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More creative ways to present statistical results / data

More creative ways to present statistical results / data. y-axis. or “the worst graphs ever” !?. The next examples are taken from a web-page that shares educational material for teachers (the graphs were actually published in newspapers and magazines)

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More creative ways to present statistical results / data

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  1. More creative ways to present statistical results / data y-axis or “the worst graphs ever” !? The next examples are taken from a web-page that shares educational material for teachers (the graphs were actually published in newspapers and magazines) http://dpcdsb-gains.wikispaces.com/file/view/Worst+Graphs+Ever.pdf/126543183/Worst%20Graphs%20Ever.pdf (retrieved March 2014) x-axis

  2. More creative ways to present statistical results / data y-axis Note: When talking about regression We say “y is regressed on x” x-axis

  3. Δ Δx=10yr Δx=2yr 1965 1973 1975 y-axis 1955 x-axis

  4. Δ Δx=10yr Δx=2yr 1965 1973 1975 y-axis 1955 $58,000 Δy= $8,000 $50,000 $29,000 Δy= $13,000 $16,000 Δy/Δx= $8,000/2yr Δy/Δx= $13,000/10yr x-axis

  5. $60,000 $40,000 $20,000 1940 1960 1980

  6. Δy=4miles Δy=0.5mile Distance (y-axis) Distortion factor ( ‘Lie-factor’) Time (x-axis)

  7. And the objective presentation of the data

  8. Some more creative ways to hide or distort the statistical results…

  9. Climate Variability:El Niño - Southern Oscillation El Niño region SST departures (anomalies) (oC)measured in different regions of the tropical Pacific (SST: Sea Surface Temperature)

  10. El Niño Region SST Departures (oC) Recent Evolution

  11. Climate Variability:El Niño - Southern Oscillation Fossil corals Image source: http://www.ncdc.noaa.gov/paleo/pubs/cobb2003/cobb2003.html

  12. Climate Variability:El Niño - Southern Oscillation Red: Observed SST anomalies Black : Coral reconstructions (oxygen isotopes) Cobb, K.M., C.D. Charles, H. Cheng & R.L. Edwards, 2003,El Niño-Southern Oscillation and tropical Pacific climate during the last millennium. Nature, Vol. 424, No. 6946, pp. 271 - 276 (17 July 2003).

  13. Climate Variability:El Niño - Southern Oscillation Cobb, K.M., C.D. Charles, H. Cheng & R.L. Edwards, 2003,El Niño-Southern Oscillation and tropical Pacific climate during the last millennium. Nature, Vol. 424, No. 6946, pp. 271 - 276 (17 July 2003). http://www.ncdc.noaa.gov/paleo/pubs/cobb2003/cobb2003.html

  14. Reconstructed Climate Variability:A.D. 1320-1480 http://www.ncdc.noaa.gov/paleo/pubs/cobb2003/cobb2003.html

  15. Effect of ENSO on Global Rainfall From Prof. Aiguo Dai’s paper in Geophysical Research Letters (2000) Global Teleconnection Pattern http://precip.gsfc.nasa.gov/rain_pages/el_nino_vsn2.html

  16. U.S. Temperature and Precipitation Departures During the Last 30 and 90 Days Effect of ENSO on Global Rainfall Last 30 Days 30-day (ending 22 Mar 2014) temperature departures (degree C) 30-day (ending 22 Mar 2014) % of average precipitation Last 90 Days 90-day (ending 22 Mar 2014) % of average precipitation 90-day (ending 22 Mar 2014) temperature departures (degree C) http://precip.gsfc.nasa.gov/rain_pages/el_nino_vsn2.html

  17. R-scripts and data update • We will work in the next weeks with ENSO and local climate data. We will explore if we find correlations between rainfall and temperatures in the state of New York and ENSO. Please update the following files in your local scripts-directory (If you have not done so already in class (April 27th, 2014)): myfunctions.R climatology.R plot_climatology.R class12.R class15.R http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/

  18. R-scripts and data update Please update the following file in your local data-directory (If you have not done so already in class (April 27th, 2014)): create a local subdirectory named ‘NY’ (for New York State) then download some of the USW station data files NOTE: ghcnd-stations-NY.csv you open in R-studio (or text edito) To see a list of stations with geographic locations and the name of th station. http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/data/NY/

  19. Processing new station data • Calculate the 1981-2010 climatology withclimatology.R • ( input is e.g. USW00094789_tavg_mon_mean.asc) • This creates two output files • (a) the monthly mean climatology) • ( e.g. USW00094789_tavg_mon_mean_climc_1981-2010.csv) • (b) the monthly mean anomalies • ( e.g. USW00094789_tavg_mon_mean_ano.asc)

  20. Processing new station data

  21. Processing new station data 2) Use plot_climatology.R to see the climatological cycle

  22. Processing new station data 3) Use class15.R To work the newly created anomaly data files to compare the time evolution and study the correlation between two stations.

  23. Processing new station data 3) Use class15.R To work the newly created anomaly data files to compare the time evolution and study the correlation between two stations. Note: If there are gaps in the data, the program does not do the calculation (this will be fixed …)

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