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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 31 Jan 2019 14:57:39 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jan/31/t1548943069wlgxxjyudl7bvhf.htm/, Retrieved Sun, 05 May 2024 08:44:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=317798, Retrieved Sun, 05 May 2024 08:44:46 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2019-01-31 13:57:39] [9f050f8aed6d13342aa7dd8a5b9f6dd8] [Current]

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1 0.5 0.67 0.67 0 2011 1 0
0.89 0.5 0.83 0.33 0.5 2011 1 1
0.89 0.4 1 0.67 0 2011 1 0
0.89 0.5 0.83 0 0 2011 1 1
0.89 0.7 0.67 0 1 2011 1 1
0.78 0.3 0 0 0.5 2011 1 1
0.89 0.4 0.83 0.67 0.5 2011 1 0
1 0.4 0.5 0.67 1 2011 1 1
0.89 0.7 0.83 0 0.5 2011 1 1
0.78 0.6 0.33 0.67 0.5 2011 1 1
1 0.6 0.5 1 0 2011 1 1
0.78 0.2 0.67 0 0.5 2011 1 1
0.89 0.4 1 0 0.5 2011 1 1
0.89 0.4 0.5 0.67 0 2011 1 0
0.89 0.5 0.67 0.33 0 2011 1 0
0.89 0.3 0.17 0.67 0 2011 0 0
0.89 0.4 0.83 0.33 0.5 2011 1 1
0.67 0.7 0.67 0.33 0.5 2011 1 0
1 0.5 0.67 0.33 0 2011 0 1
0.78 0.2 0.67 0 0 2011 1 0
0.78 0.3 0.5 0.67 0 2011 1 1
0.89 0.6 1 0.33 0 2011 1 1
0.78 0.6 0.83 0.33 0 2011 1 0
0.89 0.2 0.83 0.33 0 2011 1 1
0.89 0.7 1 0.67 1 2011 1 1
0.33 0.2 0.67 0 0 2011 1 1
1 1 1 0.33 1 2011 0 1
0.89 0.4 0.83 0.67 0 2011 0 0
0.89 0.4 1 1 0 2011 1 1
0.67 0.2 0.83 0.67 0 2011 1 0
0.56 0.4 0.67 0.33 0 2011 1 0
0.89 0.4 0.67 0 0.5 2011 1 0
0.89 0.7 1 0.67 0.5 2011 1 1
1 0.2 0.67 0.67 0 2011 1 1
0.78 0.6 1 1 0 2011 1 1
0.78 0.3 1 1 0.5 2011 1 1
0.33 0.3 0.5 0.33 0 2011 1 0
0.78 0.2 0.67 0 0.5 2011 0 1
0.89 0.5 0.83 0.67 0.5 2011 1 1
0.89 0.7 1 0.67 0.5 2011 1 0
0.78 0.6 1 0.67 0.5 2011 1 1
0.89 0.4 1 0.67 0.5 2011 1 1
0.89 0.6 1 0.33 0.5 2011 1 1
1 0.4 1 1 0 2011 1 1
0.67 0.3 0.83 0.67 0 2011 0 1
1 0.5 0.83 0.67 0.5 2011 1 0
0.89 0.2 0.5 0 0 2011 1 1
0.89 0.3 0.83 0 0.5 2011 0 1
0.89 0.5 0.17 0 0 2011 1 0
0.78 0.7 0.83 1 0.5 2011 0 1
0.89 0.4 1 0.67 1 2011 0 1
0.78 0.3 1 0 0 2011 0 1
0.78 0.2 0.67 0.67 1 2011 1 1
1 0.5 1 0 0 2011 0 1
0.78 0.4 1 0 0.5 2011 1 1
1 0.6 1 0.67 1 2011 1 1
0.78 0.4 0.83 1 0 2011 0 0
0.67 0.4 0.33 0 0 2011 1 1
0.33 0.2 0.33 0.33 0 2011 0 0
1 0.9 1 0.67 0.5 2011 0 1
1 0.8 1 0.67 1 2011 1 1
0.78 0.8 0.83 0 0.5 2011 1 0
0.67 0.3 1 1 0.5 2011 1 0
1 0.2 0.83 0.67 0 2011 1 1
0.89 0.4 0.67 0 0.5 2011 1 0
0.89 0.2 0.83 1 0 2011 0 1
0.78 0.2 0.67 0.67 0.5 2011 1 0
1 0.1 0.83 0.67 0 2011 1 1
0.56 0.4 0.67 1 0.5 2011 1 1
0.67 0.5 1 0 0.5 2011 0 0
0.89 0.8 0.83 0.33 0.5 2011 1 0
0.89 0.4 0.67 0.67 0 2011 1 0
0.89 0.6 0.83 0.33 0.5 2011 0 0
0.89 0.5 0.83 0.67 0.5 2011 0 1
0.78 0.3 0.67 0 0 2011 1 0
1 0.4 0.33 0 0.5 2011 1 0
1 0.6 0.83 0.67 0.5 2011 0 1
0.89 0.4 1 0.33 0 2011 1 0
0.44 0.3 0.83 0 0 2011 0 0
0.78 0.8 0.83 0 1 2011 1 1
