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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 17 Nov 2009 02:50:15 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/17/t1258451631r7nelll34v3xky4.htm/, Retrieved Fri, 05 Dec 2025 07:03:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57359, Retrieved Fri, 05 Dec 2025 07:03:22 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

450

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] [] [2009-11-17 09:50:15] [a4292616308a56e4faddaa97386e0403] [Current]

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Boxplots

108.8235294	111.7647059	105.8823529	100
111.7647059	108.8235294	111.7647059	105.8823529
117.6470588	111.7647059	108.8235294	111.7647059
111.7647059	117.6470588	111.7647059	108.8235294
120.5882353	111.7647059	117.6470588	111.7647059
102.9411765	120.5882353	111.7647059	117.6470588
114.7058824	102.9411765	120.5882353	111.7647059
114.7058824	114.7058824	102.9411765	120.5882353
117.6470588	114.7058824	114.7058824	102.9411765
111.7647059	117.6470588	114.7058824	114.7058824
97.05882353	111.7647059	117.6470588	114.7058824
94.11764706	97.05882353	111.7647059	117.6470588
82.35294118	94.11764706	97.05882353	111.7647059
82.35294118	82.35294118	94.11764706	97.05882353
85.29411765	82.35294118	82.35294118	94.11764706
85.29411765	85.29411765	82.35294118	82.35294118
73.52941176	85.29411765	85.29411765	82.35294118
61.76470588	73.52941176	85.29411765	85.29411765
32.35294118	61.76470588	73.52941176	85.29411765
20.58823529	32.35294118	61.76470588	73.52941176
50	20.58823529	32.35294118	61.76470588
70.58823529	50	20.58823529	32.35294118
76.47058824	70.58823529	50	20.58823529
79.41176471	76.47058824	70.58823529	50
73.52941176	79.41176471	76.47058824	70.58823529
76.47058824	73.52941176	79.41176471	76.47058824
73.52941176	76.47058824	73.52941176	79.41176471
70.58823529	73.52941176	76.47058824	73.52941176
64.70588235	70.58823529	73.52941176	76.47058824
64.70588235	64.70588235	70.58823529	73.52941176
64.70588235	64.70588235	64.70588235	70.58823529
61.76470588	64.70588235	64.70588235	64.70588235
50	61.76470588	64.70588235	64.70588235
47.05882353	50	61.76470588	64.70588235
35.29411765	47.05882353	50	61.76470588
20.58823529	35.29411765	47.05882353	50
41.17647059	20.58823529	35.29411765	47.05882353
47.05882353	41.17647059	20.58823529	35.29411765
44.11764706	47.05882353	41.17647059	20.58823529
35.29411765	44.11764706	47.05882353	41.17647059
41.17647059	35.29411765	44.11764706	47.05882353
58.82352941	41.17647059	35.29411765	44.11764706
29.41176471	58.82352941	41.17647059	35.29411765
55.88235294	29.41176471	58.82352941	41.17647059
55.88235294	55.88235294	29.41176471	58.82352941
64.70588235	55.88235294	55.88235294	29.41176471
70.58823529	64.70588235	55.88235294	55.88235294
64.70588235	70.58823529	64.70588235	55.88235294
61.76470588	64.70588235	70.58823529	64.70588235
55.88235294	61.76470588	64.70588235	70.58823529
73.52941176	55.88235294	61.76470588	64.70588235
61.76470588	73.52941176	55.88235294	61.76470588
67.64705882	61.76470588	73.52941176	55.88235294
67.64705882	67.64705882	61.76470588	73.52941176
55.88235294	67.64705882	67.64705882	61.76470588
52.94117647	55.88235294	67.64705882	67.64705882
55.88235294	52.94117647	55.88235294	67.64705882
55.88235294	55.88235294	52.94117647	55.88235294
64.70588235	55.88235294	55.88235294	52.94117647
67.64705882	64.70588235	55.88235294	55.88235294
58.82352941	67.64705882	64.70588235	55.88235294
41.17647059	58.82352941	67.64705882	64.70588235
41.17647059	41.17647059	58.82352941	67.64705882
41.17647059	41.17647059	41.17647059	58.82352941
44.11764706	41.17647059	41.17647059	41.17647059
32.35294118	44.11764706	41.17647059	41.17647059
50	32.35294118	44.11764706	41.17647059
47.05882353	50	32.35294118	44.11764706
58.82352941	47.05882353	50	32.35294118
70.58823529	58.82352941	47.05882353	50
67.64705882	70.58823529	58.82352941	47.05882353
58.82352941	67.64705882	70.58823529	58.82352941
61.76470588	58.82352941	67.64705882	70.58823529
55.88235294	61.76470588	58.82352941	67.64705882
67.64705882	55.88235294	61.76470588	58.82352941
67.64705882	67.64705882	55.88235294	61.76470588
67.64705882	67.64705882	67.64705882	55.88235294
67.64705882	67.64705882	67.64705882	67.64705882
79.41176471	67.64705882	67.64705882	67.64705882
76.47058824	79.41176471	67.64705882	67.64705882
50	76.47058824	79.41176471	67.64705882
70.58823529	50	76.47058824	79.41176471
76.47058824	70.58823529	50	76.47058824
70.58823529	76.47058824	70.58823529	50
79.41176471	70.58823529	76.47058824	70.58823529
79.41176471	79.41176471	70.58823529	76.47058824
76.47058824	79.41176471	79.41176471	70.58823529
70.58823529	76.47058824	79.41176471	79.41176471
67.64705882	70.58823529	76.47058824	79.41176471
67.64705882	67.64705882	70.58823529	76.47058824
70.58823529	67.64705882	67.64705882	70.58823529
50	70.58823529	67.64705882	67.64705882
61.76470588	50	70.58823529	67.64705882
55.88235294	61.76470588	50	70.58823529
64.70588235	55.88235294	61.76470588	50
64.70588235	64.70588235	55.88235294	61.76470588
52.94117647	64.70588235	64.70588235	55.88235294
47.05882353	52.94117647	64.70588235	64.70588235
41.17647059	47.05882353	52.94117647	64.70588235
35.29411765	41.17647059	47.05882353	52.94117647
41.17647059	35.29411765	41.17647059	47.05882353
47.05882353	41.17647059	35.29411765	41.17647059
23.52941176	47.05882353	41.17647059	35.29411765
8.823529412	23.52941176	47.05882353	41.17647059
0	8.823529412	23.52941176	47.05882353

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
x[t] = + 7.53923727288653 + 0.826888794540061y0[t] + 0.069050797530038y1[t] -0.0217694593981442y2[t] + 2.53073445567753M1[t] + 1.28398056947326M2[t] + 6.14157249017446M3[t] -0.388146101513589M4[t] + 4.19313623427351M5[t] + 1.84968635378636M6[t] -2.19663690001674M7[t] -0.985667763809098M8[t] + 3.69010557347512M9[t] + 9.24640884194022M10[t] + 4.94710351660652M11[t] -0.0551774169632081t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
x[t] =  +  7.53923727288653 +  0.826888794540061y0[t] +  0.069050797530038y1[t] -0.0217694593981442y2[t] +  2.53073445567753M1[t] +  1.28398056947326M2[t] +  6.14157249017446M3[t] -0.388146101513589M4[t] +  4.19313623427351M5[t] +  1.84968635378636M6[t] -2.19663690001674M7[t] -0.985667763809098M8[t] +  3.69010557347512M9[t] +  9.24640884194022M10[t] +  4.94710351660652M11[t] -0.0551774169632081t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]x[t] =  +  7.53923727288653 +  0.826888794540061y0[t] +  0.069050797530038y1[t] -0.0217694593981442y2[t] +  2.53073445567753M1[t] +  1.28398056947326M2[t] +  6.14157249017446M3[t] -0.388146101513589M4[t] +  4.19313623427351M5[t] +  1.84968635378636M6[t] -2.19663690001674M7[t] -0.985667763809098M8[t] +  3.69010557347512M9[t] +  9.24640884194022M10[t] +  4.94710351660652M11[t] -0.0551774169632081t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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
x[t] = + 7.53923727288653 + 0.826888794540061y0[t] + 0.069050797530038y1[t] -0.0217694593981442y2[t] + 2.53073445567753M1[t] + 1.28398056947326M2[t] + 6.14157249017446M3[t] -0.388146101513589M4[t] + 4.19313623427351M5[t] + 1.84968635378636M6[t] -2.19663690001674M7[t] -0.985667763809098M8[t] + 3.69010557347512M9[t] + 9.24640884194022M10[t] + 4.94710351660652M11[t] -0.0551774169632081t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	7.53923727288653	6.662794	1.1315	0.260866	0.130433
y0	0.826888794540061	0.106434	7.769	0	0
y1	0.069050797530038	0.137621	0.5017	0.617085	0.308543
y2	-0.0217694593981442	0.107522	-0.2025	0.840015	0.420007
M1	2.53073445567753	5.31012	0.4766	0.634824	0.317412
M2	1.28398056947326	5.35786	0.2396	0.811157	0.405579
