485587551
55147.4145.6471685926741.75283140732649
56165.05152.61220511980912.4377948801906
57154.64151.4944564835123.14554351648761
58126.2127.206218955245-1.00621895524531
59157.36152.3130611520085.04693884799228
60154.15151.8228846289412.32711537105906
61123.21127.019410722209-3.8094107222092
62113.07119.747554858558-6.67755485855797
63110.45117.000326765215-6.55032676521474
64113.57119.304070148103-5.73407014810291
65122.44131.899033212891-9.45903321289103
66114.93117.384961592343-2.45496159234323
67111.85114.564173670574-2.71417367057397
68126.04126.122772989034-0.0827729890344318
69121.34122.971546801673-1.63154680167253
70124.36104.70936165846119.6506383415394

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.34 & 103.568190843265 & -0.228190843264547 \tabularnewline
2 & 102.6 & 101.688024967374 & 0.9119750326261 \tabularnewline
3 & 100.69 & 97.4332807282237 & 3.25671927177625 \tabularnewline
4 & 105.67 & 103.291988016547 & 2.37801198345283 \tabularnewline
5 & 123.61 & 127.857302708662 & -4.24730270866232 \tabularnewline
6 & 113.08 & 113.124619509018 & -0.0446195090180228 \tabularnewline
7 & 106.46 & 106.345148220368 & 0.114851779631629 \tabularnewline
8 & 123.38 & 124.202870512573 & -0.822870512573485 \tabularnewline
9 & 109.87 & 111.668025787398 & -1.79802578739765 \tabularnewline
10 & 95.74 & 102.284194576012 & -6.54419457601223 \tabularnewline
11 & 123.06 & 126.920309917834 & -3.86030991783389 \tabularnewline
12 & 123.39 & 122.833258863641 & 0.55674113635875 \tabularnewline
13 & 120.28 & 115.003111373414 & 5.27688862658569 \tabularnewline
14 & 115.33 & 111.171221089242 & 4.15877891075798 \tabularnewline
15 & 110.4 & 108.63432690284 & 1.76567309716024 \tabularnewline
16 & 114.49 & 110.030249529382 & 4.45975047061834 \tabularnewline
17 & 132.03 & 124.105563863574 & 7.92443613642618 \tabularnewline
18 & 123.16 & 121.350734422688 & 1.80926557731227 \tabularnewline
19 & 118.82 & 115.52681630927 & 3.29318369072957 \tabularnewline
20 & 128.32 & 137.192651837054 & -8.87265183705398 \tabularnewline
21 & 112.24 & 113.205009960866 & -0.965009960866323 \tabularnewline
22 & 104.53 & 108.667632357374 & -4.1376323573739 \tabularnewline
23 & 132.57 & 133.590400726087 & -1.02040072608655 \tabularnewline
24 & 122.52 & 121.67562598271 & 0.844374017290262 \tabularnewline
25 & 131.8 & 131.023317022733 & 0.77668297726693 \tabularnewline
26 & 124.55 & 121.171713173851 & 3.3782868261494 \tabularnewline
27 & 120.96 & 118.015073082654 & 2.94492691734593 \tabularnewline
28 & 122.6 & 119.984301762978 & 2.61569823702211 \tabularnewline
29 & 145.52 & 142.577324504328 & 2.94267549567173 \tabularnewline
30 & 118.57 & 118.904196975046 & -0.334196975045805 \tabularnewline
31 & 134.25 & 137.233254875034 & -2.98325487503401 \tabularnewline
32 & 136.7 & 139.414315962451 & -2.71431596245073 \tabularnewline
33 & 121.37 & 121.664611459708 & -0.294611459707655 \tabularnewline
34 & 111.63 & 117.939331261215 & -6.30933126121468 \tabularnewline
35 & 134.42 & 137.647886136297 & -3.22788613629694 \tabularnewline
36 & 137.65 & 141.443793321606 & -3.79379332160576 \tabularnewline
37 & 137.86 & 139.564671664358 & -1.70467166435766 \tabularnewline
38 & 119.77 & 122.260347629885 & -2.49034762988493 \tabularnewline
