Chapter 8 making sense of data in six sigma and lean
Sponsored Links
This presentation is the property of its rightful owner.
1 / 29

Chapter 8 Making Sense of Data in Six Sigma and Lean PowerPoint PPT Presentation


  • 92 Views
  • Uploaded on
  • Presentation posted in: General

Chapter 8 Making Sense of Data in Six Sigma and Lean. How to tell “story” from dataset? Quantitative Data. Graphical Methods Dot Plots Stem-and-Leaf Plots Frequency Tables Histograms and Performance Histograms Run Charts Time-Series Plots Numerical Methods: Descriptive Statistics.

Download Presentation

Chapter 8 Making Sense of Data in Six Sigma and Lean

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Chapter 8Making Sense of Data inSix Sigma and Lean


How to tell “story” from dataset?Quantitative Data

  • Graphical Methods

    • Dot Plots

    • Stem-and-Leaf Plots

    • Frequency Tables

    • Histograms and Performance Histograms

    • Run Charts

    • Time-Series Plots

  • Numerical Methods: Descriptive Statistics


How to tell “story” from dataset?Qualitative Data

  • Pie Charts

  • Bar Charts

  • Pareto Analysis with Lorenz Curve


How to tell “story” from dataset?Bivarite Data

  • Graphical Methods

    • Scatter Plots

  • Numerical Methods: Correlation Coefficient

    • Pearson Coefficient

    • Spearman’s Rho ()

    • Kendall’s Tau () Rank Correlation


How to tell “story” from dataset?Multi-Vari Data

  • Graphical Methods

    • Multi-Vari Charts


Summarizing Quantitative Data:Dot Plots

  • Dot plot is one of the most simple types of plots

Example 8.1

Minitab

Graph

Dotplot

Simple


Summarizing Quantitative Data:Stem-and-Leaf Plots

  • Stem-and-Leaf Plots are a method for showing the frequency with which certain classes of values occur.

i160.photobucket.com/.../treediagram.png


Summarizing Quantitative Data:Frequency Tables

  • constructed by arranging collected data values in ascending order of magnitude with their corresponding frequencies.

  • Absolute frequencies or relative frequencies (%)

www.sci.sdsu.edu/.../Weeks/images/Frequency.png


Summarizing Quantitative Data: Histogram

www.statcan.gc.ca/.../ch9/images/histo1.gif


Summarizing Quantitative Data:Run Charts

  • A line graph of data points plotted in chronological order that helps detect special causes of variation

Minitab

Graph

Time Series Plot

Simple


Summarizing Quantitative Data: Time-Series Plots

  • A time series plot is a graph showing a set of observations taken at different points in time and charted in a time series.

Minitab

Graph

Time Series Plot

Simple


Summarizing Quantitative Data:Descriptive Statistics

Measures of Center

  • Sample mean

  • Population mean

  • Median: the "middle" value in the dataset

  • Mode: the value that occurs most often


Summarizing Quantitative Data:Descriptive Statistics

Measures of Variation

  • Range: the difference between the largest and the smallest values in the dataset

  • Sample variance

  • Sample standard deviation

  • Population variance

  • Population standard deviation


Summarizing Quantitative Data:Descriptive Statistics

Measures of Variation

  • Coefficient of Variation (CV)

  • Interquartile Range (IQR)


Summarizing Quantitative Data:Descriptive Statistics

  • Minimum

  • Maximum

  • Median

  • First Quartile

  • Third Quartile

  • Minitab:

  • Stat

  • Basic Statistics

  • Display Descriptive..

  • Boxplot


Summarizing Quantitative Data:Descriptive Statistics

Identifying Potential Outliers

  • Lower inner fence (LIF) =

  • Upper inner fence (UIF) =

  • Lower outer fence (LOF) =

  • Upper outer fence (UOF) =

  • Mild outliers: data fall between the two lower fences and between the two upper fences

  • Extreme outliers: data fall below the LOF or above the UOF


Summarizing Quantitative Data:Descriptive Statistics

Measures of Positions

  • Percentiles

    • Percentiles divide the dataset into 100 equal parts

    • Percentiles measure position from the bottom

    • Percentiles are most often used for determining the relative standing of an individual in a population or the rank position of the individual.

  • z scores

    • Standard normal distribution ( = 0 and  = 1)


Summarizing Qualitative Data:Graphical Displays

  • Pie Chart

http://techie-teacher-wanna-be.wikispaces.com/file/view/SocialPieChart.png/96606670/SocialPieChart.png


Summarizing Qualitative Data:Graphical Displays

  • Bar Graph

www.creationfactor.net/images/graph-bar.jpg


Summarizing Qualitative Data:Graphical Displays

  • Pareto Analysis with Lorenz Curve

www.spcforexcel.com/files/images/ccpareto.gif


Summarizing Bivariate Data:Scatterplot

Minitab:

Graph

Scatterplot

Simple


Summarizing Bivariate Data:Correlation Coefficient

  • Pearson Correlation Coefficient

Minitab:

Stat

Regression

Regression


Summarizing Bivariate Data:Correlation Coefficient

  • Spearman’s Rho ()

    • A measure of the linear relationship between two variables.

    • It differs from Pearson's correlation only in that the computations are done after the numbers are converted to ranks.

    • When converting to ranks, the smallest value on X becomes a rank of 1, etc.

    • D (Difference) is calculated between the pair of ranks


Summarizing Bivariate Data:Correlation Coefficient

  • Spearman’s Rho () Example


Summarizing Bivariate Data:Correlation Coefficient

  • Kendall’s Tau ()

    • A measure of the linear relationship between two variables.

    • It differs from Pearson's correlation only in that the computations are done after the numbers are converted to ranks.

    • When converting to ranks, the smallest value on X becomes a rank of 1, etc.

    • P is # of pairs with both ranks higher


Summarizing Bivariate Data:Correlation Coefficient

  • Kendall’s Tau () Example

  • Example


Summarizing Multi-Vari Data: Multi-Vari Charts

  • Show patterns of variation from several possible causes on a single chart, or set of charts

  • Obtains a first look at the process stability over time. Can be constructed in various ways to get the “best view”.

    • Positional: variation within a part or process

    • Cyclical: variation between consecutive parts or process steps

    • Temporal: Time variability


Graphical Tool: Multi-Vari Charts

Cus. Size: 1 = small

2 = large

Product:1 = Consumer

2 = Manuf.

Cus. Type: 1 = Gov’t

2 = Commercial

3 = Education

http://www.qimacros.com/qiwizard/multivari-chart.html


Graphical Tool: Multi-Vari Charts

Minitab:

Stat

Quality Tools

Multi Vari Chart


  • Login