ENGR131_FA10_Purzer_4a

ENGR131_FA10_Purzer_4a - Prof. Purzer & GTA...

Info iconThis preview shows pages 1–13. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Prof. Purzer & GTA Masaki Kakoi Section 002 1 4A See 3B slides 2 3 DESIGN CAREER MODEL PROFESSIONAL HABITS Effective communication involves modeling Engineers use statistics to model problem and solutions 4 In your team, discuss: A team working at a beverage can (soda, beer, fruit juice) manufacturing company identified that many cans were coated with more lacquer than required. This team needs to optimize the lacquer coating process. What data would they need? How would they analyze these data? Share 5 Lacquer : clear coating sprayed inside of cans to maintain product taste and prevent product leakage due to corrosion. Measure (gather right data) Analyze (use statistical tools to identify the root of a problem) Improve (correct the problem, not the symptoms) Control (put a plan in place) Tannenbaum, K. (2003). Applying Six Sigma Methodology to Energy-Saving Projects. Retrieved on September 13, 2010 from http://texasiof.ces.utexas.edu/texasshowcase/pdfs/casestudies/cs_dow_sixsigma.pdf 6 Goal Make a compelling argument It is not enough just to have a logical conclusion Different perspectives often have different logic It is not enough just to have data Is it good data? Need Trustworthy data and chain of reasoning Claim = Data + Reasoning 3 measures of central tendency median, mode, mean 50 100 150 200 250 1 2 3 4 5 6 7 8 9 10 Frequency Number of Children in Household Histogram: Children in a Sample of US Households Median: midpoint or middle score Odd number of entries = middle value of a sorted data set For even number of entries = average of the two middle values Mode: The most frequently occurring value in a data set (e.g., frequency distribution) For data that is normally distributed, mean = median = mode Mean: In Excel, average = mean FYI mean and average are not always synonymous Arithmetic mean = Sum of the observations divided by the number of observations What does this look like as a formula ? Mathematically: What would this look like in EXCEL? See page 169 in Preliminary Edition n i i n x n n x x x x 1 2 1 1 ) ..... ( Mean, x-bar Calculate the median and mean for the following set of data: 4, 4,10 ,8 ,6, 12, 16 What happens to the median and mean if the last number (16) is replaced by 86?...
View Full Document

Page1 / 41

ENGR131_FA10_Purzer_4a - Prof. Purzer & GTA...

This preview shows document pages 1 - 13. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online