39 & 130.69 & 132.050309140518 & -1.36030914051828 \tabularnewline
40 & 128.28 & 130.061112967536 & -1.78111296753637 \tabularnewline
41 & 147.45 & 150.026655304769 & -2.57665530476948 \tabularnewline
42 & 128.42 & 129.881973088456 & -1.46197308845622 \tabularnewline
43 & 136.9 & 136.36343833208 & 0.536561667920284 \tabularnewline
44 & 143.95 & 143.895183579078 & 0.0548164209220742 \tabularnewline
45 & 135.64 & 134.096349506843 & 1.54365049315655 \tabularnewline
46 & 122.48 & 124.133261191693 & -1.65326119169327 \tabularnewline
47 & 136.83 & 133.768342067775 & 3.06165793222511 \tabularnewline
48 & 153.04 & 152.974437203102 & 0.0655627968976921 \tabularnewline
49 & 142.71 & 143.021298374021 & -0.311298374021209 \tabularnewline
50 & 123.46 & 122.741138281091 & 0.718861718909419 \tabularnewline
51 & 144.37 & 144.426683380549 & -0.0566833805493945 \tabularnewline
52 & 146.15 & 148.088277575454 & -1.938277575454 \tabularnewline
53 & 147.61 & 142.194120405775 & 5.41587959422492 \tabularnewline
54 & 158.51 & 156.023514412449 & 2.486485587551 \tabularnewline
55 & 147.4 & 145.647168592674 & 1.75283140732649 \tabularnewline
56 & 165.05 & 152.612205119809 & 12.4377948801906 \tabularnewline
57 & 154.64 & 151.494456483512 & 3.14554351648761 \tabularnewline
58 & 126.2 & 127.206218955245 & -1.00621895524531 \tabularnewline
59 & 157.36 & 152.313061152008 & 5.04693884799228 \tabularnewline
60 & 154.15 & 151.822884628941 & 2.32711537105906 \tabularnewline
61 & 123.21 & 127.019410722209 & -3.8094107222092 \tabularnewline
62 & 113.07 & 119.747554858558 & -6.67755485855797 \tabularnewline
63 & 110.45 & 117.000326765215 & -6.55032676521474 \tabularnewline
64 & 113.57 & 119.304070148103 & -5.73407014810291 \tabularnewline
65 & 122.44 & 131.899033212891 & -9.45903321289103 \tabularnewline
66 & 114.93 & 117.384961592343 & -2.45496159234323 \tabularnewline
67 & 111.85 & 114.564173670574 & -2.71417367057397 \tabularnewline
68 & 126.04 & 126.122772989034 & -0.0827729890344318 \tabularnewline
69 & 121.34 & 122.971546801673 & -1.63154680167253 \tabularnewline
70 & 124.36 & 104.709361658461 & 19.6506383415394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&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]103.34[/C][C]103.568190843265[/C][C]-0.228190843264547[/C][/ROW]
[ROW][C]2[/C][C]102.6[/C][C]101.688024967374[/C][C]0.9119750326261[/C][/ROW]
[ROW][C]3[/C][C]100.69[/C][C]97.4332807282237[/C][C]3.25671927177625[/C][/ROW]
[ROW][C]4[/C][C]105.67[/C][C]103.291988016547[/C][C]2.37801198345283[/C][/ROW]
[ROW][C]5[/C][C]123.61[/C][C]127.857302708662[/C][C]-4.24730270866232[/C][/ROW]
[ROW][C]6[/C][C]113.08[/C][C]113.124619509018[/C][C]-0.0446195090180228[/C][/ROW]
[ROW][C]7[/C][C]106.46[/C][C]106.345148220368[/C][C]0.114851779631629[/C][/ROW]
[ROW][C]8[/C][C]123.38[/C][C]124.202870512573[/C][C]-0.822870512573485[/C][/ROW]
[ROW][C]9[/C][C]109.87[/C][C]111.668025787398[/C][C]-1.79802578739765[/C][/ROW]
[ROW][C]10[/C][C]95.74[/C][C]102.284194576012[/C][C]-6.54419457601223[/C][/ROW]
[ROW][C]11[/C][C]123.06[/C][C]126.920309917834[/C][C]-3.86030991783389[/C][/ROW]
[ROW][C]12[/C][C]123.39[/C][C]122.833258863641[/C][C]0.55674113635875[/C][/ROW]