M3	6.14157249017446	5.335442	1.1511	0.252778	0.126389
M4	-0.388146101513589	5.338231	-0.0727	0.9422	0.4711
M5	4.19313623427351	5.307446	0.79	0.4316	0.2158
M6	1.84968635378636	5.374224	0.3442	0.731525	0.365762
M7	-2.19663690001674	5.299369	-0.4145	0.679499	0.339749
M8	-0.985667763809098	5.381707	-0.1832	0.855096	0.427548
M9	3.69010557347512	5.439081	0.6784	0.499251	0.249626
M10	9.24640884194022	5.488148	1.6848	0.095532	0.047766
M11	4.94710351660652	5.450876	0.9076	0.366551	0.183276
t	-0.0551774169632081	0.041184	-1.3398	0.183725	0.091863

\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) & 7.53923727288653 & 6.662794 & 1.1315 & 0.260866 & 0.130433 \tabularnewline
y0 & 0.826888794540061 & 0.106434 & 7.769 & 0 & 0 \tabularnewline
y1 & 0.069050797530038 & 0.137621 & 0.5017 & 0.617085 & 0.308543 \tabularnewline
y2 & -0.0217694593981442 & 0.107522 & -0.2025 & 0.840015 & 0.420007 \tabularnewline
M1 & 2.53073445567753 & 5.31012 & 0.4766 & 0.634824 & 0.317412 \tabularnewline
M2 & 1.28398056947326 & 5.35786 & 0.2396 & 0.811157 & 0.405579 \tabularnewline
M3 & 6.14157249017446 & 5.335442 & 1.1511 & 0.252778 & 0.126389 \tabularnewline
M4 & -0.388146101513589 & 5.338231 & -0.0727 & 0.9422 & 0.4711 \tabularnewline
M5 & 4.19313623427351 & 5.307446 & 0.79 & 0.4316 & 0.2158 \tabularnewline
M6 & 1.84968635378636 & 5.374224 & 0.3442 & 0.731525 & 0.365762 \tabularnewline
M7 & -2.19663690001674 & 5.299369 & -0.4145 & 0.679499 & 0.339749 \tabularnewline
M8 & -0.985667763809098 & 5.381707 & -0.1832 & 0.855096 & 0.427548 \tabularnewline
M9 & 3.69010557347512 & 5.439081 & 0.6784 & 0.499251 & 0.249626 \tabularnewline
M10 & 9.24640884194022 & 5.488148 & 1.6848 & 0.095532 & 0.047766 \tabularnewline
M11 & 4.94710351660652 & 5.450876 & 0.9076 & 0.366551 & 0.183276 \tabularnewline
t & -0.0551774169632081 & 0.041184 & -1.3398 & 0.183725 & 0.091863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&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]7.53923727288653[/C][C]6.662794[/C][C]1.1315[/C][C]0.260866[/C][C]0.130433[/C][/ROW]
[ROW][C]y0[/C][C]0.826888794540061[/C][C]0.106434[/C][C]7.769[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y1[/C][C]0.069050797530038[/C][C]0.137621[/C][C]0.5017[/C][C]0.617085[/C][C]0.308543[/C][/ROW]
[ROW][C]y2[/C][C]-0.0217694593981442[/C][C]0.107522[/C][C]-0.2025[/C][C]0.840015[/C][C]0.420007[/C][/ROW]
[ROW][C]M1[/C][C]2.53073445567753[/C][C]5.31012[/C][C]0.4766[/C][C]0.634824[/C][C]0.317412[/C][/ROW]
[ROW][C]M2[/C][C]1.28398056947326[/C][C]5.35786[/C][C]0.2396[/C][C]0.811157[/C][C]0.405579[/C][/ROW]
[ROW][C]M3[/C][C]6.14157249017446[/C][C]5.335442[/C][C]1.1511[/C][C]0.252778[/C][C]0.126389[/C][/ROW]
[ROW][C]M4[/C][C]-0.388146101513589[/C][C]5.338231[/C][C]-0.0727[/C][C]0.9422[/C][C]0.4711[/C][/ROW]
[ROW][C]M5[/C][C]4.19313623427351[/C][C]5.307446[/C][C]0.79[/C][C]0.4316[/C][C]0.2158[/C][/ROW]
[ROW][C]M6[/C][C]1.84968635378636[/C][C]5.374224[/C][C]0.3442[/C][C]0.731525[/C][C]0.365762[/C][/ROW]
[ROW][C]M7[/C][C]-2.19663690001674[/C][C]5.299369[/C][C]-0.4145[/C][C]0.679499[/C][C]0.339749[/C][/ROW]
[ROW][C]M8[/C][C]-0.985667763809098[/C][C]5.381707[/C][C]-0.1832[/C][C]0.855096[/C][C]0.427548[/C][/ROW]
[ROW][C]M9[/C][C]3.69010557347512[/C][C]5.439081[/C][C]0.6784[/C][C]0.499251[/C][C]0.249626[/C][/ROW]
[ROW][C]M10[/C][C]9.24640884194022[/C][C]5.488148[/C][C]1.6848[/C][C]0.095532[/C][C]0.047766[/C][/ROW]
[ROW][C]M11[/C][C]4.94710351660652[/C][C]5.450876[/C][C]0.9076[/C][C]0.366551[/C][C]0.183276[/C][/ROW]
[ROW][C]t[/C][C]-0.0551774169632081[/C][C]0.041184[/C][C]-1.3398[/C][C]0.183725[/C][C]0.091863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	7.53923727288653	6.662794	1.1315	0.260866	0.130433
y0	0.826888794540061	0.106434	7.769	0	0
y1	0.069050797530038	0.137621	0.5017	0.617085	0.308543
y2	-0.0217694593981442	0.107522	-0.2025	0.840015	0.420007
M1	2.53073445567753	5.31012	0.4766	0.634824	0.317412
M2	1.28398056947326	5.35786	0.2396	0.811157	0.405579
M3	6.14157249017446	5.335442	1.1511	0.252778	0.126389
M4	-0.388146101513589	5.338231	-0.0727	0.9422	0.4711
M5	4.19313623427351	5.307446	0.79	0.4316	0.2158
M6	1.84968635378636	5.374224	0.3442	0.731525	0.365762
M7	-2.19663690001674	5.299369	-0.4145	0.679499	0.339749
M8	-0.985667763809098	5.381707	-0.1832	0.855096	0.427548
M9	3.69010557347512	5.439081	0.6784	0.499251	0.249626
M10	9.24640884194022	5.488148	1.6848	0.095532	0.047766
M11	4.94710351660652	5.450876	0.9076	0.366551	0.183276
t	-0.0551774169632081	0.041184	-1.3398	0.183725	0.091863

Multiple Linear Regression - Regression Statistics
Multiple R	0.903067366036784
R-squared	0.815530667600615
Adjusted R-squared	0.78444033067937
F-TEST (value)	26.2310012807659
F-TEST (DF numerator)	15
F-TEST (DF denominator)	89
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.8315560323775
Sum Squared Residuals	10441.7119413455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.903067366036784 \tabularnewline
R-squared & 0.815530667600615 \tabularnewline
Adjusted R-squared & 0.78444033067937 \tabularnewline
F-TEST (value) & 26.2310012807659 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 89 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.8315560323775 \tabularnewline
Sum Squared Residuals & 10441.7119413455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.903067366036784[/C][/ROW]
[ROW][C]R-squared[/C][C]0.815530667600615[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.78444033067937[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]26.2310012807659[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]89[/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]10.8315560323775[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10441.7119413455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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 R	0.903067366036784
R-squared	0.815530667600615
Adjusted R-squared	0.78444033067937
F-TEST (value)	26.2310012807659
F-TEST (DF numerator)	15
F-TEST (DF denominator)	89
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.8315560323775
Sum Squared Residuals	10441.7119413455

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	108.8235294	107.566092217664	1.25743718233622
2	111.7647059	104.110260547263	7.65444535273689
3	117.6470588	111.013554713815	6.63350408618513
4	111.7647059	109.559828809105	2.20487709089501
5	120.5882353	109.564035366087	11.0241999339127
6	102.9411765	113.927248856071	-10.9860723560715
7	114.7058824	95.9709203917174	18.7349620082826
8	114.7058824	105.44418864151	9.26169375848992
9	117.6470588	111.261313816976	6.38574498302432
10	111.7647059	118.938354188982	-7.17364828898211
11	97.05882353	109.922910324241	-12.8640867942415
12	94.11764706	92.2902910657738	1.82735599422621
13	82.35294118	91.4464249752709	-9.09348379527088
14	82.35294118	80.5334387371093	1.81950244289065
15	85.29411765	84.5875187388735	0.706598911126541
16	85.29411765	80.6907598830159	4.60335776698413
17	73.52941176	85.4199553827698	-11.8905436227698
18	61.76470588	73.2291967920726	-11.4644909120726
19	32.35294118	58.5872303336638	-26.2342891536638
20	20.58823529	34.8665123583125	-14.2782770683125
21	50	27.9842102848179	22.0157897151821
22	70.58823529	57.6335106957213	12.9547245942787
23	76.47058824	72.5902261112744	3.88036212872557
24	79.41176471	73.2333527664178	6.17841194358217
25	73.52941176	78.0989220812656	-4.56951032126561
26	76.47058824	72.007973975433	4.46261426456703
27	73.52941176	78.7722053689506	-5.24279360895062
28	70.58823529	70.0864297115538	0.501805578446237
29	64.70588235	71.913390360985	-7.20750801098503
30	64.70588235	64.511648572953	0.194233777047068
31	64.70588235	60.0679945620729	4.63788778792709