0.89 0.6 0.5 0.33 1 2011 0 0
0.67 0.3 0.5 0 0 2011 0 0
0.78 0.5 0.83 0.67 0.5 2011 0 0
0.78 0.4 1 0.33 0 2011 0 1
0.33 0.3 0.33 0.67 0 2011 0 0
0.89 0.7 1 0.33 0 2011 0 1
0.89 0.2 0.67 0.33 0.5 2011 0 1
0.89 0.4 0.83 1 0 2011 0 0
0.89 0.6 1 0.67 0.5 2011 0 0
0.56 0.6 0.83 0 0 2011 0 0
0.67 0.6 0.83 0.67 0.5 2011 0 1
0.67 0.4 1 0.33 0.5 2011 0 1
0.78 0.6 0.83 0 0 2011 0 1
0.78 0.5 1 0.33 0.5 2011 0 0
0.78 0.5 0.83 0 0 2011 0 1
0.89 0.6 0.67 0 0 2011 0 0
1 0.8 0.83 0.33 0.5 2011 0 1
0.89 0.5 0.83 0.67 1 2011 0 1
0.89 0.6 0.83 0.67 0.5 2011 0 1
0.78 0.4 0.83 0.67 0.5 2011 0 1
1 0.3 0.67 0.67 0.5 2011 0 1
0.78 0.3 0.83 1 0 2011 0 0
0.67 0.2 0 0 0 2011 0 1
0.78 0.4 0.83 0 0 2011 0 1
0.89 0.5 1 0 0 2011 0 1
0.67 0.3 0.17 0 0.5 2011 0 0
0.22 0.4 0.17 0 0.5 2011 0 0
0.44 0.5 0.5 1 0 2011 0 0
0.89 0.3 0.5 0.67 0 2011 0 0
0.67 0.5 1 0 0 2011 0 0
0.89 0.4 0.67 0.67 0 2011 0 0
0.67 0.4 0.83 0.67 0 2011 0 1
0.78 0.6 1 0 1 2012 1 1
0.78 0.3 1 0.67 1 2012 1 1
0.78 0.4 1 0.33 1 2012 1 1
1 0.3 1 1 1 2012 1 1
0.78 1 1 1 1 2012 0 0
0.67 0.4 1 0 0 2012 0 1
0.89 0.8 0.83 1 0.5 2012 1 1
0.89 0.3 1 0.67 1 2012 1 0
1 0.5 0.83 0.67 0 2012 1 1
0.78 0.4 1 0 0 2012 1 0
0.67 0.3 0.83 0.67 0 2012 1 0
0.89 0.5 0.83 1 0 2012 1 1
0.67 0.3 1 0.67 0 2012 1 1
0.67 0.3 0.67 0 0 2012 1 1
1 0.4 0.83 0 0 2012 1 1
0.67 0.3 1 0 0 2012 1 1
1 0.6 1 0.33 0.5 2012 0 1
0.89 0.6 0.83 0.67 1 2012 1 1
0.89 0.4 1 1 1 2012 1 1
1 0.4 1 0 0 2012 1 0
0.67 0.4 1 0.67 0 2012 1 1
0.44 0.3 0.67 0.67 0.5 2012 1 1
0.89 0.2 1 0.33 1 2012 1 1
0.56 0.5 0.83 0.67 0 2012 1 1
0.78 0.4 1 0.67 1 2012 1 1
1 0.4 1 0.67 0 2012 1 1
1 0.4 0.83 0.67 0 2012 1 0
0.89 0.3 0.67 0.67 0.5 2012 1 1
0.67 0.4 0.83 0.67 1 2012 0 1
0.89 0.2 1 0.33 0.5 2012 1 1
0.33 0 0 0 0 2012 1 1
0.89 0.4 1 0.67 0.5 2012 1 1
0.78 0.6 1 0 1 2012 1 0
1 0.4 0.67 0.67 0 2012 1 0
0.44 0.4 1 0 0 2012 1 1
0.67 0.4 0.83 0 0.5 2012 1 0
0.33 0.2 0.17 0 0.5 2012 1 0
0.89 0.4 0.83 1 1 2012 1 1
0.89 0.3 0.83 0 0 2012 1 0
1 0.6 0.83 0.67 1 2012 1 1
0.89 0.6 0.83 1 0 2012 1 1
0.89 0.4 0.83 0 0 2012 1 0
1 0.5 1 0.67 1 2012 0 1
0.89 0.4 0.83 0 0.5 2012 0 1
1 0.6 1 1 1 2012 1 1
0.78 0.6 0.83 0.67 0.5 2012 1 0
0.78 0.9 1 0.67 0.5 2012 0 0
0.67 0.4 0.83 0.67 0.5 2012 0 0
0.89 0.8 1 1 0.5 2012 0 0
0.67 0.5 0.83 1 0 2012 0 1
0.78 0.4 0.83 1 0 2012 1 0
0.89 0.4 1 0.67 1 2012 1 1
0.89 0.7 1 1 1 2012 0 0
0.78 0.4 1 0.33 1 2012 0 1
1 0.8 1 0.67 0.5 2012 0 0
1 0.4 1 1 1 2012 0 1
1 0.3 1 0.67 0 2012 0 0
0.67 0.5 1 0.67 0.5 2012 0 0
0.89 0.8 1 0.67 1 2012 0 1
1 0.4 0.83 0.33 0 2012 0 0
1 1 1 1 0.5 2012 0 1
0.89 0.5 1 0.67 1 2012 1 1
0.89 0.5 1 0.67 1 2012 1 1
0.89 0.3 1 0.33 0 2012 0 1
0.89 0.3 0.83 0.33 0.5 2012 1 1
0.89 0.3 0.5 0 0 2012 1 0
1 0.4 0.67 0.33 0.5 2012 0 1
0.67 0.5 1 0.33 0 2012 1 0
1 0.5 0.67 0.67 0.5 2012 0 0