[ROW][C]13[/C][C]120.28[/C][C]115.003111373414[/C][C]5.27688862658569[/C][/ROW]
[ROW][C]14[/C][C]115.33[/C][C]111.171221089242[/C][C]4.15877891075798[/C][/ROW]
[ROW][C]15[/C][C]110.4[/C][C]108.63432690284[/C][C]1.76567309716024[/C][/ROW]
[ROW][C]16[/C][C]114.49[/C][C]110.030249529382[/C][C]4.45975047061834[/C][/ROW]
[ROW][C]17[/C][C]132.03[/C][C]124.105563863574[/C][C]7.92443613642618[/C][/ROW]
[ROW][C]18[/C][C]123.16[/C][C]121.350734422688[/C][C]1.80926557731227[/C][/ROW]
[ROW][C]19[/C][C]118.82[/C][C]115.52681630927[/C][C]3.29318369072957[/C][/ROW]
[ROW][C]20[/C][C]128.32[/C][C]137.192651837054[/C][C]-8.87265183705398[/C][/ROW]
[ROW][C]21[/C][C]112.24[/C][C]113.205009960866[/C][C]-0.965009960866323[/C][/ROW]
[ROW][C]22[/C][C]104.53[/C][C]108.667632357374[/C][C]-4.1376323573739[/C][/ROW]
[ROW][C]23[/C][C]132.57[/C][C]133.590400726087[/C][C]-1.02040072608655[/C][/ROW]
[ROW][C]24[/C][C]122.52[/C][C]121.67562598271[/C][C]0.844374017290262[/C][/ROW]
[ROW][C]25[/C][C]131.8[/C][C]131.023317022733[/C][C]0.77668297726693[/C][/ROW]
[ROW][C]26[/C][C]124.55[/C][C]121.171713173851[/C][C]3.3782868261494[/C][/ROW]
[ROW][C]27[/C][C]120.96[/C][C]118.015073082654[/C][C]2.94492691734593[/C][/ROW]
[ROW][C]28[/C][C]122.6[/C][C]119.984301762978[/C][C]2.61569823702211[/C][/ROW]
[ROW][C]29[/C][C]145.52[/C][C]142.577324504328[/C][C]2.94267549567173[/C][/ROW]
[ROW][C]30[/C][C]118.57[/C][C]118.904196975046[/C][C]-0.334196975045805[/C][/ROW]
[ROW][C]31[/C][C]134.25[/C][C]137.233254875034[/C][C]-2.98325487503401[/C][/ROW]
[ROW][C]32[/C][C]136.7[/C][C]139.414315962451[/C][C]-2.71431596245073[/C][/ROW]
[ROW][C]33[/C][C]121.37[/C][C]121.664611459708[/C][C]-0.294611459707655[/C][/ROW]
[ROW][C]34[/C][C]111.63[/C][C]117.939331261215[/C][C]-6.30933126121468[/C][/ROW]
[ROW][C]35[/C][C]134.42[/C][C]137.647886136297[/C][C]-3.22788613629694[/C][/ROW]
[ROW][C]36[/C][C]137.65[/C][C]141.443793321606[/C][C]-3.79379332160576[/C][/ROW]
[ROW][C]37[/C][C]137.86[/C][C]139.564671664358[/C][C]-1.70467166435766[/C][/ROW]
[ROW][C]38[/C][C]119.77[/C][C]122.260347629885[/C][C]-2.49034762988493[/C][/ROW]
[ROW][C]39[/C][C]130.69[/C][C]132.050309140518[/C][C]-1.36030914051828[/C][/ROW]
[ROW][C]40[/C][C]128.28[/C][C]130.061112967536[/C][C]-1.78111296753637[/C][/ROW]
[ROW][C]41[/C][C]147.45[/C][C]150.026655304769[/C][C]-2.57665530476948[/C][/ROW]
[ROW][C]42[/C][C]128.42[/C][C]129.881973088456[/C][C]-1.46197308845622[/C][/ROW]
[ROW][C]43[/C][C]136.9[/C][C]136.36343833208[/C][C]0.536561667920284[/C][/ROW]
[ROW][C]44[/C][C]143.95[/C][C]143.895183579078[/C][C]0.0548164209220742[/C][/ROW]
[ROW][C]45[/C][C]135.64[/C][C]134.096349506843[/C][C]1.54365049315655[/C][/ROW]
[ROW][C]46[/C][C]122.48[/C][C]124.133261191693[/C][C]-1.65326119169327[/C][/ROW]
[ROW][C]47[/C][C]136.83[/C][C]133.768342067775[/C][C]3.06165793222511[/C][/ROW]
[ROW][C]48[/C][C]153.04[/C][C]152.974437203102[/C][C]0.0655627968976921[/C][/ROW]
[ROW][C]49[/C][C]142.71[/C][C]143.021298374021[/C][C]-0.311298374021209[/C][/ROW]
[ROW][C]50[/C][C]123.46[/C][C]122.741138281091[/C][C]0.718861718909419[/C][/ROW]
[ROW][C]51[/C][C]144.37[/C][C]144.426683380549[/C][C]-0.0566833805493945[/C][/ROW]
[ROW][C]52[/C][C]146.15[/C][C]148.088277575454[/C][C]-1.938277575454[/C][/ROW]