32	61.76470588	61.3518419248102	0.412863955189781
33	50	63.5404119793233	-13.5404119793233
34	47.05882353	59.1103437866636	-12.0515202566636
35	35.29411765	51.5755006765849	-16.2813830265849
36	20.58823529	36.8981369858392	-16.3099016958392
37	41.17647059	26.4652301852713	14.7112404047287
38	47.05882353	41.4281383326729	5.63068519732714
39	44.11764706	52.8363777441776	-8.71873068417762
40	35.29411765	43.7774422791358	-8.48332462913585
41	41.17647059	40.6763233761131	0.500147213886919
42	58.82352941	42.5965038892347	16.2270255207653
43	29.41176471	53.6854230404153	-24.2736583304153
44	55.88235294	31.6114439436683	24.2709089963316
45	55.88235294	55.7051999164809	0.177153023519054
46	64.70588235	63.674419213818	1.031463136182
47	70.58823529	66.0397636732268	4.5484716167732
48	64.70588235	66.5108062140631	-1.80492386406311
49	61.76470588	64.3364092177825	-2.57170333778247
50	55.88235294	60.068215243454	-4.18586230345405
51	73.52941176	59.931543078139	13.5978686818609
52	61.76470588	67.5966489242215	-5.83194304422145
53	67.64705882	63.7412495088871	3.90580931111286
54	67.64705882	65.0101446888536	2.63691413114641
55	55.88235294	61.5709364669332	-5.68858352693322
56	52.94117647	52.8705690794532	0.0706073905468203
57	55.88235294	54.246776810246	1.63557612975403
58	55.88235294	62.2329492336114	-6.35059629361144
59	64.70588235	58.145584893991	6.56029745600893
60	67.64705882	60.3753537360986	7.27170508390143
61	58.82352941	65.892208383411	-7.06867897341103
62	41.17647059	57.3052065985106	-16.1287360085106
63	41.17647059	46.8421663428646	-5.66569575286456
64	41.17647059	39.2308103138721	1.94566027612786
65	44.11764706	44.1410821631747	-0.0234351031746805
66	32.35294118	44.1744807315322	-11.8215395515322
67	50	30.5479671784644	19.4520328215356
68	47.05882353	45.4195239470895	1.63929958291055
69	58.82352941	49.0827487741688	9.74078063583118
70	70.58823529	63.7247205774936	6.86351471250644
71	67.64705882	69.974731443895	-2.32767262389501
72	58.82352941	63.096675681252	-4.27314627125194
73	61.76470588	57.8169532546267	3.94775262537328
74	55.88235294	58.4018038962233	-2.51945095622334
75	67.64705882	58.735340714515	8.91171810548505
76	67.64705882	61.4083391854887	6.23871963451133
77	67.64705882	66.8748620715258	0.772196748474231
78	67.64705882	64.2201234870896	3.42693533291036
79	79.41176471	60.1186228163233	19.2931418936767
80	76.47058824	71.0025180070682	5.46807023293176
81	50	74.0034503859922	-24.0034503859922
82	70.58823529	57.1571415688206	13.4310937211794
83	76.47058824	68.0630524798642	8.40753576013584
84	70.58823529	69.9227077486253	0.665527541374659
85	79.41176471	67.4921994577806	11.9195652522194
86	79.41176471	72.9521089540444	6.4596557559556
87	76.47058824	78.4918508449737	-2.02126260497373
88	70.58823529	69.2828455050576	1.30538978494244
89	67.64705882	68.7418081030667	-1.09474928306669
90	67.64705882	63.5690015990042	4.07805722099579
91	70.58823529	59.3924659910184	11.1957692989816
92	50	63.0443113978172	-13.0443113978172
93	61.76470588	50.843816838413	10.920889041587
94	55.88235294	64.5873842648895	-8.70503132488945
95	64.70588235	56.6294068669222	8.07647548307782
96	64.70588235	58.2609110819302	6.4449712680698
97	52.94117647	61.4737955069276	-8.53261903692764
98	47.05882353	50.2516772752893	-3.19285374528927
99	41.17647059	49.3776777236911	-8.20120713369113
100	35.29411765	37.7786601085497	-2.48454245854970
101	41.17647059	37.1625877773905	4.01388281260949
102	47.05882353	39.3498866931887	7.70893683681133
103	23.52941176	40.6466745593912	-17.1172627993912
104	8.823529412	22.6243848622709	-13.8008554502709
105	0	13.3320711635821	-13.3320711635821

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108.8235294 & 107.566092217664 & 1.25743718233622 \tabularnewline
2 & 111.7647059 & 104.110260547263 & 7.65444535273689 \tabularnewline
3 & 117.6470588 & 111.013554713815 & 6.63350408618513 \tabularnewline
4 & 111.7647059 & 109.559828809105 & 2.20487709089501 \tabularnewline
5 & 120.5882353 & 109.564035366087 & 11.0241999339127 \tabularnewline
6 & 102.9411765 & 113.927248856071 & -10.9860723560715 \tabularnewline
7 & 114.7058824 & 95.9709203917174 & 18.7349620082826 \tabularnewline
8 & 114.7058824 & 105.44418864151 & 9.26169375848992 \tabularnewline
9 & 117.6470588 & 111.261313816976 & 6.38574498302432 \tabularnewline
10 & 111.7647059 & 118.938354188982 & -7.17364828898211 \tabularnewline
11 & 97.05882353 & 109.922910324241 & -12.8640867942415 \tabularnewline
12 & 94.11764706 & 92.2902910657738 & 1.82735599422621 \tabularnewline
13 & 82.35294118 & 91.4464249752709 & -9.09348379527088 \tabularnewline
14 & 82.35294118 & 80.5334387371093 & 1.81950244289065 \tabularnewline
15 & 85.29411765 & 84.5875187388735 & 0.706598911126541 \tabularnewline
16 & 85.29411765 & 80.6907598830159 & 4.60335776698413 \tabularnewline
17 & 73.52941176 & 85.4199553827698 & -11.8905436227698 \tabularnewline
18 & 61.76470588 & 73.2291967920726 & -11.4644909120726 \tabularnewline
19 & 32.35294118 & 58.5872303336638 & -26.2342891536638 \tabularnewline
20 & 20.58823529 & 34.8665123583125 & -14.2782770683125 \tabularnewline
21 & 50 & 27.9842102848179 & 22.0157897151821 \tabularnewline
22 & 70.58823529 & 57.6335106957213 & 12.9547245942787 \tabularnewline
23 & 76.47058824 & 72.5902261112744 & 3.88036212872557 \tabularnewline
24 & 79.41176471 & 73.2333527664178 & 6.17841194358217 \tabularnewline
25 & 73.52941176 & 78.0989220812656 & -4.56951032126561 \tabularnewline
26 & 76.47058824 & 72.007973975433 & 4.46261426456703 \tabularnewline
27 & 73.52941176 & 78.7722053689506 & -5.24279360895062 \tabularnewline
28 & 70.58823529 & 70.0864297115538 & 0.501805578446237 \tabularnewline
29 & 64.70588235 & 71.913390360985 & -7.20750801098503 \tabularnewline
30 & 64.70588235 & 64.511648572953 & 0.194233777047068 \tabularnewline
31 & 64.70588235 & 60.0679945620729 & 4.63788778792709 \tabularnewline
32 & 61.76470588 & 61.3518419248102 & 0.412863955189781 \tabularnewline
33 & 50 & 63.5404119793233 & -13.5404119793233 \tabularnewline
34 & 47.05882353 & 59.1103437866636 & -12.0515202566636 \tabularnewline
35 & 35.29411765 & 51.5755006765849 & -16.2813830265849 \tabularnewline
36 & 20.58823529 & 36.8981369858392 & -16.3099016958392 \tabularnewline
37 & 41.17647059 & 26.4652301852713 & 14.7112404047287 \tabularnewline
38 & 47.05882353 & 41.4281383326729 & 5.63068519732714 \tabularnewline
39 & 44.11764706 & 52.8363777441776 & -8.71873068417762 \tabularnewline
40 & 35.29411765 & 43.7774422791358 & -8.48332462913585 \tabularnewline
41 & 41.17647059 & 40.6763233761131 & 0.500147213886919 \tabularnewline
42 & 58.82352941 & 42.5965038892347 & 16.2270255207653 \tabularnewline
43 & 29.41176471 & 53.6854230404153 & -24.2736583304153 \tabularnewline
44 & 55.88235294 & 31.6114439436683 & 24.2709089963316 \tabularnewline
45 & 55.88235294 & 55.7051999164809 & 0.177153023519054 \tabularnewline
46 & 64.70588235 & 63.674419213818 & 1.031463136182 \tabularnewline
47 & 70.58823529 & 66.0397636732268 & 4.5484716167732 \tabularnewline
48 & 64.70588235 & 66.5108062140631 & -1.80492386406311 \tabularnewline
49 & 61.76470588 & 64.3364092177825 & -2.57170333778247 \tabularnewline
50 & 55.88235294 & 60.068215243454 & -4.18586230345405 \tabularnewline
51 & 73.52941176 & 59.931543078139 & 13.5978686818609 \tabularnewline
52 & 61.76470588 & 67.5966489242215 & -5.83194304422145 \tabularnewline
53 & 67.64705882 & 63.7412495088871 & 3.90580931111286 \tabularnewline
54 & 67.64705882 & 65.0101446888536 & 2.63691413114641 \tabularnewline
55 & 55.88235294 & 61.5709364669332 & -5.68858352693322 \tabularnewline
56 & 52.94117647 & 52.8705690794532 & 0.0706073905468203 \tabularnewline