0.89 0.4 1 0 0 2012 0 0
0.89 0.7 1 1 0.5 2012 1 1
0.89 0.5 0.5 0.33 0 2012 0 0
0.89 0.4 0.67 0.33 1 2012 0 1
1 0.7 0.67 1 0 2012 0 0
1 0.7 0.67 1 0 2012 0 0
1 0.7 0.67 1 0 2012 0 0
0.89 0.7 0.67 1 0 2012 0 0
0.89 0.7 0.67 0 0 2012 0 1
0.89 0.7 1 0.67 0.5 2012 0 1
0.33 0.1 0.67 0.33 0.5 2012 0 1
0.67 0.2 0.67 0.67 0.5 2012 0 1
0.56 0.3 0.33 0.33 0 2012 0 0
0.44 0.6 0.83 0.33 0 2012 0 0
1 0.8 1 1 1 2012 0 1
0.89 0.8 1 0.33 0.5 2012 1 0
0.33 0 0.17 0 0 2012 0 0
0.67 0.3 0.67 0.33 0 2012 0 0
0.67 0.6 0.83 0.33 0.5 2012 0 0
1 0.5 0.83 0.67 0 2012 1 1
0.78 0.7 1 0.33 0 2012 0 1
0.67 0.3 0.83 0 0.5 2012 1 0
1 0.3 1 0.67 0 2012 0 0
0.78 0.4 1 0.67 0 2012 1 1
0.89 0.4 0.83 1 0 2012 0 0
0.89 0.1 0.83 0 0 2012 1 1
0.89 0.5 1 0.67 0 2012 0 1
0 0 0 0 0 2012 0 0
0.67 0.4 1 0.33 0.5 2012 0 1
1 0.6 0.83 0.67 1 2012 0 1
1 0.4 1 0.33 0.5 2012 1 1
0.67 0.1 0.33 0 0.5 2012 1 0
0.89 0.3 0.83 0 0 2012 0 0
0.89 0.7 0.83 0.67 0 2012 1 0
0.56 0.3 0.17 0 0 2012 1 1
0.67 0.5 0.83 0.33 0.5 2012 0 1
1 0.3 0.83 0.67 1 2012 1 1
1 0.6 0.67 0.67 0.5 2012 0 1
1 0.9 1 1 0 2012 0 0
0.67 0.4 0.83 0 0.5 2012 1 0
0.44 0.3 1 0 0.5 2012 0 0
0.89 0.9 1 0.67 1 2012 1 1
0.44 0.5 1 0 0.5 2012 1 1
0.56 0.3 1 1 0.5 2012 1 0
0.89 0.6 0.83 0.67 0 2012 0 0
0.67 0.2 1 0.33 0 2012 1 0
0.89 0.4 0.83 1 0.5 2012 0 1
1 0.5 0.83 0.67 0.5 2012 1 1
0.78 0.4 0.83 0.67 0 2012 1 0
0.44 0 0 0 0 2012 1 0
0.89 0.2 1 0.33 0.5 2012 1 0
0.89 0.5 1 0.67 0.5 2012 0 1
0.89 0.3 1 0.67 0 2012 1 1
0.44 0 0 0 0 2012 1 1
1 0.5 0.83 1 0 2012 1 1
0.89 0.6 0.83 0.33 0 2012 0 0
0.67 0.3 0.83 0 0.5 2012 1 1
0.33 0 0 0 0 2012 1 1
0.78 0.3 0.67 0 0.5 2012 0 1
0.89 0.5 1 0.67 0.5 2012 1 0
0.78 0.4 0.67 0 0 2012 0 0
0.78 0.5 0.83 0.67 0 2012 0 1
0.89 0.7 1 1 1 2012 0 0
0.78 0.8 1 0.67 0.5 2012 0 1
0.78 0.6 1 0.33 0.5 2012 0 1
0.67 0.4 0.83 0.33 0 2012 1 0
0.89 0.5 0.83 0.33 0.5 2012 1 1
0.89 0.5 1 0 0.5 2012 1 0
0.78 0.3 1 0.33 0 2012 1 1
1 0.6 1 0 0.5 2012 1 1
1 0.3 0.67 0.67 0 2012 1 0
0.78 0.6 0.83 1 0.5 2012 0 1
0.78 0.3 0.33 0.33 0 2012 0 0
0.89 0.7 1 0.67 1 2012 1 1
0.89 0.7 1 1 0 2012 0 0
0.67 0.6 0.67 1 0.5 2012 0 1
1 0.5 1 0.33 0.5 2012 1 0
0.67 0.5 0.83 0.33 0 2012 0 0
0.56 0.4 0.67 0 0 2012 0 1
0.78 0.4 1 0.33 1 2012 0 1
1 0.7 1 1 0 2012 0 0
0.67 0.2 0.17 0 0.5 2012 0 1
0.78 0.5 0.83 0.67 0 2012 0 1
0.56 0.4 0.83 0.67 0.5 2012 0 0
1 0.2 1 0.67 1 2012 0 1
0.89 0.5 0.67 0.67 0 2012 0 0
0.44 0.4 0.5 0 0 2012 0 0
1 0.7 0.67 1 1 2012 0 1
0.89 0.6 0.83 0.67 1 2012 0 1
0.78 0.4 0.83 0 0 2012 0 0
0.89 0.5 1 0.67 1 2012 0 1
0.11 0 0.17 0 0 2012 0 0
0.89 0.7 1 0.67 0.5 2012 0 0
0.89 0.4 0.67 0.67 0 2012 0 1
1 0.5 0.67 1 0 2012 1 0
0.89 0.6 0.83 0.67 0 2012 0 0
1 0.8 0.5 0.67 0.5 2012 0 1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time13 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