[ROW][C]53[/C][C]147.61[/C][C]142.194120405775[/C][C]5.41587959422492[/C][/ROW]
[ROW][C]54[/C][C]158.51[/C][C]156.023514412449[/C][C]2.486485587551[/C][/ROW]
[ROW][C]55[/C][C]147.4[/C][C]145.647168592674[/C][C]1.75283140732649[/C][/ROW]
[ROW][C]56[/C][C]165.05[/C][C]152.612205119809[/C][C]12.4377948801906[/C][/ROW]
[ROW][C]57[/C][C]154.64[/C][C]151.494456483512[/C][C]3.14554351648761[/C][/ROW]
[ROW][C]58[/C][C]126.2[/C][C]127.206218955245[/C][C]-1.00621895524531[/C][/ROW]
[ROW][C]59[/C][C]157.36[/C][C]152.313061152008[/C][C]5.04693884799228[/C][/ROW]
[ROW][C]60[/C][C]154.15[/C][C]151.822884628941[/C][C]2.32711537105906[/C][/ROW]
[ROW][C]61[/C][C]123.21[/C][C]127.019410722209[/C][C]-3.8094107222092[/C][/ROW]
[ROW][C]62[/C][C]113.07[/C][C]119.747554858558[/C][C]-6.67755485855797[/C][/ROW]
[ROW][C]63[/C][C]110.45[/C][C]117.000326765215[/C][C]-6.55032676521474[/C][/ROW]
[ROW][C]64[/C][C]113.57[/C][C]119.304070148103[/C][C]-5.73407014810291[/C][/ROW]
[ROW][C]65[/C][C]122.44[/C][C]131.899033212891[/C][C]-9.45903321289103[/C][/ROW]
[ROW][C]66[/C][C]114.93[/C][C]117.384961592343[/C][C]-2.45496159234323[/C][/ROW]
[ROW][C]67[/C][C]111.85[/C][C]114.564173670574[/C][C]-2.71417367057397[/C][/ROW]
[ROW][C]68[/C][C]126.04[/C][C]126.122772989034[/C][C]-0.0827729890344318[/C][/ROW]
[ROW][C]69[/C][C]121.34[/C][C]122.971546801673[/C][C]-1.63154680167253[/C][/ROW]
[ROW][C]70[/C][C]124.36[/C][C]104.709361658461[/C][C]19.6506383415394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202987&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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
1103.34103.568190843265-0.228190843264547
2102.6101.6880249673740.9119750326261
3100.6997.43328072822373.25671927177625
4105.67103.2919880165472.37801198345283
5123.61127.857302708662-4.24730270866232
6113.08113.124619509018-0.0446195090180228
7106.46106.3451482203680.114851779631629
8123.38124.202870512573-0.822870512573485
9109.87111.668025787398-1.79802578739765
1095.74102.284194576012-6.54419457601223
11123.06126.920309917834-3.86030991783389
12123.39122.8332588636410.55674113635875
13120.28115.0031113734145.27688862658569
14115.33111.1712210892424.15877891075798
15110.4108.634326902841.76567309716024
16114.49110.0302495293824.45975047061834
17132.03124.1055638635747.92443613642618
18123.16121.3507344226881.80926557731227
19118.82115.526816309273.29318369072957
20128.32137.192651837054-8.87265183705398
21112.24113.205009960866-0.965009960866323
22104.53108.667632357374-4.1376323573739
23132.57133.590400726087-1.02040072608655
24122.52121.675625982710.844374017290262
25131.8131.0233170227330.77668297726693
26124.55121.1717131738513.3782868261494
27120.96118.0150730826542.94492691734593
28122.6119.9843017629782.61569823702211
29145.52142.5773245043282.94267549567173
30118.57118.904196975046-0.334196975045805
31134.25137.233254875034-2.98325487503401
32136.7139.414315962451-2.71431596245073
33121.37121.664611459708-0.294611459707655
34111.63117.939331261215-6.30933126121468
35134.42137.647886136297-3.22788613629694
36137.65141.443793321606-3.79379332160576
37137.86139.564671664358-1.70467166435766
38119.77122.260347629885-2.49034762988493
39130.69132.050309140518-1.36030914051828