57 & 55.88235294 & 54.246776810246 & 1.63557612975403 \tabularnewline
58 & 55.88235294 & 62.2329492336114 & -6.35059629361144 \tabularnewline
59 & 64.70588235 & 58.145584893991 & 6.56029745600893 \tabularnewline
60 & 67.64705882 & 60.3753537360986 & 7.27170508390143 \tabularnewline
61 & 58.82352941 & 65.892208383411 & -7.06867897341103 \tabularnewline
62 & 41.17647059 & 57.3052065985106 & -16.1287360085106 \tabularnewline
63 & 41.17647059 & 46.8421663428646 & -5.66569575286456 \tabularnewline
64 & 41.17647059 & 39.2308103138721 & 1.94566027612786 \tabularnewline
65 & 44.11764706 & 44.1410821631747 & -0.0234351031746805 \tabularnewline
66 & 32.35294118 & 44.1744807315322 & -11.8215395515322 \tabularnewline
67 & 50 & 30.5479671784644 & 19.4520328215356 \tabularnewline
68 & 47.05882353 & 45.4195239470895 & 1.63929958291055 \tabularnewline
69 & 58.82352941 & 49.0827487741688 & 9.74078063583118 \tabularnewline
70 & 70.58823529 & 63.7247205774936 & 6.86351471250644 \tabularnewline
71 & 67.64705882 & 69.974731443895 & -2.32767262389501 \tabularnewline
72 & 58.82352941 & 63.096675681252 & -4.27314627125194 \tabularnewline
73 & 61.76470588 & 57.8169532546267 & 3.94775262537328 \tabularnewline
74 & 55.88235294 & 58.4018038962233 & -2.51945095622334 \tabularnewline
75 & 67.64705882 & 58.735340714515 & 8.91171810548505 \tabularnewline
76 & 67.64705882 & 61.4083391854887 & 6.23871963451133 \tabularnewline
77 & 67.64705882 & 66.8748620715258 & 0.772196748474231 \tabularnewline
78 & 67.64705882 & 64.2201234870896 & 3.42693533291036 \tabularnewline
79 & 79.41176471 & 60.1186228163233 & 19.2931418936767 \tabularnewline
80 & 76.47058824 & 71.0025180070682 & 5.46807023293176 \tabularnewline
81 & 50 & 74.0034503859922 & -24.0034503859922 \tabularnewline
82 & 70.58823529 & 57.1571415688206 & 13.4310937211794 \tabularnewline
83 & 76.47058824 & 68.0630524798642 & 8.40753576013584 \tabularnewline
84 & 70.58823529 & 69.9227077486253 & 0.665527541374659 \tabularnewline
85 & 79.41176471 & 67.4921994577806 & 11.9195652522194 \tabularnewline
86 & 79.41176471 & 72.9521089540444 & 6.4596557559556 \tabularnewline
87 & 76.47058824 & 78.4918508449737 & -2.02126260497373 \tabularnewline
88 & 70.58823529 & 69.2828455050576 & 1.30538978494244 \tabularnewline
89 & 67.64705882 & 68.7418081030667 & -1.09474928306669 \tabularnewline
90 & 67.64705882 & 63.5690015990042 & 4.07805722099579 \tabularnewline
91 & 70.58823529 & 59.3924659910184 & 11.1957692989816 \tabularnewline
92 & 50 & 63.0443113978172 & -13.0443113978172 \tabularnewline
93 & 61.76470588 & 50.843816838413 & 10.920889041587 \tabularnewline
94 & 55.88235294 & 64.5873842648895 & -8.70503132488945 \tabularnewline
95 & 64.70588235 & 56.6294068669222 & 8.07647548307782 \tabularnewline
96 & 64.70588235 & 58.2609110819302 & 6.4449712680698 \tabularnewline
97 & 52.94117647 & 61.4737955069276 & -8.53261903692764 \tabularnewline
98 & 47.05882353 & 50.2516772752893 & -3.19285374528927 \tabularnewline
99 & 41.17647059 & 49.3776777236911 & -8.20120713369113 \tabularnewline
100 & 35.29411765 & 37.7786601085497 & -2.48454245854970 \tabularnewline
101 & 41.17647059 & 37.1625877773905 & 4.01388281260949 \tabularnewline
102 & 47.05882353 & 39.3498866931887 & 7.70893683681133 \tabularnewline
103 & 23.52941176 & 40.6466745593912 & -17.1172627993912 \tabularnewline
104 & 8.823529412 & 22.6243848622709 & -13.8008554502709 \tabularnewline
105 & 0 & 13.3320711635821 & -13.3320711635821 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&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]108.8235294[/C][C]107.566092217664[/C][C]1.25743718233622[/C][/ROW]
[ROW][C]2[/C][C]111.7647059[/C][C]104.110260547263[/C][C]7.65444535273689[/C][/ROW]
[ROW][C]3[/C][C]117.6470588[/C][C]111.013554713815[/C][C]6.63350408618513[/C][/ROW]
[ROW][C]4[/C][C]111.7647059[/C][C]109.559828809105[/C][C]2.20487709089501[/C][/ROW]
[ROW][C]5[/C][C]120.5882353[/C][C]109.564035366087[/C][C]11.0241999339127[/C][/ROW]
[ROW][C]6[/C][C]102.9411765[/C][C]113.927248856071[/C][C]-10.9860723560715[/C][/ROW]
[ROW][C]7[/C][C]114.7058824[/C][C]95.9709203917174[/C][C]18.7349620082826[/C][/ROW]
[ROW][C]8[/C][C]114.7058824[/C][C]105.44418864151[/C][C]9.26169375848992[/C][/ROW]
[ROW][C]9[/C][C]117.6470588[/C][C]111.261313816976[/C][C]6.38574498302432[/C][/ROW]
[ROW][C]10[/C][C]111.7647059[/C][C]118.938354188982[/C][C]-7.17364828898211[/C][/ROW]
[ROW][C]11[/C][C]97.05882353[/C][C]109.922910324241[/C][C]-12.8640867942415[/C][/ROW]
[ROW][C]12[/C][C]94.11764706[/C][C]92.2902910657738[/C][C]1.82735599422621[/C][/ROW]
[ROW][C]13[/C][C]82.35294118[/C][C]91.4464249752709[/C][C]-9.09348379527088[/C][/ROW]
[ROW][C]14[/C][C]82.35294118[/C][C]80.5334387371093[/C][C]1.81950244289065[/C][/ROW]
[ROW][C]15[/C][C]85.29411765[/C][C]84.5875187388735[/C][C]0.706598911126541[/C][/ROW]
[ROW][C]16[/C][C]85.29411765[/C][C]80.6907598830159[/C][C]4.60335776698413[/C][/ROW]
[ROW][C]17[/C][C]73.52941176[/C][C]85.4199553827698[/C][C]-11.8905436227698[/C][/ROW]
[ROW][C]18[/C][C]61.76470588[/C][C]73.2291967920726[/C][C]-11.4644909120726[/C][/ROW]
[ROW][C]19[/C][C]32.35294118[/C][C]58.5872303336638[/C][C]-26.2342891536638[/C][/ROW]
[ROW][C]20[/C][C]20.58823529[/C][C]34.8665123583125[/C][C]-14.2782770683125[/C][/ROW]
[ROW][C]21[/C][C]50[/C][C]27.9842102848179[/C][C]22.0157897151821[/C][/ROW]
[ROW][C]22[/C][C]70.58823529[/C][C]57.6335106957213[/C][C]12.9547245942787[/C][/ROW]
[ROW][C]23[/C][C]76.47058824[/C][C]72.5902261112744[/C][C]3.88036212872557[/C][/ROW]
[ROW][C]24[/C][C]79.41176471[/C][C]73.2333527664178[/C][C]6.17841194358217[/C][/ROW]
[ROW][C]25[/C][C]73.52941176[/C][C]78.0989220812656[/C][C]-4.56951032126561[/C][/ROW]
[ROW][C]26[/C][C]76.47058824[/C][C]72.007973975433[/C][C]4.46261426456703[/C][/ROW]
[ROW][C]27[/C][C]73.52941176[/C][C]78.7722053689506[/C][C]-5.24279360895062[/C][/ROW]
[ROW][C]28[/C][C]70.58823529[/C][C]70.0864297115538[/C][C]0.501805578446237[/C][/ROW]
[ROW][C]29[/C][C]64.70588235[/C][C]71.913390360985[/C][C]-7.20750801098503[/C][/ROW]
[ROW][C]30[/C][C]64.70588235[/C][C]64.511648572953[/C][C]0.194233777047068[/C][/ROW]
[ROW][C]31[/C][C]64.70588235[/C][C]60.0679945620729[/C][C]4.63788778792709[/C][/ROW]
[ROW][C]32[/C][C]61.76470588[/C][C]61.3518419248102[/C][C]0.412863955189781[/C][/ROW]
[ROW][C]33[/C][C]50[/C][C]63.5404119793233[/C][C]-13.5404119793233[/C][/ROW]
[ROW][C]34[/C][C]47.05882353[/C][C]59.1103437866636[/C][C]-12.0515202566636[/C][/ROW]
[ROW][C]35[/C][C]35.29411765[/C][C]51.5755006765849[/C][C]-16.2813830265849[/C][/ROW]
[ROW][C]36[/C][C]20.58823529[/C][C]36.8981369858392[/C][C]-16.3099016958392[/C][/ROW]
[ROW][C]37[/C][C]41.17647059[/C][C]26.4652301852713[/C][C]14.7112404047287[/C][/ROW]
[ROW][C]38[/C][C]47.05882353[/C][C]41.4281383326729[/C][C]5.63068519732714[/C][/ROW]
[ROW][C]39[/C][C]44.11764706[/C][C]52.8363777441776[/C][C]-8.71873068417762[/C][/ROW]
[ROW][C]40[/C][C]35.29411765[/C][C]43.7774422791358[/C][C]-8.48332462913585[/C][/ROW]
[ROW][C]41[/C][C]41.17647059[/C][C]40.6763233761131[/C][C]0.500147213886919[/C][/ROW]
[ROW][C]42[/C][C]58.82352941[/C][C]42.5965038892347[/C][C]16.2270255207653[/C][/ROW]
[ROW][C]43[/C][C]29.41176471[/C][C]53.6854230404153[/C][C]-24.2736583304153[/C][/ROW]
[ROW][C]44[/C][C]55.88235294[/C][C]31.6114439436683[/C][C]24.2709089963316[/C][/ROW]
[ROW][C]45[/C][C]55.88235294[/C][C]55.7051999164809[/C][C]0.177153023519054[/C][/ROW]
[ROW][C]46[/C][C]64.70588235[/C][C]63.674419213818[/C][C]1.031463136182[/C][/ROW]
[ROW][C]47[/C][C]70.58823529[/C][C]66.0397636732268[/C][C]4.5484716167732[/C][/ROW]
[ROW][C]48[/C][C]64.70588235[/C][C]66.5108062140631[/C][C]-1.80492386406311[/C][/ROW]
[ROW][C]49[/C][C]61.76470588[/C][C]64.3364092177825[/C][C]-2.57170333778247[/C][/ROW]
