IOE366-Ch10-Analysis+of+Variance

IOE366-Ch10-Analysis+of+Variance - Linear Statistical...

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inear Statistical Models Linear Statistical Models IOE 366, Win 2010 Gary D. Herrin, Ph.D. IOE Professor mail: dherrin@umich edu Email: gdherrin@umich.edu Office: 1733 IOE Phone: 763-0040 Some of my background. . Education Background: ± S, MS, PhD: Industrial and Systems Engineering BS, MS, PhD: Industrial and Systems Engineering (Ohio State University, 1964-73) Industry Experience: yp ± Consultant to 50+ companies ± Father of 3 IEs Research Experience: ± 70 PhD Committees – Quality Engineering / Ergonomics Teaching Experience: ± IOE 265, 366, 465, 466, 565, 566 lso: IOE 300 (201, 202), 333, 463, 424, 490, etc. 2 ± Also: IOE 300 (201, 202), 333, 463, 424, 490, etc. ± Co-Director, Six Sigma Programs @ UM Graduate Student Instructors Yinghao Ni niyingh@umich.edu Howard Wu howardwu@umich.edu 3 Resources ± Lecture & Lab Resources: https://ctools.umich.edu/portal p/ / / p ± Homework Resources: https://www.webassign.net/login.html ( Class Key : annarbor.umich 4349 3534 ) ± Lecture Archive: http://inst-tech.engin.umich.edu/media/?sk=ioe366-w10- enw4xxl2 ± Faculty Email: ioe366w10instructors@umich.edu 4
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About the Course rerequisites ± Prerequisites ± CTools, WebAssign , Video Archives ttendance Policy ± Attendance Policy ± Labs omework ± Homework ± Exams onor Code ± Honor Code ± Minitab ± rading Policy 5 Grading Policy Prerequisites E/Stat 265 + Math 214 ± IOE/Stat 265 + Math 214 ± High Speed Internet Access ± Check class email; access CTools, WileyPlus, and Video Archives! y, ± ~10-15 hours per week to commit to tudy study ± Attend class and lab, read, review, hw, 6 THINK. Course Objectives ± Learn how to analyze data arising from undesigned experiments through regression analysis. ± Learn how to model relationships between variables. ± Learn how to design an experiment. earn how to apply linear statistical models to ± Learn how to apply linear statistical models to industrial processes. 7 ± Use statistical software for experiment design and data analysis. Course Outcomes ppreciate the concept of variability and its ± Appreciate the concept of variability and its importance. ± Understand basic principles of data collection (random sampling, randomization, and blocking) ± Know and demonstrate graphical and numerical techniques for summarizing and presenting ata data ± Understand the basic methods for drawing valid conclusions (inference) for different situations 8
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h 10 he Analysis of Variance: Ch 10 The Analysis of Variance: Single Factor Studies 10.1 Single Factor ANOVA 0 2 Posterior Multiple Comparisons 10.2 Posterior Multiple Comparisons 10.3 More on Single Factor ANOVA ± Analysis of Variance ± Checking Model Aptness gp ± Sample Size Determination 9 Example 1: Golf Balls rand 1 rand 2 rand 3 rand Brand 1 Brand 2 Brand 3 Brand 206 217 227 231 208 221 225 228 206 218 230 232 204 224 229 225 210 212 221 230 204 214 224 231 07 21 24 22 207 221 224 222 206 229 234 235 10 11 Dotplots of Distance by Design Dotplots of Distance by Design (group means are indicated by lines) (group means are indicated by lines) 235 235 225 ance 225 215 Dis 215 Dist 205 205 1 2 3 4 Design Design 12
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Boxplots of Distance by Design (means are indicated by solid circles) 235 225 ce 215 Distan 1 2 3 4 205 Design 13 10-1 One-Way Design Layout
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This note was uploaded on 03/17/2010 for the course IOE 366 taught by Professor Garyherrin during the Winter '10 term at University of Michigan-Dearborn.

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IOE366-Ch10-Analysis+of+Variance - Linear Statistical...

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