40128.28130.061112967536-1.78111296753637
41147.45150.026655304769-2.57665530476948
42128.42129.881973088456-1.46197308845622
43136.9136.363438332080.536561667920284
44143.95143.8951835790780.0548164209220742
45135.64134.0963495068431.54365049315655
46122.48124.133261191693-1.65326119169327
47136.83133.7683420677753.06165793222511
48153.04152.9744372031020.0655627968976921
49142.71143.021298374021-0.311298374021209
50123.46122.7411382810910.718861718909419
51144.37144.426683380549-0.0566833805493945
52146.15148.088277575454-1.938277575454
53147.61142.1941204057755.41587959422492
54158.51156.0235144124492.486485587551
55147.4145.6471685926741.75283140732649
56165.05152.61220511980912.4377948801906
57154.64151.4944564835123.14554351648761
58126.2127.206218955245-1.00621895524531
59157.36152.3130611520085.04693884799228
60154.15151.8228846289412.32711537105906
61123.21127.019410722209-3.8094107222092
62113.07119.747554858558-6.67755485855797
63110.45117.000326765215-6.55032676521474
64113.57119.304070148103-5.73407014810291
65122.44131.899033212891-9.45903321289103
66114.93117.384961592343-2.45496159234323
67111.85114.564173670574-2.71417367057397
68126.04126.122772989034-0.0827729890344318
69121.34122.971546801673-1.63154680167253
70124.36104.70936165846119.6506383415394







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.02736296706898870.05472593413797750.972637032931011
190.00784315659593150.0156863131918630.992156843404069
200.0287670229057580.05753404581151590.971232977094242
210.01982510954846060.03965021909692130.980174890451539
220.007962018565097910.01592403713019580.992037981434902
230.002781986162068690.005563972324137380.997218013837931
240.02051620845411850.04103241690823710.979483791545881
250.009692685807965580.01938537161593120.990307314192034
260.005062094440382860.01012418888076570.994937905559617
270.003093621080503530.006187242161007060.996906378919497
280.003614487523201830.007228975046403670.996385512476798
290.002115721560577530.004231443121155060.997884278439423
300.007855268917516690.01571053783503340.992144731082483
310.004097044560868490.008194089121736970.995902955439132
320.002010226459327640.004020452918655290.997989773540672
330.001167204412687780.002334408825375560.998832795587312
340.0008979913720710440.001795982744142090.999102008627929
350.0006041230849273160.001208246169854630.999395876915073
360.0003484115091730570.0006968230183461150.999651588490827
370.0001620908380772840.0003241816761545690.999837909161923
380.0003241404816878290.0006482809633756580.999675859518312
390.0001508809233972560.0003017618467945120.999849119076603
409.45300911956197e-050.0001890601823912390.999905469908804
414.29784748029892e-058.59569496059784e-050.999957021525197
421.68367976950445e-053.3673595390089e-050.999983163202305
439.1682156103426e-061.83364312206852e-050.99999083178439
449.71114826857847e-061.94222965371569e-050.999990288851731
457.81581813158694e-061.56316362631739e-050.999992184181868
463.50518976565034e-057.01037953130068e-050.999964948102343
471.48857957188877e-052.97715914377754e-050.999985114204281
481.86160176362099e-053.72320352724199e-050.999981383982364
497.97374001049115e-061.59474800209823e-050.99999202625999
