Data analysis 16621 arch13.2003
Data Analysis 16.621 March 13, 2003
Data analysis process Keep the end goal in mind! Experiment Report (Raw Data) F Indings Assess Hypothesis Data reduction (Reduced Data) Data Interpretation Graphica Extract Analysis findings Conceptually Compare separate, but with theor Incorporate can be or other data k Knowledge iterations/feedback between them
Data Analysis Process Experiment (Raw Data) Data Reduction (Reduced Data) Assess Hypothesis Report Findings Data Interpretation Graphical Analysis Compare with theory or other data Incorporate knowledge Extract findings Conceptually separate, but can be iterations/feedback between them Keep the end goal in mind!
Experiment Step Experiment produces raw data In measured units(volts, seconds, lbs, .. Organization determined by recording method (notebook, tabular form, database Take data early to check-out experiment Do some"quick data reduction/analysis during experiment to see if results look ok Goal: Assure data set is complete and all information is collected while experiment is still set u p argely a recording process
Experiment Step • Experiment produces “raw data” – In measured units (volts, seconds, lbs, ….) – Organization determined by recording method (notebook, tabular form, database,….) • Take data early to check-out experiment • Do some “quick data reduction/analysis” during experiment to see if results look ok. • Goal: Assure data set is complete and all information is collected while experiment is still set up. Largely a recording process
Data Reduction Step Convert and normalize or nondimensionalize raw data to meaningful variables temperature, time of flight, fc orce coefficients,) Statistical analysis as appropriate(next class) Error analysis as appropriate Goal: assure data is“ valid” before Interpretation Largely a deductive process
Data Reduction Step • Convert and normalize or nondimensionalize raw data to “meaningful variables” (temperature, time of flight, force coefficients,...) • Statistical analysis as appropriate (next class) • Error analysis as appropriate • Goal: assure data is “valid” before interpretation Largely a deductive process
Data Interpretation Step Interaction/Iteration of four sub elements Graphical analysis: helps visualize data to see trends, patterns relationships to theory Compare with theory or other data to determine agreement or differences(both important Incorporate knowledge learned in classes or from experience Extract findings which represent the knowledge generated from the experiment Implementation tailored to each project This step is where value' is added by researcher a highly inductive process
Data Interpretation Step • Interaction/Iteration of four sub elements – Graphical analysis: helps visualize data to see trends, patterns, relationships to theory,… – Compare with theory or other data to determine agreement or differences (both important) – Incorporate knowledge learned in classes or from experience – Extract findings which represent t he knowledge generated from the experiment • Implementation tailored to each project. • This step is where “value” is added by researcher A highly inductive process!
Error Analysis-A General pproach During the design of the experiment 1 Identify all possible sources of error Experiment set up: facility effects, environmental effects, human subjects Measurement system: velocity, temperature 2 Estimate possible severity of each source Discuss with advisor 3 For those that are considered "important, identify mitigation strategies Experimental design and/or test protocols(e.g. repeat tests 4 Plan for quantitative analysis of reduced data Quantitative analysis relies on math model of the system Not possible for all situations: human factors tests, s/w studies Often good for measurement systems: pitot probe, strain gauge Sometimes quoted by manufacturer or supplier Keep the end goal in mind
Error Analysis - A General Approach z During the design of the experiment 1 Identify all possible sources of error: – Experiment set up: facility effects, environmental effects, human subjects, ….. – Measurement system: velocity, temperature,... 2 Estimate possible severity of each source – Discuss with advisor. 3 For those that are considered “important”, identify mitigation strategies. – Experimental design and/or test protocols (e.g. repeat tests) 4 Plan for quantitative analysis of reduced data – Quantitative analysis relies on math model of the system – Not possible for all situations: human factors tests, s/w studies – Often good for measurement systems: pitot probe, strain gauge,... • Sometimes quoted by manufacturer or supplier Keep the end goal in mind!
Error Analysis-A General Approach II During the experiment Execute experiment according to protocols Record notes in lab notebook Check for mistakes · During data reduction Calculate error bars for measurements Check for outlier points During data interpretation/reporting Consider errors when interpreting data assure findings are beyond uncertainty of experiment Display error bars in way that aids in understanding findings Goal: To qualify"the accuracy of your data to support findings
Error Analysis - A General Approach II • During the experiment – Execute experiment according to protocols – Record notes in lab notebook – Check for mistakes • During data reduction – Calculate error bars for measurements – Check for outlier points • During data interpretation/reporting – Consider errors when interpreting data • Assure findings are beyond uncertainty of experiment – Display error bars in way that aids in understanding findings Goal: To “qualify” the accuracy of your data to support findings
Exercise Make a sketch of a figure that you might put in your final report which displays your experimental data in a way that can be used to assess your hypothesis What questions do you have about data reduction For example Statistical analysis grap cal analysis Error analysis Turn in your sketch and questions
Exercise • Make a sketch of a figure that you might put in your final report which displays your experimental data in a way that can be used to assess your hypothesis. • What questions do you have about data reduction? For example – Statistical analysis – Graphical analysis – Error analysis • Turn in your sketch and questions