[ROW][C]50[/C][C]55.88235294[/C][C]60.068215243454[/C][C]-4.18586230345405[/C][/ROW]
[ROW][C]51[/C][C]73.52941176[/C][C]59.931543078139[/C][C]13.5978686818609[/C][/ROW]
[ROW][C]52[/C][C]61.76470588[/C][C]67.5966489242215[/C][C]-5.83194304422145[/C][/ROW]
[ROW][C]53[/C][C]67.64705882[/C][C]63.7412495088871[/C][C]3.90580931111286[/C][/ROW]
[ROW][C]54[/C][C]67.64705882[/C][C]65.0101446888536[/C][C]2.63691413114641[/C][/ROW]
[ROW][C]55[/C][C]55.88235294[/C][C]61.5709364669332[/C][C]-5.68858352693322[/C][/ROW]
[ROW][C]56[/C][C]52.94117647[/C][C]52.8705690794532[/C][C]0.0706073905468203[/C][/ROW]
[ROW][C]57[/C][C]55.88235294[/C][C]54.246776810246[/C][C]1.63557612975403[/C][/ROW]
[ROW][C]58[/C][C]55.88235294[/C][C]62.2329492336114[/C][C]-6.35059629361144[/C][/ROW]
[ROW][C]59[/C][C]64.70588235[/C][C]58.145584893991[/C][C]6.56029745600893[/C][/ROW]
[ROW][C]60[/C][C]67.64705882[/C][C]60.3753537360986[/C][C]7.27170508390143[/C][/ROW]
[ROW][C]61[/C][C]58.82352941[/C][C]65.892208383411[/C][C]-7.06867897341103[/C][/ROW]
[ROW][C]62[/C][C]41.17647059[/C][C]57.3052065985106[/C][C]-16.1287360085106[/C][/ROW]
[ROW][C]63[/C][C]41.17647059[/C][C]46.8421663428646[/C][C]-5.66569575286456[/C][/ROW]
[ROW][C]64[/C][C]41.17647059[/C][C]39.2308103138721[/C][C]1.94566027612786[/C][/ROW]
[ROW][C]65[/C][C]44.11764706[/C][C]44.1410821631747[/C][C]-0.0234351031746805[/C][/ROW]
[ROW][C]66[/C][C]32.35294118[/C][C]44.1744807315322[/C][C]-11.8215395515322[/C][/ROW]
[ROW][C]67[/C][C]50[/C][C]30.5479671784644[/C][C]19.4520328215356[/C][/ROW]
[ROW][C]68[/C][C]47.05882353[/C][C]45.4195239470895[/C][C]1.63929958291055[/C][/ROW]
[ROW][C]69[/C][C]58.82352941[/C][C]49.0827487741688[/C][C]9.74078063583118[/C][/ROW]
[ROW][C]70[/C][C]70.58823529[/C][C]63.7247205774936[/C][C]6.86351471250644[/C][/ROW]
[ROW][C]71[/C][C]67.64705882[/C][C]69.974731443895[/C][C]-2.32767262389501[/C][/ROW]
[ROW][C]72[/C][C]58.82352941[/C][C]63.096675681252[/C][C]-4.27314627125194[/C][/ROW]
[ROW][C]73[/C][C]61.76470588[/C][C]57.8169532546267[/C][C]3.94775262537328[/C][/ROW]
[ROW][C]74[/C][C]55.88235294[/C][C]58.4018038962233[/C][C]-2.51945095622334[/C][/ROW]
[ROW][C]75[/C][C]67.64705882[/C][C]58.735340714515[/C][C]8.91171810548505[/C][/ROW]
[ROW][C]76[/C][C]67.64705882[/C][C]61.4083391854887[/C][C]6.23871963451133[/C][/ROW]
[ROW][C]77[/C][C]67.64705882[/C][C]66.8748620715258[/C][C]0.772196748474231[/C][/ROW]
[ROW][C]78[/C][C]67.64705882[/C][C]64.2201234870896[/C][C]3.42693533291036[/C][/ROW]
[ROW][C]79[/C][C]79.41176471[/C][C]60.1186228163233[/C][C]19.2931418936767[/C][/ROW]
[ROW][C]80[/C][C]76.47058824[/C][C]71.0025180070682[/C][C]5.46807023293176[/C][/ROW]
[ROW][C]81[/C][C]50[/C][C]74.0034503859922[/C][C]-24.0034503859922[/C][/ROW]
[ROW][C]82[/C][C]70.58823529[/C][C]57.1571415688206[/C][C]13.4310937211794[/C][/ROW]
[ROW][C]83[/C][C]76.47058824[/C][C]68.0630524798642[/C][C]8.40753576013584[/C][/ROW]
[ROW][C]84[/C][C]70.58823529[/C][C]69.9227077486253[/C][C]0.665527541374659[/C][/ROW]
[ROW][C]85[/C][C]79.41176471[/C][C]67.4921994577806[/C][C]11.9195652522194[/C][/ROW]
[ROW][C]86[/C][C]79.41176471[/C][C]72.9521089540444[/C][C]6.4596557559556[/C][/ROW]
[ROW][C]87[/C][C]76.47058824[/C][C]78.4918508449737[/C][C]-2.02126260497373[/C][/ROW]
[ROW][C]88[/C][C]70.58823529[/C][C]69.2828455050576[/C][C]1.30538978494244[/C][/ROW]
[ROW][C]89[/C][C]67.64705882[/C][C]68.7418081030667[/C][C]-1.09474928306669[/C][/ROW]
[ROW][C]90[/C][C]67.64705882[/C][C]63.5690015990042[/C][C]4.07805722099579[/C][/ROW]
[ROW][C]91[/C][C]70.58823529[/C][C]59.3924659910184[/C][C]11.1957692989816[/C][/ROW]
[ROW][C]92[/C][C]50[/C][C]63.0443113978172[/C][C]-13.0443113978172[/C][/ROW]
[ROW][C]93[/C][C]61.76470588[/C][C]50.843816838413[/C][C]10.920889041587[/C][/ROW]
[ROW][C]94[/C][C]55.88235294[/C][C]64.5873842648895[/C][C]-8.70503132488945[/C][/ROW]
[ROW][C]95[/C][C]64.70588235[/C][C]56.6294068669222[/C][C]8.07647548307782[/C][/ROW]
[ROW][C]96[/C][C]64.70588235[/C][C]58.2609110819302[/C][C]6.4449712680698[/C][/ROW]
[ROW][C]97[/C][C]52.94117647[/C][C]61.4737955069276[/C][C]-8.53261903692764[/C][/ROW]
[ROW][C]98[/C][C]47.05882353[/C][C]50.2516772752893[/C][C]-3.19285374528927[/C][/ROW]
[ROW][C]99[/C][C]41.17647059[/C][C]49.3776777236911[/C][C]-8.20120713369113[/C][/ROW]
[ROW][C]100[/C][C]35.29411765[/C][C]37.7786601085497[/C][C]-2.48454245854970[/C][/ROW]
[ROW][C]101[/C][C]41.17647059[/C][C]37.1625877773905[/C][C]4.01388281260949[/C][/ROW]
[ROW][C]102[/C][C]47.05882353[/C][C]39.3498866931887[/C][C]7.70893683681133[/C][/ROW]
[ROW][C]103[/C][C]23.52941176[/C][C]40.6466745593912[/C][C]-17.1172627993912[/C][/ROW]
[ROW][C]104[/C][C]8.823529412[/C][C]22.6243848622709[/C][C]-13.8008554502709[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]13.3320711635821[/C][C]-13.3320711635821[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	108.8235294	107.566092217664	1.25743718233622
2	111.7647059	104.110260547263	7.65444535273689
3	117.6470588	111.013554713815	6.63350408618513
4	111.7647059	109.559828809105	2.20487709089501
5	120.5882353	109.564035366087	11.0241999339127
6	102.9411765	113.927248856071	-10.9860723560715
7	114.7058824	95.9709203917174	18.7349620082826
8	114.7058824	105.44418864151	9.26169375848992
9	117.6470588	111.261313816976	6.38574498302432
10	111.7647059	118.938354188982	-7.17364828898211
11	97.05882353	109.922910324241	-12.8640867942415
12	94.11764706	92.2902910657738	1.82735599422621
13	82.35294118	91.4464249752709	-9.09348379527088
14	82.35294118	80.5334387371093	1.81950244289065
15	85.29411765	84.5875187388735	0.706598911126541
16	85.29411765	80.6907598830159	4.60335776698413
17	73.52941176	85.4199553827698	-11.8905436227698
18	61.76470588	73.2291967920726	-11.4644909120726
19	32.35294118	58.5872303336638	-26.2342891536638
20	20.58823529	34.8665123583125	-14.2782770683125
21	50	27.9842102848179	22.0157897151821
22	70.58823529	57.6335106957213	12.9547245942787
23	76.47058824	72.5902261112744	3.88036212872557
24	79.41176471	73.2333527664178	6.17841194358217
25	73.52941176	78.0989220812656	-4.56951032126561
26	76.47058824	72.007973975433	4.46261426456703
27	73.52941176	78.7722053689506	-5.24279360895062
28	70.58823529	70.0864297115538	0.501805578446237
29	64.70588235	71.913390360985	-7.20750801098503
30	64.70588235	64.511648572953	0.194233777047068
31	64.70588235	60.0679945620729	4.63788778792709
32	61.76470588	61.3518419248102	0.412863955189781
33	50	63.5404119793233	-13.5404119793233
34	47.05882353	59.1103437866636	-12.0515202566636
35	35.29411765	51.5755006765849	-16.2813830265849
36	20.58823529	36.8981369858392	-16.3099016958392
37	41.17647059	26.4652301852713	14.7112404047287
38	47.05882353	41.4281383326729	5.63068519732714
39	44.11764706	52.8363777441776	-8.71873068417762
40	35.29411765	43.7774422791358	-8.48332462913585
41	41.17647059	40.6763233761131	0.500147213886919
42	58.82352941	42.5965038892347	16.2270255207653
43	29.41176471	53.6854230404153	-24.2736583304153
44	55.88235294	31.6114439436683	24.2709089963316
45	55.88235294	55.7051999164809	0.177153023519054
46	64.70588235	63.674419213818	1.031463136182
47	70.58823529	66.0397636732268	4.5484716167732
48	64.70588235	66.5108062140631	-1.80492386406311
49	61.76470588	64.3364092177825	-2.57170333778247
50	55.88235294	60.068215243454	-4.18586230345405
51	73.52941176	59.931543078139	13.5978686818609
52	61.76470588	67.5966489242215	-5.83194304422145
53	67.64705882	63.7412495088871	3.90580931111286
54	67.64705882	65.0101446888536	2.63691413114641
55	55.88235294	61.5709364669332	-5.68858352693322
56	52.94117647	52.8705690794532	0.0706073905468203
57	55.88235294	54.246776810246	1.63557612975403