503.96208025974044e-067.92416051948087e-060.99999603791974
511.18445015597285e-062.3689003119457e-060.999998815549844
525.85313431602034e-071.17062686320407e-060.999999414686568

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.0273629670689887 & 0.0547259341379775 & 0.972637032931011 \tabularnewline
19 & 0.0078431565959315 & 0.015686313191863 & 0.992156843404069 \tabularnewline
20 & 0.028767022905758 & 0.0575340458115159 & 0.971232977094242 \tabularnewline
21 & 0.0198251095484606 & 0.0396502190969213 & 0.980174890451539 \tabularnewline
22 & 0.00796201856509791 & 0.0159240371301958 & 0.992037981434902 \tabularnewline
23 & 0.00278198616206869 & 0.00556397232413738 & 0.997218013837931 \tabularnewline
24 & 0.0205162084541185 & 0.0410324169082371 & 0.979483791545881 \tabularnewline
25 & 0.00969268580796558 & 0.0193853716159312 & 0.990307314192034 \tabularnewline
26 & 0.00506209444038286 & 0.0101241888807657 & 0.994937905559617 \tabularnewline
27 & 0.00309362108050353 & 0.00618724216100706 & 0.996906378919497 \tabularnewline
28 & 0.00361448752320183 & 0.00722897504640367 & 0.996385512476798 \tabularnewline
29 & 0.00211572156057753 & 0.00423144312115506 & 0.997884278439423 \tabularnewline
30 & 0.00785526891751669 & 0.0157105378350334 & 0.992144731082483 \tabularnewline
31 & 0.00409704456086849 & 0.00819408912173697 & 0.995902955439132 \tabularnewline
32 & 0.00201022645932764 & 0.00402045291865529 & 0.997989773540672 \tabularnewline
33 & 0.00116720441268778 & 0.00233440882537556 & 0.998832795587312 \tabularnewline
34 & 0.000897991372071044 & 0.00179598274414209 & 0.999102008627929 \tabularnewline
35 & 0.000604123084927316 & 0.00120824616985463 & 0.999395876915073 \tabularnewline
36 & 0.000348411509173057 & 0.000696823018346115 & 0.999651588490827 \tabularnewline
37 & 0.000162090838077284 & 0.000324181676154569 & 0.999837909161923 \tabularnewline
38 & 0.000324140481687829 & 0.000648280963375658 & 0.999675859518312 \tabularnewline
39 & 0.000150880923397256 & 0.000301761846794512 & 0.999849119076603 \tabularnewline
40 & 9.45300911956197e-05 & 0.000189060182391239 & 0.999905469908804 \tabularnewline
41 & 4.29784748029892e-05 & 8.59569496059784e-05 & 0.999957021525197 \tabularnewline
42 & 1.68367976950445e-05 & 3.3673595390089e-05 & 0.999983163202305 \tabularnewline
43 & 9.1682156103426e-06 & 1.83364312206852e-05 & 0.99999083178439 \tabularnewline
44 & 9.71114826857847e-06 & 1.94222965371569e-05 & 0.999990288851731 \tabularnewline
45 & 7.81581813158694e-06 & 1.56316362631739e-05 & 0.999992184181868 \tabularnewline
46 & 3.50518976565034e-05 & 7.01037953130068e-05 & 0.999964948102343 \tabularnewline
47 & 1.48857957188877e-05 & 2.97715914377754e-05 & 0.999985114204281 \tabularnewline
48 & 1.86160176362099e-05 & 3.72320352724199e-05 & 0.999981383982364 \tabularnewline
49 & 7.97374001049115e-06 & 1.59474800209823e-05 & 0.99999202625999 \tabularnewline
50 & 3.96208025974044e-06 & 7.92416051948087e-06 & 0.99999603791974 \tabularnewline
51 & 1.18445015597285e-06 & 2.3689003119457e-06 & 0.999998815549844 \tabularnewline
52 & 5.85313431602034e-07 & 1.17062686320407e-06 & 0.999999414686568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]18[/C][C]0.0273629670689887[/C][C]0.0547259341379775[/C][C]0.972637032931011[/C][/ROW]