13 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=317798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&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	13 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Calculation[t] = + 55.3143 + 0.248321Algebraic_Reasoning[t] + 0.193203Graphical_Interpretation[t] + 0.102884Proportionality_and_Ratio[t] + 0.00225489Probability_and_Sampling[t] -0.0272716year[t] + 0.0383029group[t] + 0.031862gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Calculation[t] =  +  55.3143 +  0.248321Algebraic_Reasoning[t] +  0.193203Graphical_Interpretation[t] +  0.102884Proportionality_and_Ratio[t] +  0.00225489Probability_and_Sampling[t] -0.0272716year[t] +  0.0383029group[t] +  0.031862gender[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Calculation[t] =  +  55.3143 +  0.248321Algebraic_Reasoning[t] +  0.193203Graphical_Interpretation[t] +  0.102884Proportionality_and_Ratio[t] +  0.00225489Probability_and_Sampling[t] -0.0272716year[t] +  0.0383029group[t] +  0.031862gender[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&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
Calculation[t] = + 55.3143 + 0.248321Algebraic_Reasoning[t] + 0.193203Graphical_Interpretation[t] + 0.102884Proportionality_and_Ratio[t] + 0.00225489Probability_and_Sampling[t] -0.0272716year[t] + 0.0383029group[t] + 0.031862gender[t] + 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)	+55.31	37.66	+1.4690e+00	0.1431	0.07153
Algebraic_Reasoning	+0.2483	0.05584	+4.4470e+00	1.274e-05	6.369e-06
Graphical_Interpretation	+0.1932	0.04363	+4.4280e+00	1.382e-05	6.908e-06
Proportionality_and_Ratio	+0.1029	0.02765	+3.7200e+00	0.000242	0.000121
Probability_and_Sampling	+0.002255	0.02721	+8.2880e-02	0.934	0.467
year	-0.02727	0.01872	-1.4570e+00	0.1464	0.07319
group	+0.0383	0.01859	+2.0600e+00	0.04032	0.02016
gender	+0.03186	0.01939	+1.6430e+00	0.1016	0.0508