58	55.88235294	62.2329492336114	-6.35059629361144
59	64.70588235	58.145584893991	6.56029745600893
60	67.64705882	60.3753537360986	7.27170508390143
61	58.82352941	65.892208383411	-7.06867897341103
62	41.17647059	57.3052065985106	-16.1287360085106
63	41.17647059	46.8421663428646	-5.66569575286456
64	41.17647059	39.2308103138721	1.94566027612786
65	44.11764706	44.1410821631747	-0.0234351031746805
66	32.35294118	44.1744807315322	-11.8215395515322
67	50	30.5479671784644	19.4520328215356
68	47.05882353	45.4195239470895	1.63929958291055
69	58.82352941	49.0827487741688	9.74078063583118
70	70.58823529	63.7247205774936	6.86351471250644
71	67.64705882	69.974731443895	-2.32767262389501
72	58.82352941	63.096675681252	-4.27314627125194
73	61.76470588	57.8169532546267	3.94775262537328
74	55.88235294	58.4018038962233	-2.51945095622334
75	67.64705882	58.735340714515	8.91171810548505
76	67.64705882	61.4083391854887	6.23871963451133
77	67.64705882	66.8748620715258	0.772196748474231
78	67.64705882	64.2201234870896	3.42693533291036
79	79.41176471	60.1186228163233	19.2931418936767
80	76.47058824	71.0025180070682	5.46807023293176
81	50	74.0034503859922	-24.0034503859922
82	70.58823529	57.1571415688206	13.4310937211794
83	76.47058824	68.0630524798642	8.40753576013584
84	70.58823529	69.9227077486253	0.665527541374659
85	79.41176471	67.4921994577806	11.9195652522194
86	79.41176471	72.9521089540444	6.4596557559556
87	76.47058824	78.4918508449737	-2.02126260497373
88	70.58823529	69.2828455050576	1.30538978494244
89	67.64705882	68.7418081030667	-1.09474928306669
90	67.64705882	63.5690015990042	4.07805722099579
91	70.58823529	59.3924659910184	11.1957692989816
92	50	63.0443113978172	-13.0443113978172
93	61.76470588	50.843816838413	10.920889041587
94	55.88235294	64.5873842648895	-8.70503132488945
95	64.70588235	56.6294068669222	8.07647548307782
96	64.70588235	58.2609110819302	6.4449712680698
97	52.94117647	61.4737955069276	-8.53261903692764
98	47.05882353	50.2516772752893	-3.19285374528927
99	41.17647059	49.3776777236911	-8.20120713369113
100	35.29411765	37.7786601085497	-2.48454245854970
101	41.17647059	37.1625877773905	4.01388281260949
102	47.05882353	39.3498866931887	7.70893683681133
103	23.52941176	40.6466745593912	-17.1172627993912
104	8.823529412	22.6243848622709	-13.8008554502709
105	0	13.3320711635821	-13.3320711635821

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.281912121595811	0.563824243191622	0.71808787840419
20	0.18450624501164	0.36901249002328	0.81549375498836
21	0.9132219186573	0.173556162685399	0.0867780813426997
22	0.936220565501796	0.127558868996409	0.0637794344982045
23	0.906653814772452	0.186692370455096	0.0933461852275478
24	0.860546698720898	0.278906602558204	0.139453301279102
25	0.841262036971209	0.317475926057582	0.158737963028791
26	0.8093909114126	0.3812181771748	0.1906090885874
27	0.74109545406589	0.51780909186822	0.25890454593411
28	0.683409932501212	0.633180134997576	0.316590067498788
29	0.610487165534414	0.779025668931173	0.389512834465586
30	0.647512695327753	0.704974609344493	0.352487304672246
31	0.596562630194958	0.806874739610085	0.403437369805042
32	0.518677674644713	0.962644650710574	0.481322325355287
33	0.61662644631183	0.766747107376341	0.383373553688171
34	0.573743166103369	0.852513667793262	0.426256833896631
35	0.591186459533412	0.817627080933175	0.408813540466588
36	0.672702523555652	0.654594952888696	0.327297476444348
37	0.808790714569878	0.382418570860245	0.191209285430122
38	0.785094086263103	0.429811827473794	0.214905913736897
39	0.748816100835349	0.502367798329302	0.251183899164651
40	0.71022031164688	0.57955937670624	0.28977968835312
41	0.67579130411968	0.64841739176064	0.32420869588032
42	0.78906703145489	0.421865937090222	0.210932968545111
43	0.906549919956668	0.186900160086664	0.0934500800433318
44	0.977763306352532	0.0444733872949371	0.0222366936474685
45	0.968537957517603	0.0629240849647939	0.0314620424823969
46	0.957071701294215	0.0858565974115703	0.0429282987057852
47	0.959974955952985	0.0800500880940293	0.0400250440470147
48	0.947048858906828	0.105902282186344	0.052951141093172
49	0.92894337917981	0.142113241640380	0.0710566208201902
50	0.904772417437276	0.190455165125448	0.095227582562724
51	0.92632473600099	0.147350527998020	0.0736752639990101
52	0.906654996777704	0.186690006444592	0.0933450032222958
53	0.880191652925728	0.239616694148544	0.119808347074272
54	0.85051406395176	0.298971872096479	0.149485936048240
55	0.860382225049478	0.279235549901043	0.139617774950522
56	0.820527352207334	0.358945295585331	0.179472647792666
57	0.772724599008706	0.454550801982589	0.227275400991294
58	0.74529164920571	0.509416701588579	0.254708350794290
59	0.71564289548234	0.56871420903532	0.28435710451766
60	0.675933266627741	0.648133466744518	0.324066733372259
61	0.649097689772367	0.701804620455265	0.350902310227632
62	0.737764240989278	0.524471518021444	0.262235759010722
63	0.73092320337295	0.538153593254099	0.269076796627049
64	0.692225551125105	0.615548897749791	0.307774448874896
65	0.638098244514466	0.723803510971068	0.361901755485534
66	0.754558785199003	0.490882429601993	0.245441214800997
67	0.777125424470976	0.445749151058049	0.222874575529024
68	0.718650018957375	0.56269996208525	0.281349981042625
69	0.766957758849959	0.466084482300082	0.233042241150041
70	0.754312118504878	0.491375762990244	0.245687881495122
71	0.704767300863245	0.590465398273511	0.295232699136755
72	0.733531237544996	0.532937524910007	0.266468762455004
73	0.718168203006991	0.563663593986018	0.281831796993009
74	0.724858687457771	0.550282625084458	0.275141312542229
75	0.658960143174935	0.68207971365013	0.341039856825065
76	0.583644724111087	0.832710551777826	0.416355275888913
77	0.497235746448333	0.994471492896666	0.502764253551667
78	0.511643381420781	0.976713237158439	0.488356618579219
79	0.466190166954649	0.932380333909297	0.533809833045351
80	0.52571823404574	0.94856353190852	0.47428176595426
81	0.745240709302363	0.509518581395274	0.254759290697637
82	0.655194423367866	0.689611153264268	0.344805576632134
83	0.556357603076589	0.887284793846823	0.443642396923411
84	0.609691108035881	0.780617783928239	0.390308891964119
85	0.466556175127239	0.933112350254479	0.533443824872761
86	0.311222016211226	0.622444032422452	0.688777983788774

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.281912121595811 & 0.563824243191622 & 0.71808787840419 \tabularnewline
20 & 0.18450624501164 & 0.36901249002328 & 0.81549375498836 \tabularnewline
21 & 0.9132219186573 & 0.173556162685399 & 0.0867780813426997 \tabularnewline
22 & 0.936220565501796 & 0.127558868996409 & 0.0637794344982045 \tabularnewline
23 & 0.906653814772452 & 0.186692370455096 & 0.0933461852275478 \tabularnewline
24 & 0.860546698720898 & 0.278906602558204 & 0.139453301279102 \tabularnewline
25 & 0.841262036971209 & 0.317475926057582 & 0.158737963028791 \tabularnewline
26 & 0.8093909114126 & 0.3812181771748 & 0.1906090885874 \tabularnewline
27 & 0.74109545406589 & 0.51780909186822 & 0.25890454593411 \tabularnewline
28 & 0.683409932501212 & 0.633180134997576 & 0.316590067498788 \tabularnewline
29 & 0.610487165534414 & 0.779025668931173 & 0.389512834465586 \tabularnewline
30 & 0.647512695327753 & 0.704974609344493 & 0.352487304672246 \tabularnewline
31 & 0.596562630194958 & 0.806874739610085 & 0.403437369805042 \tabularnewline
32 & 0.518677674644713 & 0.962644650710574 & 0.481322325355287 \tabularnewline
33 & 0.61662644631183 & 0.766747107376341 & 0.383373553688171 \tabularnewline
34 & 0.573743166103369 & 0.852513667793262 & 0.426256833896631 \tabularnewline
35 & 0.591186459533412 & 0.817627080933175 & 0.408813540466588 \tabularnewline
