From: Dawn.Belle.Flood@gmail.com   
      
   On 1/15/2026 8:03 AM, % wrote:   
   > 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   ————†  
   â€”——————————————   
   >>>>   
   >>>> 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   
      
   Me, too!!   
      
   --- SoupGate-Win32 v1.05   
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