CBEE 213
Process Data Analysis

Spring 2017

Lecture: TR 4-5:50 LINC 210

Instructors
Prof. Milo Koretsky Rich Hilliard Joseph Kraai Michael Rodriquez Arni Sturluson
e-Mail: koretsm@engr.orst.edu rodrimic@oregonstate.edu sturlusa@oregonstate.edu
  • Office Hours (201 Gleeson)

Tuesday 1-2:30
Thursday 2-3

  • Office Hours (Gleeson Lobby)

Tuesday 2:30 -3:30

  • Office Hours (Gleeson Lobby)

Monday 2-3


  • Office Hours (Gleeson Lobby)

Monday 3-4
 

  • Office Hours (Gleeson Lobby)

Tuesday 12-1
 

 

AIChE Concept Warehouse


Page Contents

 

Week

Topic

Reading Prequiz Handout Studio Homework

Due

Solution

1

Summary statistics, box plots, scatter plots and histograms

R. Text: 1.1, 2.1, 2.3 - 2.6 R: Go to Esrimation of Mean, Mode, and SD Use the button  new data set to try again. Repeat until you can come close to the values. Week 1
W_1_R.xlsx
Studio1

HW#1

4/12

HW#1Sln

2

Probability distributions

R: 3.1, 3.2, 3.5.1, 3.6.1, 3.8, 3.13 T: Go to Histograms and Box Plots. Use the button :new data set' to try again. Repeat until you can match the representations consistently.
R: Pre-quiz on Concept Warehouse
Week 2
W_2_T.xlsx
W_2_R.xlsx
Studio 2 HW#2

4/19

HW#2Sln
3

Sampling distributions and confidence intervals

Supplemental reading 

T: 4.1,4.2,4.6
supp read 9.2
R: supp read 9.4, 9.6
T: Pre-quiz on Concept Warehouse
R: Pre-quiz on Concept Warehouse
Week 3
Statistical Tables
MATLAB dists.
Studio 3 HW#3
HW 3_1
HW_3_2.xlsx
HW_3_3.xls
HW_3_4.xls

4/26

HW#3Sln
4

Sampling distributions and confidence intervals 

Linear regession and model fits

T: Catch-up
R: 6.1, 6.2.1, 6.2.3, 6.2.4
T: Pre-quiz on Concept Warehouse
R: Pre-quiz on Concept Warehouse
Example
W_4_T.xlsx
Worked
Week 4
W_4_R.xlsx
Studio 4 HW#4

5/4 

(start of class)

HW#4Sln
5

Linear regession and model fits

T: 6.3.1, 6.4.1
R: Matrix methods see handout Week 4
T: Pre-quiz on Concept Warehouse (and muddies/surprising point)
R: HW Due ... no pre-quiz 
W_5_T.xlsx
MT guide
Matrix MATLAB
Raw_Data.xlsx
Studio 5 No HW


6

Multiple linear and non linear regression

T: Midterm Exam
R: No Reading
T: MT Exam ... no pre-quiz MT Cover
Concept Map
Studio 6
(MT Group)
HW#5

5/17

HW#5Sln
7

ANOVA

T: pp 272-281
R: pp 281-288
T: Pre-quiz on Concept Warehouse
R: Complete CATME Peer Eval
Week 7
W_7_T.xlsx
Visual Data
Lecture Slides T
Studio 7 HW#6
HW6.xls

5/24

HW#6Sln
8

Statistical Process Control

T: pp 439-456,461-464 T: Complete CATME Peer Eval
R
Week 8
Lecture Slides W8
Studio 8 HW#7
Studio 8 data

5/31

HW#7Sln
9

DOE

T: Sections 7-2, 7-3
R:Sections 7-4, 7-5
Week 9_T
AT&T Paper
W_9_T.xlsx
Week 9_R
W_9_R.xlsx
Studio 9
Fab Link
Equip Manual
HW#8