36 & 0.672702523555652 & 0.654594952888696 & 0.327297476444348 \tabularnewline
37 & 0.808790714569878 & 0.382418570860245 & 0.191209285430122 \tabularnewline
38 & 0.785094086263103 & 0.429811827473794 & 0.214905913736897 \tabularnewline
39 & 0.748816100835349 & 0.502367798329302 & 0.251183899164651 \tabularnewline
40 & 0.71022031164688 & 0.57955937670624 & 0.28977968835312 \tabularnewline
41 & 0.67579130411968 & 0.64841739176064 & 0.32420869588032 \tabularnewline
42 & 0.78906703145489 & 0.421865937090222 & 0.210932968545111 \tabularnewline
43 & 0.906549919956668 & 0.186900160086664 & 0.0934500800433318 \tabularnewline
44 & 0.977763306352532 & 0.0444733872949371 & 0.0222366936474685 \tabularnewline
45 & 0.968537957517603 & 0.0629240849647939 & 0.0314620424823969 \tabularnewline
46 & 0.957071701294215 & 0.0858565974115703 & 0.0429282987057852 \tabularnewline
47 & 0.959974955952985 & 0.0800500880940293 & 0.0400250440470147 \tabularnewline
48 & 0.947048858906828 & 0.105902282186344 & 0.052951141093172 \tabularnewline
49 & 0.92894337917981 & 0.142113241640380 & 0.0710566208201902 \tabularnewline
50 & 0.904772417437276 & 0.190455165125448 & 0.095227582562724 \tabularnewline
51 & 0.92632473600099 & 0.147350527998020 & 0.0736752639990101 \tabularnewline
52 & 0.906654996777704 & 0.186690006444592 & 0.0933450032222958 \tabularnewline
53 & 0.880191652925728 & 0.239616694148544 & 0.119808347074272 \tabularnewline
54 & 0.85051406395176 & 0.298971872096479 & 0.149485936048240 \tabularnewline
55 & 0.860382225049478 & 0.279235549901043 & 0.139617774950522 \tabularnewline
56 & 0.820527352207334 & 0.358945295585331 & 0.179472647792666 \tabularnewline
57 & 0.772724599008706 & 0.454550801982589 & 0.227275400991294 \tabularnewline
58 & 0.74529164920571 & 0.509416701588579 & 0.254708350794290 \tabularnewline
59 & 0.71564289548234 & 0.56871420903532 & 0.28435710451766 \tabularnewline
60 & 0.675933266627741 & 0.648133466744518 & 0.324066733372259 \tabularnewline
61 & 0.649097689772367 & 0.701804620455265 & 0.350902310227632 \tabularnewline
62 & 0.737764240989278 & 0.524471518021444 & 0.262235759010722 \tabularnewline
63 & 0.73092320337295 & 0.538153593254099 & 0.269076796627049 \tabularnewline
64 & 0.692225551125105 & 0.615548897749791 & 0.307774448874896 \tabularnewline
65 & 0.638098244514466 & 0.723803510971068 & 0.361901755485534 \tabularnewline
66 & 0.754558785199003 & 0.490882429601993 & 0.245441214800997 \tabularnewline
67 & 0.777125424470976 & 0.445749151058049 & 0.222874575529024 \tabularnewline
68 & 0.718650018957375 & 0.56269996208525 & 0.281349981042625 \tabularnewline
69 & 0.766957758849959 & 0.466084482300082 & 0.233042241150041 \tabularnewline
70 & 0.754312118504878 & 0.491375762990244 & 0.245687881495122 \tabularnewline
71 & 0.704767300863245 & 0.590465398273511 & 0.295232699136755 \tabularnewline
72 & 0.733531237544996 & 0.532937524910007 & 0.266468762455004 \tabularnewline
73 & 0.718168203006991 & 0.563663593986018 & 0.281831796993009 \tabularnewline
74 & 0.724858687457771 & 0.550282625084458 & 0.275141312542229 \tabularnewline
75 & 0.658960143174935 & 0.68207971365013 & 0.341039856825065 \tabularnewline
76 & 0.583644724111087 & 0.832710551777826 & 0.416355275888913 \tabularnewline
77 & 0.497235746448333 & 0.994471492896666 & 0.502764253551667 \tabularnewline
78 & 0.511643381420781 & 0.976713237158439 & 0.488356618579219 \tabularnewline
79 & 0.466190166954649 & 0.932380333909297 & 0.533809833045351 \tabularnewline
80 & 0.52571823404574 & 0.94856353190852 & 0.47428176595426 \tabularnewline
81 & 0.745240709302363 & 0.509518581395274 & 0.254759290697637 \tabularnewline
82 & 0.655194423367866 & 0.689611153264268 & 0.344805576632134 \tabularnewline
83 & 0.556357603076589 & 0.887284793846823 & 0.443642396923411 \tabularnewline
84 & 0.609691108035881 & 0.780617783928239 & 0.390308891964119 \tabularnewline
85 & 0.466556175127239 & 0.933112350254479 & 0.533443824872761 \tabularnewline
86 & 0.311222016211226 & 0.622444032422452 & 0.688777983788774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&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]0.281912121595811[/C][C]0.563824243191622[/C][C]0.71808787840419[/C][/ROW]
[ROW][C]20[/C][C]0.18450624501164[/C][C]0.36901249002328[/C][C]0.81549375498836[/C][/ROW]
[ROW][C]21[/C][C]0.9132219186573[/C][C]0.173556162685399[/C][C]0.0867780813426997[/C][/ROW]
[ROW][C]22[/C][C]0.936220565501796[/C][C]0.127558868996409[/C][C]0.0637794344982045[/C][/ROW]
[ROW][C]23[/C][C]0.906653814772452[/C][C]0.186692370455096[/C][C]0.0933461852275478[/C][/ROW]
[ROW][C]24[/C][C]0.860546698720898[/C][C]0.278906602558204[/C][C]0.139453301279102[/C][/ROW]
[ROW][C]25[/C][C]0.841262036971209[/C][C]0.317475926057582[/C][C]0.158737963028791[/C][/ROW]
[ROW][C]26[/C][C]0.8093909114126[/C][C]0.3812181771748[/C][C]0.1906090885874[/C][/ROW]
[ROW][C]27[/C][C]0.74109545406589[/C][C]0.51780909186822[/C][C]0.25890454593411[/C][/ROW]
[ROW][C]28[/C][C]0.683409932501212[/C][C]0.633180134997576[/C][C]0.316590067498788[/C][/ROW]
[ROW][C]29[/C][C]0.610487165534414[/C][C]0.779025668931173[/C][C]0.389512834465586[/C][/ROW]
[ROW][C]30[/C][C]0.647512695327753[/C][C]0.704974609344493[/C][C]0.352487304672246[/C][/ROW]
[ROW][C]31[/C][C]0.596562630194958[/C][C]0.806874739610085[/C][C]0.403437369805042[/C][/ROW]
[ROW][C]32[/C][C]0.518677674644713[/C][C]0.962644650710574[/C][C]0.481322325355287[/C][/ROW]
[ROW][C]33[/C][C]0.61662644631183[/C][C]0.766747107376341[/C][C]0.383373553688171[/C][/ROW]
[ROW][C]34[/C][C]0.573743166103369[/C][C]0.852513667793262[/C][C]0.426256833896631[/C][/ROW]
[ROW][C]35[/C][C]0.591186459533412[/C][C]0.817627080933175[/C][C]0.408813540466588[/C][/ROW]
[ROW][C]36[/C][C]0.672702523555652[/C][C]0.654594952888696[/C][C]0.327297476444348[/C][/ROW]
[ROW][C]37[/C][C]0.808790714569878[/C][C]0.382418570860245[/C][C]0.191209285430122[/C][/ROW]
[ROW][C]38[/C][C]0.785094086263103[/C][C]0.429811827473794[/C][C]0.214905913736897[/C][/ROW]
[ROW][C]39[/C][C]0.748816100835349[/C][C]0.502367798329302[/C][C]0.251183899164651[/C][/ROW]
[ROW][C]40[/C][C]0.71022031164688[/C][C]0.57955937670624[/C][C]0.28977968835312[/C][/ROW]
[ROW][C]41[/C][C]0.67579130411968[/C][C]0.64841739176064[/C][C]0.32420869588032[/C][/ROW]
[ROW][C]42[/C][C]0.78906703145489[/C][C]0.421865937090222[/C][C]0.210932968545111[/C][/ROW]
[ROW][C]43[/C][C]0.906549919956668[/C][C]0.186900160086664[/C][C]0.0934500800433318[/C][/ROW]
[ROW][C]44[/C][C]0.977763306352532[/C][C]0.0444733872949371[/C][C]0.0222366936474685[/C][/ROW]
[ROW][C]45[/C][C]0.968537957517603[/C][C]0.0629240849647939[/C][C]0.0314620424823969[/C][/ROW]
[ROW][C]46[/C][C]0.957071701294215[/C][C]0.0858565974115703[/C][C]0.0429282987057852[/C][/ROW]
[ROW][C]47[/C][C]0.959974955952985[/C][C]0.0800500880940293[/C][C]0.0400250440470147[/C][/ROW]
[ROW][C]48[/C][C]0.947048858906828[/C][C]0.105902282186344[/C][C]0.052951141093172[/C][/ROW]
[ROW][C]49[/C][C]0.92894337917981[/C][C]0.142113241640380[/C][C]0.0710566208201902[/C][/ROW]
[ROW][C]50[/C][C]0.904772417437276[/C][C]0.190455165125448[/C][C]0.095227582562724[/C][/ROW]
[ROW][C]51[/C][C]0.92632473600099[/C][C]0.147350527998020[/C][C]0.0736752639990101[/C][/ROW]
[ROW][C]52[/C][C]0.906654996777704[/C][C]0.186690006444592[/C][C]0.0933450032222958[/C][/ROW]
[ROW][C]53[/C][C]0.880191652925728[/C][C]0.239616694148544[/C][C]0.119808347074272[/C][/ROW]
[ROW][C]54[/C][C]0.85051406395176[/C][C]0.298971872096479[/C][C]0.149485936048240[/C][/ROW]
[ROW][C]55[/C][C]0.860382225049478[/C][C]0.279235549901043[/C][C]0.139617774950522[/C][/ROW]
[ROW][C]56[/C][C]0.820527352207334[/C][C]0.358945295585331[/C][C]0.179472647792666[/C][/ROW]
[ROW][C]57[/C][C]0.772724599008706[/C][C]0.454550801982589[/C][C]0.227275400991294[/C][/ROW]
