ENGR 1181 | Class 14: Data Analysis Application Instructions

Define You are being assigned 1 warmup problem and 1 application problem. Do all problems in one script file: Class_14_App.m. Include comment statements to help organize your script file.

Represent In the following problems, you will create some figures; use good graphing practices.

Plan Create a script file named Class_14_App.m Outline the steps your program will take by adding comment statements to your script file.

Implement Complete the problems in the script file.

Evaluate Comment on your answers to the Application Problems; are they reasonable? You may do a calculation (by hand or in Excel) to support your evaluation.

Document Publish the script file to pdf by clicking the publish button or by typing the following command into the Command Window. publish('Class_14_App','pdf') The script file name (no .m) must match what is in the single quotes in the publish command.

Submit the following: o Script file, plot, & command window output PDF generated above o Evaluate step comments including any calculation(s) done Warmup Problem 1 Use the linspace() command to generate a vector x of 100 elements from to , calculate two new vectors y1 and y2 using the relationships 1 = sin() and 2 = (sin ()) 2 . Plot y1 and y2 versus x on the same graph as a theoretical relationship. Follow good graphing practices: label your axes (as y1 and y2 and x) and include the title: sin(x) and sin(x)^2. Be sure to differentiate the line styles between the two plots.

Application Problem 1

Background You are an industrial systems engineer working on a quality control problem at your plant that produces high quality bicycle components. You have attempted a different manufacturing process on the line that produces headsets. You want to see if the new measures that you put in place on have improved the consistency and quality of the parts being made. The headsets should be 1.25 inches with a tolerance of 0.0001 inches.

Problem Statement

The headset size data for the old and new processes are stored in new_process.txt and old_process.txt, respectively; each line represents the size of a different part off the line. Load the data into MATLAB and use it to calculate the mean, standard deviation, maximum, and minimum headset size for each process. Use fprintf() commands (with units!) to display your results. In your evaluate step, also answer the question: is the new process better than the old one? Why or why not?