6/7

HW#8Sln
10 Week 10_T
Final Study Guide
Week 10_R
Concept Maps
Studio 10


Announcements

  1. The FInal Cover page  is avialable.
  2. HW 8 Solution has been posted (6/8)
  3. HW 8 has been updated to correct a couple of typos (6/5)
  4. HW 7 solution posted
  5. HW7: If you did not complete studio 8, there is data available in the link beneath the HW.
  6. Final Exam: June 12, 2 - 350 PM
    Rooms:
    Sections 12, 17 (GTA = Rich) and 16, 18 (GTA = Arni) in LiNC 128
    Sections 13, 15 (GTA = Mike) and 14, 19 (GTA = Joe) in LiNC 210
  7. One 8x11” piece of paper with notes only in your own writing, calculator, and pencil/pens are permitted.
    Content: Focus on Linear Regression through Design of Experiments
  8. Week 8 lecture reflection available here
  9. HW 5 and 6 solutions and Week 8 lecture notes have been posted
  10. HW 7 Posted
  11. MKs office hours for May 25 will be moved to 1-2 PM
  12. HW 6 posted
  13. Lecture slides from T W7 posted
  14. The pre-quiz for Tuesday week 7 has been assigned.
  15. HW 5 posted
  16. Midterm Rooms:
    Sections 12, 17 (GTA = Rich) and 16, 18 (GTA = Arni) in LiNC 210
    Sections 13, 15 (GTA = Mike) and 14, 19 (GTA = Joe) in LiNC 228
  17. HW 4 solutions posted
  18. A midterm study guide has been posted, MT guide and so has the MT Cover and the Concept Map presented in class. 
  19. Week 4 muddiest / surprised points are available here
  20. Handout 4 updated to correct some typos (2 PM April 27)
  21. The pre-quiz for Thursday week 4 has been assigned.
  22. Output from the class example on Tuesday Week 4 has been posted
  23. Week 4 handout has been posted
  24. HW #4 and HW #2 solutions have been posted
  25. There is an error in HW 3 problem 3 statement; it shoud read, "Match data from the first 182 days of the year to the last 182 days of the year." A corrected vesion is posted.
  26. A pre-quiz for Thursday week 3 is available on the Concept Warehouse
  27. HW 3 Posted
  28. HW 2 clarifications:
  29. MATLAB Review Session: Monday April 17, 1-3 PM 200 Gleeson
  30. A pre-quiz for Tuesday week 3 is available on the Concept Warehouse
  31. HW1 Solutions Posted
  32. HW 2 Posted
  33. A pre-quiz for Thursday week 2 is available on the Concept Warehouse
  34. MATLAB resources are available here
  35. Week 1 muddiest / surprised points are available here
  36. HW #1 Posted
  37. Welcome to ChE, BioE, EnvE 213 :)

Homework Assignments

The homework assignments will be posted on this web page every week.

Homework assignments are to be turned in at studio at the beginning of class on the due date. Any computer work should be turned in as a hard copy and submitted via the web. Go to https://secure.engr.oregonstate.edu:8000 chose 'login to ENGR', and then follow instructions to submit. Files should be named HW#_YourLastName.

 

The required format is posted here. Your homework will not be graded if it does not adhere to this format (although we may be lenient for HW1)

Solutions (PDF) will be provided in this page after the date each homework assignment is due.

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Studios

 

Studio Time Section Room GTA LA
W 8-10 012 BEXL 102 Rich Hilliard Ryan Cashen
013 BEXL 103 Michael Rodriquez Abdullah Almutawa
W 12 - 2 014 BEXL 102 Joseph Kraai Emma Flaherty
015 BEXL 103 Michael Rodriquez Lorena Colcer
W 2 - 4 016 BEXL 102 Arni Sturluson Ayman Alabdullatif
017 BEXL 103 Rich Hilliard Emma Flaherty
W 4-6 018 BEXL 102 Arni Sturluson Ayman Alabdullatif
019 BEXL 103 Joseph Kraai Lorena Colcer
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Syllabus and Outline

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Lecture Handouts

Lecture handouts are available in the Table towards the top of this page.

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Course Learning Objectives

By the end of this course, you will be able to:

  1. By hand and using software, perform the following: (1) statistically summarize data including measures of central tendency and dispersion, and (2) use the appropriate graphical form to summarize data for analysis including box plots, scatter plots and histograms. Match given graphical output to the corresponding summary statistics. Explain trends in data based on these methods.
  2. List the key characteristics of probability distributions, in particular the normal distribution. Given a histogram, explain how it relates to the normal distribution. Given a mean, standard deviation and observed value, calculate the z-score and find the corresponding percentile. Identify populations that follow a binominal distribution and a Poisson distribution
  3. Describe the sampling distribution of a statistic, in particular the t distribution and the chi-squared distribution. Given a study, describe what role statistical inference plays in terms of the population and sample. Calculate confidence intervals. Statistically analyze data for significance and compare sets of data. Define the standard error of a statistic.
  4. Fit experimental data to an empirical model equation using least squares analysis. For linear regression, both by hand and using software, calculate the slope intercept and correlation coefficient. Explain the relation between the slope of the regression line and the correlation coefficient.
  5. Given data from a process, calculate control limits and capability (Cp and Cpk). Distinguish between specification limits and control limits. Make SPC control charts, including x, x-bar R, and x-bar S charts.
  6. Quantify the effect of (i) a single factor and (ii) two factors on a process by applying Analysis of Variance (ANOVA).
  7. In the context of Design of Experiments (DOE), (i) set up a balanced design array, (ii) create a marginal means plots and/or an interaction plot from the experimental response, and (iii) develop an empirical model equation.
  8. Define the important elements of a measurement system. Calculate the repeatability and reproducibility of a gauge based on measured data.

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Links and Applets

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