[ROW][C]58[/C][C]0.74529164920571[/C][C]0.509416701588579[/C][C]0.254708350794290[/C][/ROW]
[ROW][C]59[/C][C]0.71564289548234[/C][C]0.56871420903532[/C][C]0.28435710451766[/C][/ROW]
[ROW][C]60[/C][C]0.675933266627741[/C][C]0.648133466744518[/C][C]0.324066733372259[/C][/ROW]
[ROW][C]61[/C][C]0.649097689772367[/C][C]0.701804620455265[/C][C]0.350902310227632[/C][/ROW]
[ROW][C]62[/C][C]0.737764240989278[/C][C]0.524471518021444[/C][C]0.262235759010722[/C][/ROW]
[ROW][C]63[/C][C]0.73092320337295[/C][C]0.538153593254099[/C][C]0.269076796627049[/C][/ROW]
[ROW][C]64[/C][C]0.692225551125105[/C][C]0.615548897749791[/C][C]0.307774448874896[/C][/ROW]
[ROW][C]65[/C][C]0.638098244514466[/C][C]0.723803510971068[/C][C]0.361901755485534[/C][/ROW]
[ROW][C]66[/C][C]0.754558785199003[/C][C]0.490882429601993[/C][C]0.245441214800997[/C][/ROW]
[ROW][C]67[/C][C]0.777125424470976[/C][C]0.445749151058049[/C][C]0.222874575529024[/C][/ROW]
[ROW][C]68[/C][C]0.718650018957375[/C][C]0.56269996208525[/C][C]0.281349981042625[/C][/ROW]
[ROW][C]69[/C][C]0.766957758849959[/C][C]0.466084482300082[/C][C]0.233042241150041[/C][/ROW]
[ROW][C]70[/C][C]0.754312118504878[/C][C]0.491375762990244[/C][C]0.245687881495122[/C][/ROW]
[ROW][C]71[/C][C]0.704767300863245[/C][C]0.590465398273511[/C][C]0.295232699136755[/C][/ROW]
[ROW][C]72[/C][C]0.733531237544996[/C][C]0.532937524910007[/C][C]0.266468762455004[/C][/ROW]
[ROW][C]73[/C][C]0.718168203006991[/C][C]0.563663593986018[/C][C]0.281831796993009[/C][/ROW]
[ROW][C]74[/C][C]0.724858687457771[/C][C]0.550282625084458[/C][C]0.275141312542229[/C][/ROW]
[ROW][C]75[/C][C]0.658960143174935[/C][C]0.68207971365013[/C][C]0.341039856825065[/C][/ROW]
[ROW][C]76[/C][C]0.583644724111087[/C][C]0.832710551777826[/C][C]0.416355275888913[/C][/ROW]
[ROW][C]77[/C][C]0.497235746448333[/C][C]0.994471492896666[/C][C]0.502764253551667[/C][/ROW]
[ROW][C]78[/C][C]0.511643381420781[/C][C]0.976713237158439[/C][C]0.488356618579219[/C][/ROW]
[ROW][C]79[/C][C]0.466190166954649[/C][C]0.932380333909297[/C][C]0.533809833045351[/C][/ROW]
[ROW][C]80[/C][C]0.52571823404574[/C][C]0.94856353190852[/C][C]0.47428176595426[/C][/ROW]
[ROW][C]81[/C][C]0.745240709302363[/C][C]0.509518581395274[/C][C]0.254759290697637[/C][/ROW]
[ROW][C]82[/C][C]0.655194423367866[/C][C]0.689611153264268[/C][C]0.344805576632134[/C][/ROW]
[ROW][C]83[/C][C]0.556357603076589[/C][C]0.887284793846823[/C][C]0.443642396923411[/C][/ROW]
[ROW][C]84[/C][C]0.609691108035881[/C][C]0.780617783928239[/C][C]0.390308891964119[/C][/ROW]
[ROW][C]85[/C][C]0.466556175127239[/C][C]0.933112350254479[/C][C]0.533443824872761[/C][/ROW]
[ROW][C]86[/C][C]0.311222016211226[/C][C]0.622444032422452[/C][C]0.688777983788774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.281912121595811	0.563824243191622	0.71808787840419
20	0.18450624501164	0.36901249002328	0.81549375498836
21	0.9132219186573	0.173556162685399	0.0867780813426997
22	0.936220565501796	0.127558868996409	0.0637794344982045
23	0.906653814772452	0.186692370455096	0.0933461852275478
24	0.860546698720898	0.278906602558204	0.139453301279102
25	0.841262036971209	0.317475926057582	0.158737963028791
26	0.8093909114126	0.3812181771748	0.1906090885874
27	0.74109545406589	0.51780909186822	0.25890454593411
28	0.683409932501212	0.633180134997576	0.316590067498788
29	0.610487165534414	0.779025668931173	0.389512834465586
30	0.647512695327753	0.704974609344493	0.352487304672246
31	0.596562630194958	0.806874739610085	0.403437369805042
32	0.518677674644713	0.962644650710574	0.481322325355287
33	0.61662644631183	0.766747107376341	0.383373553688171
34	0.573743166103369	0.852513667793262	0.426256833896631
35	0.591186459533412	0.817627080933175	0.408813540466588
36	0.672702523555652	0.654594952888696	0.327297476444348
37	0.808790714569878	0.382418570860245	0.191209285430122
38	0.785094086263103	0.429811827473794	0.214905913736897
39	0.748816100835349	0.502367798329302	0.251183899164651
40	0.71022031164688	0.57955937670624	0.28977968835312
41	0.67579130411968	0.64841739176064	0.32420869588032
42	0.78906703145489	0.421865937090222	0.210932968545111
43	0.906549919956668	0.186900160086664	0.0934500800433318
44	0.977763306352532	0.0444733872949371	0.0222366936474685
45	0.968537957517603	0.0629240849647939	0.0314620424823969
46	0.957071701294215	0.0858565974115703	0.0429282987057852
47	0.959974955952985	0.0800500880940293	0.0400250440470147
48	0.947048858906828	0.105902282186344	0.052951141093172
49	0.92894337917981	0.142113241640380	0.0710566208201902
50	0.904772417437276	0.190455165125448	0.095227582562724
51	0.92632473600099	0.147350527998020	0.0736752639990101
52	0.906654996777704	0.186690006444592	0.0933450032222958
53	0.880191652925728	0.239616694148544	0.119808347074272
54	0.85051406395176	0.298971872096479	0.149485936048240
55	0.860382225049478	0.279235549901043	0.139617774950522
56	0.820527352207334	0.358945295585331	0.179472647792666
57	0.772724599008706	0.454550801982589	0.227275400991294
58	0.74529164920571	0.509416701588579	0.254708350794290
59	0.71564289548234	0.56871420903532	0.28435710451766
60	0.675933266627741	0.648133466744518	0.324066733372259
61	0.649097689772367	0.701804620455265	0.350902310227632
62	0.737764240989278	0.524471518021444	0.262235759010722
63	0.73092320337295	0.538153593254099	0.269076796627049
64	0.692225551125105	0.615548897749791	0.307774448874896
65	0.638098244514466	0.723803510971068	0.361901755485534
66	0.754558785199003	0.490882429601993	0.245441214800997
67	0.777125424470976	0.445749151058049	0.222874575529024
68	0.718650018957375	0.56269996208525	0.281349981042625
69	0.766957758849959	0.466084482300082	0.233042241150041
70	0.754312118504878	0.491375762990244	0.245687881495122
71	0.704767300863245	0.590465398273511	0.295232699136755
72	0.733531237544996	0.532937524910007	0.266468762455004
73	0.718168203006991	0.563663593986018	0.281831796993009
74	0.724858687457771	0.550282625084458	0.275141312542229
75	0.658960143174935	0.68207971365013	0.341039856825065
76	0.583644724111087	0.832710551777826	0.416355275888913
77	0.497235746448333	0.994471492896666	0.502764253551667
78	0.511643381420781	0.976713237158439	0.488356618579219
79	0.466190166954649	0.932380333909297	0.533809833045351
80	0.52571823404574	0.94856353190852	0.47428176595426
81	0.745240709302363	0.509518581395274	0.254759290697637
82	0.655194423367866	0.689611153264268	0.344805576632134
83	0.556357603076589	0.887284793846823	0.443642396923411
84	0.609691108035881	0.780617783928239	0.390308891964119
85	0.466556175127239	0.933112350254479	0.533443824872761
86	0.311222016211226	0.622444032422452	0.688777983788774

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0147058823529412	OK
10% type I error level	4	0.0588235294117647	OK

\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 & 0.0147058823529412 & OK \tabularnewline
10% type I error level & 4 & 0.0588235294117647 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57359&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]0.0147058823529412[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0588235294117647[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57359&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57359&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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0147058823529412	OK
10% type I error level	4	0.0588235294117647	OK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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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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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code