\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) & +55.31 &  37.66 & +1.4690e+00 &  0.1431 &  0.07153 \tabularnewline
Algebraic_Reasoning & +0.2483 &  0.05584 & +4.4470e+00 &  1.274e-05 &  6.369e-06 \tabularnewline
Graphical_Interpretation & +0.1932 &  0.04363 & +4.4280e+00 &  1.382e-05 &  6.908e-06 \tabularnewline
Proportionality_and_Ratio & +0.1029 &  0.02765 & +3.7200e+00 &  0.000242 &  0.000121 \tabularnewline
Probability_and_Sampling & +0.002255 &  0.02721 & +8.2880e-02 &  0.934 &  0.467 \tabularnewline
year & -0.02727 &  0.01872 & -1.4570e+00 &  0.1464 &  0.07319 \tabularnewline
group & +0.0383 &  0.01859 & +2.0600e+00 &  0.04032 &  0.02016 \tabularnewline
gender & +0.03186 &  0.01939 & +1.6430e+00 &  0.1016 &  0.0508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&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]+55.31[/C][C] 37.66[/C][C]+1.4690e+00[/C][C] 0.1431[/C][C] 0.07153[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]+0.2483[/C][C] 0.05584[/C][C]+4.4470e+00[/C][C] 1.274e-05[/C][C] 6.369e-06[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]+0.1932[/C][C] 0.04363[/C][C]+4.4280e+00[/C][C] 1.382e-05[/C][C] 6.908e-06[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]+0.1029[/C][C] 0.02765[/C][C]+3.7200e+00[/C][C] 0.000242[/C][C] 0.000121[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]+0.002255[/C][C] 0.02721[/C][C]+8.2880e-02[/C][C] 0.934[/C][C] 0.467[/C][/ROW]
[ROW][C]year[/C][C]-0.02727[/C][C] 0.01872[/C][C]-1.4570e+00[/C][C] 0.1464[/C][C] 0.07319[/C][/ROW]
[ROW][C]group[/C][C]+0.0383[/C][C] 0.01859[/C][C]+2.0600e+00[/C][C] 0.04032[/C][C] 0.02016[/C][/ROW]
[ROW][C]gender[/C][C]+0.03186[/C][C] 0.01939[/C][C]+1.6430e+00[/C][C] 0.1016[/C][C] 0.0508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317798&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&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)	+55.31	37.66	+1.4690e+00	0.1431	0.07153
Algebraic_Reasoning	+0.2483	0.05584	+4.4470e+00	1.274e-05	6.369e-06
Graphical_Interpretation	+0.1932	0.04363	+4.4280e+00	1.382e-05	6.908e-06
Proportionality_and_Ratio	+0.1029	0.02765	+3.7200e+00	0.000242	0.000121
Probability_and_Sampling	+0.002255	0.02721	+8.2880e-02	0.934	0.467
year	-0.02727	0.01872	-1.4570e+00	0.1464	0.07319
group	+0.0383	0.01859	+2.0600e+00	0.04032	0.02016
gender	+0.03186	0.01939	+1.6430e+00	0.1016	0.0508

