Last year I committed to providing this group with annual updates on worldwide temperature anomalies versus annual CO2 concentrations, and
so, here is my January 2026 update for this past year. First off, my
data sources; for the annual temperature anomalies, go here:
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
This is from a NASA GISS website, and I am pulling the J-D values for
each year. For CO2 concentrations, here is the NOAA website:
https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt
As before, it's the annual concentration. I am using Minitab v14, and
ran a regression with the dependent variable being temperature anomalies
and the independent being CO2 concentration; here are the results:
————— 1/14/2026 2:05:41 PM ————————————————————
Worksheet size: 10000 cells.
Welcome to Minitab, press F1 for help.
Retrieving project from file: 'C:\Program Files (x86)\MINITAB 14 Student\Studnt14\Global Warming.MPJ'
MTB > Regress 'Temp' 1 'CO2';
SUBC> Constant;
SUBC> Brief 2.
Regression Analysis: Temp versus CO2
The regression equation is
Temp = - 351 + 1.08 CO2
Predictor Coef SE Coef T P
Constant -350.57 12.77 -27.46 0.000
CO2 1.08012 0.03519 30.69 0.000
S = 9.40662 R-Sq = 93.5% R-Sq(adj) = 93.4%
Analysis of Variance
Source DF SS MS F P Regression 1 83344 83344 941.91 0.000
Residual Error 65 5751 88
Total 66 89096
Unusual Observations
Obs CO2 Temp Fit SE Fit Residual St Resid
66 425 128.00 108.06 2.51 19.94 2.20R
R denotes an observation with a large standardized residual.
END OUTPUT
As compared to the 2024 results, the R-Sq has gone up from 93.1 to 93.5 (with a corresponding smaller T-value); the tool is still identifying
#66 (2024) as being an outlier.
Dawn
Dawn Flood wrote:
Last year I committed to providing this group with annual updates on
worldwide temperature anomalies versus annual CO2 concentrations, and
so, here is my January 2026 update for this past year. First off, my
data sources; for the annual temperature anomalies, go here:
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
This is from a NASA GISS website, and I am pulling the J-D values for
each year. For CO2 concentrations, here is the NOAA website:
https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt
As before, it's the annual concentration. I am using Minitab v14, and
ran a regression with the dependent variable being temperature
anomalies and the independent being CO2 concentration; here are the
results:
————— 1/14/2026 2:05:41 PM ————————————————————
Worksheet size: 10000 cells.
Welcome to Minitab, press F1 for help.
Retrieving project from file: 'C:\Program Files (x86)\MINITAB 14
Student\Studnt14\Global Warming.MPJ'
MTB > Regress 'Temp' 1 'CO2';
SUBC> Constant;
SUBC> Brief 2.
Regression Analysis: Temp versus CO2
The regression equation is
Temp = - 351 + 1.08 CO2
Predictor Coef SE Coef T P
Constant -350.57 12.77 -27.46 0.000
CO2 1.08012 0.03519 30.69 0.000
S = 9.40662 R-Sq = 93.5% R-Sq(adj) = 93.4%
Analysis of Variance
Source DF SS MS F P >> Regression 1 83344 83344 941.91 0.000
Residual Error 65 5751 88
Total 66 89096
Unusual Observations
Obs CO2 Temp Fit SE Fit Residual St Resid
66 425 128.00 108.06 2.51 19.94 2.20R
R denotes an observation with a large standardized residual.
END OUTPUT
As compared to the 2024 results, the R-Sq has gone up from 93.1 to
93.5 (with a corresponding smaller T-value); the tool is still
identifying #66 (2024) as being an outlier.
Dawn
useless info
On 1/14/2026 2:34 PM, % wrote:
Dawn Flood wrote:
Last year I committed to providing this group with annual updates on
worldwide temperature anomalies versus annual CO2 concentrations, and
so, here is my January 2026 update for this past year. First off, my
data sources; for the annual temperature anomalies, go here:
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
This is from a NASA GISS website, and I am pulling the J-D values for
each year. For CO2 concentrations, here is the NOAA website:
https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt
As before, it's the annual concentration. I am using Minitab v14,
and ran a regression with the dependent variable being temperature
anomalies and the independent being CO2 concentration; here are the
results:
————— 1/14/2026 2:05:41 PM ————————————————————
Worksheet size: 10000 cells.
Welcome to Minitab, press F1 for help.
Retrieving project from file: 'C:\Program Files (x86)\MINITAB 14
Student\Studnt14\Global Warming.MPJ'
MTB > Regress 'Temp' 1 'CO2';
SUBC> Constant;
SUBC> Brief 2.
Regression Analysis: Temp versus CO2
The regression equation is
Temp = - 351 + 1.08 CO2
Predictor Coef SE Coef T P
Constant -350.57 12.77 -27.46 0.000
CO2 1.08012 0.03519 30.69 0.000
S = 9.40662 R-Sq = 93.5% R-Sq(adj) = 93.4%
Analysis of Variance
Source DF SS MS F P >>> Regression 1 83344 83344 941.91 0.000
Residual Error 65 5751 88
Total 66 89096
Unusual Observations
Obs CO2 Temp Fit SE Fit Residual St Resid
66 425 128.00 108.06 2.51 19.94 2.20R
R denotes an observation with a large standardized residual.
END OUTPUT
As compared to the 2024 results, the R-Sq has gone up from 93.1 to
93.5 (with a corresponding smaller T-value); the tool is still
identifying #66 (2024) as being an outlier.
Dawn
useless info
Why? It's just a simple regression. Try plotting the annual heights of children as they grow older sometime! I guarantee that you will get an overall result with a positive, highly correlated regression coefficient!