[ROW][C]19[/C][C]0.0078431565959315[/C][C]0.015686313191863[/C][C]0.992156843404069[/C][/ROW]
[ROW][C]20[/C][C]0.028767022905758[/C][C]0.0575340458115159[/C][C]0.971232977094242[/C][/ROW]
[ROW][C]21[/C][C]0.0198251095484606[/C][C]0.0396502190969213[/C][C]0.980174890451539[/C][/ROW]
[ROW][C]22[/C][C]0.00796201856509791[/C][C]0.0159240371301958[/C][C]0.992037981434902[/C][/ROW]
[ROW][C]23[/C][C]0.00278198616206869[/C][C]0.00556397232413738[/C][C]0.997218013837931[/C][/ROW]
[ROW][C]24[/C][C]0.0205162084541185[/C][C]0.0410324169082371[/C][C]0.979483791545881[/C][/ROW]
[ROW][C]25[/C][C]0.00969268580796558[/C][C]0.0193853716159312[/C][C]0.990307314192034[/C][/ROW]
[ROW][C]26[/C][C]0.00506209444038286[/C][C]0.0101241888807657[/C][C]0.994937905559617[/C][/ROW]
[ROW][C]27[/C][C]0.00309362108050353[/C][C]0.00618724216100706[/C][C]0.996906378919497[/C][/ROW]
[ROW][C]28[/C][C]0.00361448752320183[/C][C]0.00722897504640367[/C][C]0.996385512476798[/C][/ROW]
[ROW][C]29[/C][C]0.00211572156057753[/C][C]0.00423144312115506[/C][C]0.997884278439423[/C][/ROW]
[ROW][C]30[/C][C]0.00785526891751669[/C][C]0.0157105378350334[/C][C]0.992144731082483[/C][/ROW]
[ROW][C]31[/C][C]0.00409704456086849[/C][C]0.00819408912173697[/C][C]0.995902955439132[/C][/ROW]
[ROW][C]32[/C][C]0.00201022645932764[/C][C]0.00402045291865529[/C][C]0.997989773540672[/C][/ROW]
[ROW][C]33[/C][C]0.00116720441268778[/C][C]0.00233440882537556[/C][C]0.998832795587312[/C][/ROW]
[ROW][C]34[/C][C]0.000897991372071044[/C][C]0.00179598274414209[/C][C]0.999102008627929[/C][/ROW]
[ROW][C]35[/C][C]0.000604123084927316[/C][C]0.00120824616985463[/C][C]0.999395876915073[/C][/ROW]
[ROW][C]36[/C][C]0.000348411509173057[/C][C]0.000696823018346115[/C][C]0.999651588490827[/C][/ROW]
[ROW][C]37[/C][C]0.000162090838077284[/C][C]0.000324181676154569[/C][C]0.999837909161923[/C][/ROW]
[ROW][C]38[/C][C]0.000324140481687829[/C][C]0.000648280963375658[/C][C]0.999675859518312[/C][/ROW]
[ROW][C]39[/C][C]0.000150880923397256[/C][C]0.000301761846794512[/C][C]0.999849119076603[/C][/ROW]
[ROW][C]40[/C][C]9.45300911956197e-05[/C][C]0.000189060182391239[/C][C]0.999905469908804[/C][/ROW]
[ROW][C]41[/C][C]4.29784748029892e-05[/C][C]8.59569496059784e-05[/C][C]0.999957021525197[/C][/ROW]
[ROW][C]42[/C][C]1.68367976950445e-05[/C][C]3.3673595390089e-05[/C][C]0.999983163202305[/C][/ROW]
[ROW][C]43[/C][C]9.1682156103426e-06[/C][C]1.83364312206852e-05[/C][C]0.99999083178439[/C][/ROW]
[ROW][C]44[/C][C]9.71114826857847e-06[/C][C]1.94222965371569e-05[/C][C]0.999990288851731[/C][/ROW]
[ROW][C]45[/C][C]7.81581813158694e-06[/C][C]1.56316362631739e-05[/C][C]0.999992184181868[/C][/ROW]
[ROW][C]46[/C][C]3.50518976565034e-05[/C][C]7.01037953130068e-05[/C][C]0.999964948102343[/C][/ROW]
[ROW][C]47[/C][C]1.48857957188877e-05[/C][C]2.97715914377754e-05[/C][C]0.999985114204281[/C][/ROW]
[ROW][C]48[/C][C]1.86160176362099e-05[/C][C]3.72320352724199e-05[/C][C]0.999981383982364[/C][/ROW]
[ROW][C]49[/C][C]7.97374001049115e-06[/C][C]1.59474800209823e-05[/C][C]0.99999202625999[/C][/ROW]
[ROW][C]50[/C][C]3.96208025974044e-06[/C][C]7.92416051948087e-06[/C][C]0.99999603791974[/C][/ROW]
[ROW][C]51[/C][C]1.18445015597285e-06[/C][C]2.3689003119457e-06[/C][C]0.999998815549844[/C][/ROW]
