CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject he

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/Correla...efficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)


Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78. The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero. These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years. It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes. It will quickly separate the
wheat from the chaff.


-.-. --.- Roger Coppock

=-=-=-=-=-=-= The Data =-=-=-=-=-=-=
The global mean surface "Temp"erature data are the GISS
adjusted J-D yearly land and sea average, available from
NASA at:

http://data.giss.nasa.gov/gistemp/ta...LB.Ts+dSST.txt


The "CO2" data are the yearly averages of the monthly data
from the Keeling curve measured at Mauna Loa, available at:

ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt


"Sunspots" are the yearly averages of the monthly means
in the NOAA NGDC "MONTHLY" file. They are available at
the FTP site accessed through this web page:

http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html


Year Temp CO2 Sunspots
1958 14.08 315.33 184.5917
1959 14.06 315.98 158.75
1960 13.99 316.91 112.275
1961 14.08 317.65 53.8833
1962 14.04 318.46 37.6
1963 14.08 318.99 27.8917
1964 13.79 319.20 10.2
1965 13.89 320.03 15.0583
1966 13.97 321.37 46.875
1967 14.00 322.18 93.6667
1968 13.96 323.05 105.8917
1969 14.08 324.62 105.5583
1970 14.03 325.68 104.6917
1971 13.90 326.32 66.65
1972 14.00 327.46 68.9333
1973 14.14 329.68 38.15
1974 13.92 330.17 34.4083
1975 13.95 331.14 15.4583
1976 13.84 332.06 12.55
1977 14.13 333.78 27.4833
1978 14.02 335.40 92.6583
1979 14.09 336.78 155.275
1980 14.18 338.70 154.65
1981 14.27 340.11 140.45
1982 14.05 340.98 116.2917
1983 14.26 342.84 66.6333
1984 14.09 344.20 45.85
1985 14.06 345.87 17.9417
1986 14.13 347.19 13.4
1987 14.27 348.98 29.225
1988 14.31 351.45 100
1989 14.19 352.89 157.7917
1990 14.38 354.16 142.2917
1991 14.35 355.48 145.775
1992 14.12 356.27 94.4833
1993 14.14 356.96 54.7333
1994 14.24 358.63 29.8667
1995 14.38 360.63 17.5
1996 14.30 362.37 8.625
1997 14.40 363.47 21.4833
1998 14.57 366.50 64.2083
1999 14.33 368.14 93.175
2000 14.33 369.41 119.5333
2001 14.48 371.07 110.925
2002 14.56 373.16 104.0917
2003 14.55 375.80 63.5667
2004 14.49 377.55 40.4417
2005 14.62 379.75 29.7833
2006 14.54 381.85 15.1833
2007 14.57 383.72 7.5417

=-=-=-=-=-=-= "R" Program Outputs =-=-=-=-=-=-=
The following are outputs of the "R" statistical program:
For information on "R," please see:

http://www.r-project.org/

--------

Call:
lm(formula = Temp ~ CO2, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.2316612 -0.0805322 0.0185249 0.0763159 0.1798386

Coefficients:
Estimate Std. Error t value Pr(|t|)
(Intercept) 1.10008e+01 2.41721e-01 45.5103 2.22e-16 ***
CO2 9.24797e-03 7.01018e-04 13.1922 2.22e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.101321 on 48 degrees of freedom
Multiple R-Squared: 0.783817, Adjusted R-squared: 0.779313
F-statistic: 174.034 on 1 and 48 DF, p-value: 2.220e-16

--------

Call:
lm(formula = Temp ~ Sunspots, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.3909495 -0.1523184 -0.0514594 0.1445919 0.4380756

Coefficients:
Estimate Std. Error t value Pr(|t|)
(Intercept) 1.41804e+01 5.39054e-02 263.06149 2e-16 ***
Sunspots 4.97803e-05 6.18766e-04 0.08045 0.93621
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.217902 on 48 degrees of freedom
Multiple R-Squared: 0.000134823, Adjusted R-squared: -0.0206957
F-statistic: 0.00647235 on 1 and 48 DF, p-value: 0.936213

"Roger Coppock" wrote in message
...
CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

Behold! Roger's own hockey stick!

James was whining again because:

Roger Coppock replied:

CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject he

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/Correla...efficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)


Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78. The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero. These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years. It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes. It will quickly separate the
wheat from the chaff.

Behold! Roger's own hockey stick!

Behold, James cowardice and intentional ignorance..

--Gas music from Jupiter indeed..

On Feb 17, 3:20 pm, Roger Coppock wrote:
CO2 or Sunspots: Statistical Correlation ...

I have an interesting idea, Roger, concerning the analysis of
temperature stats. You can email me by replacing "hotmail" in my addy
with "yahoo" and I'll get back to you.

Roger Coppock wrote:
CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject he

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/Correla...efficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)


Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.
...

As originally posted in spawning thread:

Why only 50 years? I'm not considering CO2 as part of my correlation,
why are you?

This is an arbitrary and fictitious limit that you have imposed without
sound justification, and therefore I must conclude that your results are
(deliberately?) skewed.

If you wish to engage in the discussion with your 'winning
computations', please use a timespan that is equivalent to what we are
discussing, specifically 1850-2000.

On Feb 17, 10:30 am, Kunt Loadturd the returd Loadturd the
wrote:
James was whining again because:





Roger Coppock replied:

CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject he

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/Correla...efficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78. The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero. These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years. It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes. It will quickly separate the
wheat from the chaff.

Behold! Roger's own hockey stick!

who cares?

--Gas bag from where the steers and queers reside indeed..

Loadturd the returd...how many sockpuppets you using today, ****tard?

"James" wrote
Behold! Roger's own hockey stick!

Behold. Sunspot / Temp Correlation = 0.00

"Peter Franks" wrote
Why only 50 years? I'm not considering CO2 as part of my correlation, why
are you?

This is an arbitrary and fictitious limit that you have imposed without
sound justification, and therefore I must conclude that your results are
(deliberately?) skewed.

One of the nice things about mathematics, is that it's available to
everyone.

Feel free to add years to that 50 you are complaining about. Franks.

Don't you have the ability?

On Feb 17, 2:15*pm, Peter Franks wrote:
[ . . . ]
Roger Coppock wrote:
Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. *This is as long as the longest directly
observed record of atmospheric CO2 concentration.
...

As originally posted in spawning thread:

Why only 50 years? *I'm not considering CO2 as part of my correlation,
why are you?

This is an arbitrary and fictitious limit that you have imposed without
sound justification, and therefore I must conclude that your results are
(deliberately?) skewed.
[ . . . ]
DID YOU READ MY POST? I don't think so.

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

Roger Coppock wrote:
On Feb 17, 2:15 pm, Peter Franks wrote:
[ . . . ]
Roger Coppock wrote:
Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.
...
As originally posted in spawning thread:

Why only 50 years? I'm not considering CO2 as part of my correlation,
why are you?

This is an arbitrary and fictitious limit that you have imposed without
sound justification, and therefore I must conclude that your results are
(deliberately?) skewed.
[ . . . ]
DID YOU READ MY POST? I don't think so.

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

Yes, I read that.

DID YOU READ MY POST? I don't think so either.

I made no mention of CO2. I'm not interested in correlating with CO2 in
this discussion, I AM interested in the correlation between temperature
and sunspots for the time period in question, specifically 1850-2000.

And just so that it is clear this time you ignore it, again.

NOT INTERESTED IN CORRELATING WITH CO2 IN THIS DISCUSSION.