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Continuous Improvement Toolkit Histogram
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The Continuous Improvement Map
Multivariate Brainstorming IDEF0 Document control Check Sheets** Flowcharting Flow Process Charts** KPIs SCAMPER*** Pugh Matrix Lateral Thinking Reliability Service Blueprints Affinity Diagrams Mind Mapping* Attribute Analysis Morphological Analysis How-How Diagram*** Control Charts Waste Analysis** Time Value Map** Value Stream Mapping** Value Analysis** Suggestion Systems Histogram Activity Networks RACI Matrix Stakeholder Analysis Improvement Roadmaps DMAIC SWOT Analysis Policy Deployment Project Charter PDCA Gantt Charts MOST PERT/CPM Daily Planning Delphi Method Payoff Matrix Relations Mapping Data Mining Just in Time Automation Product Family Matrix Flow Spaghetti** Multi-vari Studies Decision Tree FMEA PDPC RAID Log* Fault Tree Analysis Paired Comparison Traffic Light Assessment TPN Analysis Risk Analysis* Run Charts Scatter Diagram A3 Thinking Importance Urgency Matrix Four Field Matrix Critical-to X Portfolio Matrix Force Field Analysis Decision Balance Sheet Break-even Analysis Voting Quality Function Deployment Pick Chart Gap Analysis* Bottleneck Analysis Cost Benefit Analysis Kaizen Events Control Planning Standard Work Mistake Proofing Quick Changeover Visual Management Simulation TPM 5S Health & Safety Best Practices Pareto Analysis 5 Whys Prioritization Matrix Hypothesis SIPOC* Matrix Diagram Fishbone Diagrams Tree Diagram* Root Cause Analysis Correlation DOE ANOVA Nonparametric Chi-Square Regression Observations Kano Lean Measures Benchmarking*** Interviews Graphical Methods Data collection planner* Questionnaires Probability Distributions MSA Descriptive Statistics Cost of Quality* Sampling Focus Groups Capability Indices Process Yield Project KPIs Normal Distribution
Data Collection Understanding Performance** Implementing Solutions*** Planning & Project Management* Managing Risk Understanding Cause & Effect Designing & Analyzing Processes Group Creativity Selecting & Decision Making
Five Ws Process Redesign Pull Process Mapping OEE
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Histogram
One of the best ways to analyze any process is to plot the data
* X X X X X X X X X X X
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Histogram
A histogram is a graphical way that summarizes the important aspects of the distribution of continuous data
It is a type of bar chart
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Histogram
Histograms are sometimes called Frequency Plots as they show the frequency of continuous data values on a graph
While Pareto charts plot the frequency of count data
Number of occurrence (frequency) |––––––––– Value bins –––––––––|
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Histogram
Histograms can be drawn either vertically or horizontally
|–––––– Frequency ––––––| |–––––– Frequency ––––––|
The height of the column indicates how often that data value occurred
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Histogram
Histograms are widely used in statistics, process improvement, scientific research, economics, and in social and human sciences
Mainly used to explore data as well as to present the data in an easy and understandable manner.
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Histogram
It allows to visually and quickly assess . . .
The central tendency and the amount of spread in the data The shape of the distribution The presence of gaps, outliers or unusual data points
Spread Outliers Gap Center
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Histogram
Spread Outliers Center
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Shows where most of the data exists
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Histogram
Spread Outliers Center
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Enables to quickly identify the spread of the data
Creates a picture of the variation in a process
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Histogram
Spread Outliers Center
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Overall shape shows how the data is distributed
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Histogram
Spread Outliers Center
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Helps to find unusual data points and outliers that may need further investigation
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Histogram
Spread Outliers Center
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Helps to find unusual data points and outliers that may need further investigation Overall shape shows how the data is distributed Enables to quickly identify the spread of the data Shows where most of the data exists
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Histogram
Plotting data in a histogram allows to know . . .
Minimum and maximum values Gaps and outliers The shape of data (symmetric or skewed) Whether it’s unimodal, bimodal or multimodal
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Histogram
Used as the first step to determine the underlying probability distribution of a data set
A way to shape the sample data to make predictions and draw conclusions about an entire population
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Histogram
Histograms are used to identify . . .
Whether you can apply certain statistical tests Patterns that provide clues to certain types of problems Whether variability is within specification limits Whether the process is capable or not Whether there is a shift in the process
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Histogram
Used to verify that the changes made were a real improvement
Before After
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Histogram
Ideal to represent moderate to large amount of data
In practice, a sample size of at least 30 data values would be sufficient
N = 40 N = 14
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Histogram
It may not accurately display the distribution shape if the data size is too small
Dot plots are preferred over histograms when representing small amount of data
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Histogram
How to Construct a Histogram
Collect the data set and prepare it for the analysis
Data C r e a t e a s u m m a r y t a b l e o f t h e d a t a
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Histogram
How to Construct a Histogram
T h e t o t a l w i d t h s h o u l d b e e q u a l t o t h e r a n g e o f t h e d a t a
Draw a horizontal line and divide it into equal intervals or bins (between 7 to 10 intervals)
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Histogram
How to Construct a Histogram
Draw bars above each bin to represent the frequency
- f the data values within each interval
T h e b a r s s h o u l d b e a d j a c e n t w i t h n o g a p s b e t w e e n t h e m ( t o i n d i c a t e t h e c o n t i n u i t y o f t h e d a t a )
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Histogram
How to Construct a Histogram
Indicate the mean of the data and other important information
S u c h a s t h e s t a n d a r d d e v i a t i o n a n d t h e s p e c i f i c a t i o n l i m i t s
Mean
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Histogram
Example – Cable Diameters
0.60 0.58 0.56 0.54 0.52 0.50
20 1 5 1 0 5
Mean 0.5465 StDev 0.01934 N 100
Diameter of cable Frequency
Data source: Minitab
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Histogram
Example – Cable Diameters
0.60 0.58 0.56 0.54 0.52 0.50
5 5
The result can be summarized using day to day language such as: “The distribution looks symmetric around the cable diameter mean (0.546 cm) and appears to fit the Normal Distribution”.
Data source: Minitab
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Histogram
Example – Presence of Diabetes
Mean 99.65 StDev 36.58 N 310
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Histogram
Example – Presence of Diabetes
The distribution of the data is skewed to the right. The distribution is more like an exponential distribution which is normal for this type of data.
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Histogram
Further Information
Histograms, however, can’t see changes and trends over time Histograms like control charts can be used to assess improvement overtime
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Histogram
Further Information
You can illustrate a stratification factor in histograms
Morning shift Evening shift Night shift
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Histogram
Further Information
There are many applications and online services that allow the creation of histograms quickly and automatically (such as Minitab, JMP, and SPSS)
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Histogram
Further Information – One of the 7 Basic Tools of Quality
