More creative ways to present statistical results / data y-axis x-axis or “the worst graphs ever” !? The next examples are taken from a web-page that shares.

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Presentation transcript:

More creative ways to present statistical results / data y-axis x-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) gains.wikispaces.com/file/view/Worst+Graphs +Ever.pdf/ /Worst%20Graphs%20Ev er.pdf (retrieved March 2014)

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

y-axis x-axis Δx=10yr Δ Δx=2yr

y-axis x-axis Δx=10yr Δ Δx=2yr $58,000 $50,000 $16,000 $29,000 Δy= $8,000 Δy= $13,000 Δy/Δx= $8,000/2yr Δy/Δx= $13,000/10yr

$20,000 $40,000 $60,000

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

And the objective presentation of the data

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

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

During the last 2 months, an oceanic Kelvin wave (downwelling phase) was associated with the eastward shift of above-average temperatures. Recently, positive subsurface anomalies have shifted farther eastward, while negative anomalies have retracted to near S. America. Sub-Surface Temperature Departures ( o C) in the Equatorial Pacific Most recent pentad analysis Longitude Time

El Niño Region SST Departures ( o C) Recent Evolution

Climate Variability: El Niño - Southern Oscillation Image source: Fossil corals

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 (17 July 2003). Red: Observed SST anomalies Black : Coral reconstructions (oxygen isotopes)

Climate Variability: El Niño - Southern Oscillation 003/cobb2003.html 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 (17 July 2003).

Reconstructed Climate Variability: A.D /cobb2003.html

Effect of ENSO on Global Rainfall _vsn2.html From Prof. Aiguo Dai’s paper in Geophysical Research Letters (2000) Global Teleconnection Pattern

Effect of ENSO on Global Rainfall _vsn2.html U.S. Temperature and Precipitation Departures During the Last 30 and 90 Days 30-day (ending 22 Mar 2014) temperature departures (degree C) 90-day (ending 22 Mar 2014) % of average precipitation 90-day (ending 22 Mar 2014) temperature departures (degree C) Last 30 Days Last 90 Days 30-day (ending 22 Mar 2014) % of average precipitation

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 27 th, 2014)): myfunctions.R climatology.R plot_climatology.R class12.R class15.R

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 27 th, 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.

Processing new station data 1)Calculate the climatology with climatology.R ( input is e.g. USW _tavg_mon_mean.asc) This creates two output files (a) the monthly mean climatology) ( e.g. USW _tavg_mon_mean_climc_ csv) (b) the monthly mean anomalies ( e.g. USW _tavg_mon_mean_ano.asc)

Processing new station data

2) Use plot_climatology.R to see the climatological cycle

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.

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 …)