1. Presenting and Analysing your Data CSCI 6620 Spring 2014 Thesis Projects: Chapter 10 CSCI 6620 Spring 2014 Thesis Projects: Chapter 10

2. Process »Gather Data »Present Data »Analyze Data »Gather Data »Present Data »Analyze Data

3. Process »Gather Data - Already Done!! »Present Data »Analyze Data »Gather Data - Already Done!! »Present Data »Analyze Data

4. Non-Numerical Data »Literature Analysis »Interviews »Questionnaires »Implementations (code) »Literature Analysis »Interviews »Questionnaires »Implementations (code)

5. Literature Analysis Dos and Don’ts »Don’t simply list a set of quotes »Don’t use the amount of time you spent reading a source as a criteria for whether or not it should be included »Do structure the presentation in such a way that it will lend itself to analysis »Do carefully separate useful literature from other literature »Do include important counter-arguments »Don’t simply list a set of quotes »Don’t use the amount of time you spent reading a source as a criteria for whether or not it should be included »Do structure the presentation in such a way that it will lend itself to analysis »Do carefully separate useful literature from other literature »Do include important counter-arguments

6. Sample Literature Analysis Presentation Structure »Describe problem in detail including shortcomings of previous methods and properties needed in a good method »Describe method in detail including how it has the properties previously mentioned. »Describe arguments as to why the method might fail »Describe problem in detail including shortcomings of previous methods and properties needed in a good method »Describe method in detail including how it has the properties previously mentioned. »Describe arguments as to why the method might fail

7. Presenting Questionnaires and Interviews »Have to decide if you will use “exact” transcripts of entire interviews or summaries of each interview. »Probably will use tables »Try structuring the tables multiple ways and get opinions on what presents the data better. »Have to decide if you will use “exact” transcripts of entire interviews or summaries of each interview. »Probably will use tables »Try structuring the tables multiple ways and get opinions on what presents the data better.

8. Presenting Implementations »Use good software development practice when developing the implementation »Show as much of the documentation as is needed in order to convince the reader that the implementation is correct, i.e. that there are no errors in the code. »Use good software development practice when developing the implementation »Show as much of the documentation as is needed in order to convince the reader that the implementation is correct, i.e. that there are no errors in the code.

9. Implementation Dos and Don’ts »Don’t show all the code and expect the user to verify the correctness »Don’t put all the code in the body of the document »Do show pseudo-code or flowcharts or some other graphical representation of the algorithm. »Do present relevant pieces of the code »Do follow good commenting style »Do provide any hardware or software requirements or user instructions »Do use standard test cases for validation »Don’t show all the code and expect the user to verify the correctness »Don’t put all the code in the body of the document »Do show pseudo-code or flowcharts or some other graphical representation of the algorithm. »Do present relevant pieces of the code »Do follow good commenting style »Do provide any hardware or software requirements or user instructions »Do use standard test cases for validation

10. Presenting Numerical Data »Tables »Graphs »Significance Tests »Tables »Graphs »Significance Tests

11. Use of Tables »Qualitative data »Very few data points (less than 6-10) »Only one independent variable »Need to show exact individual values »Qualitative data »Very few data points (less than 6-10) »Only one independent variable »Need to show exact individual values

12. Use of Graphs »Quantitative data »Large number of data points (>6-10) »More than one independent variable »Presenting statistical information (such as variations in average) »Need to show overall trend »Quantitative data »Large number of data points (>6-10) »More than one independent variable »Presenting statistical information (such as variations in average) »Need to show overall trend

13. Avoid Misleading Graphs/Tables »Too narrow an interval »Too big (or small) of a scale on the y axis »Watch out for results of automated graphing tools »Too narrow an interval »Too big (or small) of a scale on the y axis »Watch out for results of automated graphing tools

14. Significance Tests »Use if system is stochastic »Do multiple runs and present average  http://www.surveysystem.com/sscalc.htm http://www.surveysystem.com/sscalc.htm »Choose appropriate statistical test  http://en.wikipedia.org/wiki/Statistical_hypothesis_testing#Common_test_statistics http://en.wikipedia.org/wiki/Statistical_hypothesis_testing#Common_test_statistics »Get help from an expert »Use if system is stochastic »Do multiple runs and present average  http://www.surveysystem.com/sscalc.htm http://www.surveysystem.com/sscalc.htm »Choose appropriate statistical test  http://en.wikipedia.org/wiki/Statistical_hypothesis_testing#Common_test_statistics http://en.wikipedia.org/wiki/Statistical_hypothesis_testing#Common_test_statistics »Get help from an expert

15. Beware of claiming significance: http://forums.construx.com/blogs/stevemc c/archive/2008/03/27/productivity- variations-among-software-developers- and-teams-the-origin-of-quot-10x-quot.aspx http://forums.construx.com/blogs/stevemc c/archive/2008/03/27/productivity- variations-among-software-developers- and-teams-the-origin-of-quot-10x-quot.aspx

16. Analyze your Data »Systematically evaluate against the objectives »Good analysis is dependent on good data collection »Carefully analyze so as not to introduce errors »Be objective »Focus on verifying or falsifying the aim »Systematically evaluate against the objectives »Good analysis is dependent on good data collection »Carefully analyze so as not to introduce errors »Be objective »Focus on verifying or falsifying the aim