Multiple Linear Regression - Regression Statistics
Multiple R	0.5682
R-squared	0.3228
Adjusted R-squared	0.3052
F-TEST (value)	18.39
F-TEST (DF numerator)	7
F-TEST (DF denominator)	270
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1502
Sum Squared Residuals	6.095

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5682 \tabularnewline
R-squared &  0.3228 \tabularnewline
Adjusted R-squared &  0.3052 \tabularnewline
F-TEST (value) &  18.39 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 270 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.1502 \tabularnewline
Sum Squared Residuals &  6.095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5682[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3228[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3052[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 18.39[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]270[/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] 0.1502[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 6.095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317798&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&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	0.5682
R-squared	0.3228
Adjusted R-squared	0.3052
F-TEST (value)	18.39
F-TEST (DF numerator)	7
F-TEST (DF denominator)	270
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1502
Sum Squared Residuals	6.095

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317798&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 15.84, df1 = 2, df2 = 268, p-value = 3.147e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6699, df1 = 14, df2 = 256, p-value = 0.06209
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7475, df1 = 2, df2 = 268, p-value = 0.1762

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 15.84, df1 = 2, df2 = 268, p-value = 3.147e-07
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6699, df1 = 14, df2 = 256, p-value = 0.06209
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7475, df1 = 2, df2 = 268, p-value = 0.1762
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 15.84, df1 = 2, df2 = 268, p-value = 3.147e-07
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6699, df1 = 14, df2 = 256, p-value = 0.06209
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7475, df1 = 2, df2 = 268, p-value = 0.1762
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317798&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&T=5

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 15.84, df1 = 2, df2 = 268, p-value = 3.147e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.6699, df1 = 14, df2 = 256, p-value = 0.06209
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7475, df1 = 2, df2 = 268, p-value = 0.1762

Variance Inflation Factors (Multicollinearity)
> vif Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio 1.424253 1.357782 1.171012 Probability_and_Sampling year group 1.241136 1.038467 1.063476 gender 1.136489

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      Algebraic_Reasoning  Graphical_Interpretation Proportionality_and_Ratio 
                 1.424253                  1.357782                  1.171012 
 Probability_and_Sampling                      year                     group 
                 1.241136                  1.038467                  1.063476 
                   gender 
                 1.136489 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317798&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      Algebraic_Reasoning  Graphical_Interpretation Proportionality_and_Ratio 
                 1.424253                  1.357782                  1.171012 
 Probability_and_Sampling                      year                     group 
                 1.241136                  1.038467                  1.063476 
                   gender 
                 1.136489 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317798&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317798&T=6

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio 1.424253 1.357782 1.171012 Probability_and_Sampling year group 1.241136 1.038467 1.063476 gender 1.136489

Figure 1

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Figure 3

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Figure 4

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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 = 2 ; par3 = 3 ; par4 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code