Dawn Flood wrote:
On 1/14/2026 2:34 PM, % wrote:
Dawn Flood wrote:
Last year I committed to providing this group with annual updates on
worldwide temperature anomalies versus annual CO2 concentrations,
and so, here is my January 2026 update for this past year. First
off, my data sources; for the annual temperature anomalies, go here:
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
This is from a NASA GISS website, and I am pulling the J-D values
for each year. For CO2 concentrations, here is the NOAA website:
https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt
As before, it's the annual concentration. I am using Minitab v14,
and ran a regression with the dependent variable being temperature
anomalies and the independent being CO2 concentration; here are the
results:
————— 1/14/2026 2:05:41 PM ————————————————————
Worksheet size: 10000 cells.
Welcome to Minitab, press F1 for help.
Retrieving project from file: 'C:\Program Files (x86)\MINITAB 14
Student\Studnt14\Global Warming.MPJ'
MTB > Regress 'Temp' 1 'CO2';
SUBC> Constant;
SUBC> Brief 2.
Regression Analysis: Temp versus CO2
The regression equation is
Temp = - 351 + 1.08 CO2
Predictor Coef SE Coef T P
Constant -350.57 12.77 -27.46 0.000
CO2 1.08012 0.03519 30.69 0.000
S = 9.40662 R-Sq = 93.5% R-Sq(adj) = 93.4%
Analysis of Variance
Source DF SS MS F P
Regression 1 83344 83344 941.91 0.000
Residual Error 65 5751 88
Total 66 89096
Unusual Observations
Obs CO2 Temp Fit SE Fit Residual St Resid
66 425 128.00 108.06 2.51 19.94 2.20R >>>>
R denotes an observation with a large standardized residual.
END OUTPUT
As compared to the 2024 results, the R-Sq has gone up from 93.1 to
93.5 (with a corresponding smaller T-value); the tool is still
identifying #66 (2024) as being an outlier.
Dawn
useless info
Why? It's just a simple regression. Try plotting the annual heights
of children as they grow older sometime! I guarantee that you will
get an overall result with a positive, highly correlated regression
coefficient!
no thanks i'm here to talk about atheism
Last year I committed to providing this group with annual updates
on worldwide temperature anomalies versus annual CO2
concentrations, and so, here is my January 2026 update for this
past year. First off, my data sources; for the annual
temperature anomalies, go here:
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
This is from a NASA GISS website, and I am pulling the J-D values
for each year. For CO2 concentrations, here is the NOAA website:
https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt
As before, it's the annual concentration. I am using Minitab
v14, and ran a regression with the dependent variable being
temperature anomalies and the independent being CO2
concentration; here are the results:
————— 1/14/2026 2:05:41 PM ————————————————————
Worksheet size: 10000 cells.
Welcome to Minitab, press F1 for help.
Retrieving project from file: 'C:\Program Files (x86)\MINITAB 14 Student\Studnt14\Global Warming.MPJ'
MTB > Regress 'Temp' 1 'CO2';
SUBC> Constant;
SUBC> Brief 2.
Regression Analysis: Temp versus CO2
The regression equation is
Temp = - 351 + 1.08 CO2
Predictor Coef SE Coef T P
Constant -350.57 12.77 -27.46 0.000
CO2 1.08012 0.03519 30.69 0.000
S = 9.40662 R-Sq = 93.5% R-Sq(adj) = 93.4%
Analysis of Variance
Source DF SS MS F P Regression 1 83344 83344 941.91 0.000
Residual Error 65 5751 88
Total 66 89096
Unusual Observations
Obs CO2 Temp Fit SE Fit Residual St Resid
66 425 128.00 108.06 2.51 19.94 2.20R
R denotes an observation with a large standardized residual.
END OUTPUT
As compared to the 2024 results, the R-Sq has gone up from 93.1
to 93.5 (with a corresponding smaller T-value); the tool is still identifying #66 (2024) as being an outlier.
Dawn
Dawn Flood wrote:
Last year I committed to providing this group with annual updates on
worldwide temperature anomalies versus annual CO2 concentrations, and
so, here is my January 2026 update for this past year. First off, my
data sources; for the annual temperature anomalies, go here:
https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt
This is from a NASA GISS website, and I am pulling the J-D values for
each year. For CO2 concentrations, here is the NOAA website:
https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt
As before, it's the annual concentration. I am using Minitab v14, and
ran a regression with the dependent variable being temperature
anomalies and the independent being CO2 concentration; here are the
results:
————— 1/14/2026 2:05:41 PM ————————————————————
Worksheet size: 10000 cells.
Welcome to Minitab, press F1 for help.
Retrieving project from file: 'C:\Program Files (x86)\MINITAB 14
Student\Studnt14\Global Warming.MPJ'
MTB > Regress 'Temp' 1 'CO2';
SUBC> Constant;
SUBC> Brief 2.
Regression Analysis: Temp versus CO2
The regression equation is
Temp = - 351 + 1.08 CO2
Predictor Coef SE Coef T P
Constant -350.57 12.77 -27.46 0.000
CO2 1.08012 0.03519 30.69 0.000
S = 9.40662 R-Sq = 93.5% R-Sq(adj) = 93.4%
Analysis of Variance
Source DF SS MS F P >> Regression 1 83344 83344 941.91 0.000
Residual Error 65 5751 88
Total 66 89096
Unusual Observations
Obs CO2 Temp Fit SE Fit Residual St Resid
66 425 128.00 108.06 2.51 19.94 2.20R
R denotes an observation with a large standardized residual.
END OUTPUT
As compared to the 2024 results, the R-Sq has gone up from 93.1 to
93.5 (with a corresponding smaller T-value); the tool is still
identifying #66 (2024) as being an outlier.
Dawn
2024 had some el nino thing?
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