[ROW][C]52[/C][C]5.85313431602034e-07[/C][C]1.17062686320407e-06[/C][C]0.999999414686568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202987&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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
180.02736296706898870.05472593413797750.972637032931011
190.00784315659593150.0156863131918630.992156843404069
200.0287670229057580.05753404581151590.971232977094242
210.01982510954846060.03965021909692130.980174890451539
220.007962018565097910.01592403713019580.992037981434902
230.002781986162068690.005563972324137380.997218013837931
240.02051620845411850.04103241690823710.979483791545881
250.009692685807965580.01938537161593120.990307314192034
260.005062094440382860.01012418888076570.994937905559617
270.003093621080503530.006187242161007060.996906378919497
280.003614487523201830.007228975046403670.996385512476798
290.002115721560577530.004231443121155060.997884278439423
300.007855268917516690.01571053783503340.992144731082483
310.004097044560868490.008194089121736970.995902955439132
320.002010226459327640.004020452918655290.997989773540672
330.001167204412687780.002334408825375560.998832795587312
340.0008979913720710440.001795982744142090.999102008627929
350.0006041230849273160.001208246169854630.999395876915073
360.0003484115091730570.0006968230183461150.999651588490827
370.0001620908380772840.0003241816761545690.999837909161923
380.0003241404816878290.0006482809633756580.999675859518312
390.0001508809233972560.0003017618467945120.999849119076603
409.45300911956197e-050.0001890601823912390.999905469908804
414.29784748029892e-058.59569496059784e-050.999957021525197
421.68367976950445e-053.3673595390089e-050.999983163202305
439.1682156103426e-061.83364312206852e-050.99999083178439
449.71114826857847e-061.94222965371569e-050.999990288851731
457.81581813158694e-061.56316362631739e-050.999992184181868
463.50518976565034e-057.01037953130068e-050.999964948102343
471.48857957188877e-052.97715914377754e-050.999985114204281
481.86160176362099e-053.72320352724199e-050.999981383982364
497.97374001049115e-061.59474800209823e-050.99999202625999
503.96208025974044e-067.92416051948087e-060.99999603791974
511.18445015597285e-062.3689003119457e-060.999998815549844
525.85313431602034e-071.17062686320407e-060.999999414686568







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level260.742857142857143NOK
5% type I error level330.942857142857143NOK
10% type I error level351NOK

\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 & 26 & 0.742857142857143 & NOK \tabularnewline
5% type I error level & 33 & 0.942857142857143 & NOK \tabularnewline
10% type I error level & 35 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202987&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]26[/C][C]0.742857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.942857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202987&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202987&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 level260.742857142857143NOK
5% type I error level330.942857142857143NOK
10% type I error level351NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}