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Data analysis

Data analysis. Data analysis and interpretation. Think about analysis EARLY Start with a plan Collect data, clean, quality check Analyze Interpret Reflect What did we learn? What conclusions can we draw? What are our recommendations? What are the limitations of our analysis?.

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Data analysis

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  1. Data analysis

  2. Data analysis and interpretation • Think about analysis EARLY • Start with a plan • Collect data, clean, quality check • Analyze • Interpret • Reflect • What did we learn? • What conclusions can we draw? • What are our recommendations? • What are the limitations of our analysis?

  3. Blind men and an elephant - Indian fableSix blind men go to observe an elephant. One feels the side and thinks the elephant is like a wall. One feels the tusk and thinks the elephant is a like a spear. One touches the squirming trunk and thinks the elephant is like a snake. One feels the knee and thinks the elephant is like a tree One touches the ear, and thinks the elephant is like a fan. One grasps the tail and thinks it is like a rope. They argue long and loud and though each was partly in the right, all were in the wrong.

  4. Blind men and an elephant - Indian fableThings aren’t always what we think! For a detailed version of this fable see: http://www.wordinfo.info/words/index/info/view_unit/1/?letter=B&spage=3

  5. Data analysis Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions and support decision making. • Type of data • Data can be of several types • Quantitative data • Categorical data • Qualitative data • Data Analysis Process • Data Cleaning • Data quality • Analysis

  6. Data analysis Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions and support decision making. • Type of data • Data can be of several types • Quantitative data • Categorical data • Qualitative data • Data Analysis Process • Data Cleaning • Data quality • Analysis

  7. Data analysis • Data Cleaning • Data cleaning is an important procedure during which the data are inspected, and erroneous data are—if necessary, preferable, and possible—corrected. • NOTE: • Before Cleaning: • Back up original data (cleaning should not be performed on the original data) • Do not throw information away at any stage in the data cleaning phase. • all alterations to the data set should carefully and clearly documented • Checks on data cleaning • have decisions influenced the distribution of the variables? • The distribution of the variables before data cleaning is compared to the distribution of the variables after data cleaning to see whether data cleaning has had unwanted effects on the data.

  8. … data quality • The quality of the data should be checked as early as possible. • Data quality can be assessed in several ways, using different types of analyses: • Frequency counts • Descriptive statistics • mean, • standard deviation, • median, • Normality • skewness, • kurtosis, • frequency histograms, • normal probability plots, • Associations • correlations, • scatter plots.

  9. … data quality • missing data • are there many missing values? • and are the values missing at random (MAR)? • Are missing data more than 25% ? • Extreme Values • outlying observations in the data are analyzed to see if they seem to disturb the distribution. • Test for method to be used • eg Pearson Correlation - linear relationship between two variables, Spearman's correlation – non linear relationship • Analyses • …..

  10. Where to get Climatological Data http://www.esrl.noaa.gov/psd/data/gridded/

  11. Where to get Climatological Data http://www.esrl.noaa.gov/psd/data/climateindices/list/

  12. Where to get Climatological Data http://weather.unisys.com/hurricane/

  13. PACKAGING YOUR RESULTS

  14. The Grid Analysis and Display System (GrADS) NCAR Command Language (NCL) R Visualization and Analysis Platform for Ocean (VAPOR) SURFER

  15. Where to get Climatological Data http://www.esrl.noaa.gov/psd/cgi-bin/data/composites/printpage.pl

  16. Thank you

  17. Key components of a data analysis plan • Purpose of the evaluation • Questions • What you hope to learn from the question • Analysis technique • How data will be presented

  18. Common descriptive statistics • Count (frequencies) • Percentage • Mean • Mode • Median • Range • Standard deviation • Variance • Ranking

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