2) graphing using minitab

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Graphing using Graphing using MinitabMinitab

L. Goch – February 2011

AGENDA Why Graph Data? Under STAT

Run ChartPareto ChartMulti-Vari Chart

Under GRAPHScatterplotHistogramBoxplot Individual Value PlotBar ChartPie Chart3D Scatterplot

All Minitab Tutorial Worksheets are located in the folder ‘C:\Program Files\Minitab 16\English\Sample Data’

Graphing using Minitab.mtb

Source: Donald Wheeler: Understanding Variation

WHY GRAPH THE DATA? Graphs help us understand the nature of

variation Graphs make the nature of data more

accessible to the human mind Graphs help display the context of the data Graphs should be the primary presentation tool

in data analysis If you can’t show it graphically, you

probably don’t have a good conclusion Graphs help separate the signal from the noise

Graphical Analysis is also Called Graphical Analysis is also Called DATA MINING!DATA MINING!

RULES FOR EFFECTIVE DATA COLLECTION

Team must follow sampling plan consistently Do a short Pilot Run to test your procedures Note changes in operating conditions that are

not part of the normal or initial operating conditions

Maintain monitors on gauges for key process inputs

Record any events that are out of the ordinary Log data into database quickly Keep a log book

AVAILABLE GRAPH TOOLS

RUN CHART: RUN CHART:

STAT > QUALITY TOOLS > RUN CHART

RUN CHART: STAT > QUALITY TOOLS > RUN CHART Tests for Process Stability by applying some

statistical diagnostic tests to the series

Open worksheet Radon.mtwRadon.mtw

RUN CHART

10987654321

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Sample

Memb

rane

Number of runs about median: 3Expected number of runs: 6.0Longest run about median: 5Approx P-Value for Clustering: 0.022Approx P-Value for Mixtures: 0.978

Number of runs up or down: 5Expected number of runs: 6.3Longest run up or down: 3Approx P-Value for Trends: 0.135Approx P-Value for Oscillation: 0.865

Run Chart of Membrane

PARETO CHART: PARETO CHART:

STAT > QUALITY TOOLS > PARETO CHART

PARETO CHART: STAT > QUALITY TOOLS > PARETO CHART Pareto Charts are an

essential tool to help prioritize improvement targets

Pareto’s allow us to focus on the 20% of the problems that cause 80% of the poor performance

Open worksheet EXH_QC.MTWEXH_QC.MTW

Counts 274 59 43 19 10 8 6 4Percent 64.8 13.9 10.2 4.5 2.4 1.9 1.4 0.9Cum % 64.8 78.7 88.9 93.4 95.7 97.6 99.1 100.0

Defects

Scra

p

Missi

ng S

tuds

Unco

nnec

ted W

ir

Inco

mplet

e Par

t

Defec

tive H

ousi

Leak

y Ga

sket

Missi

ng C

lips

Missi

ng S

crew

s300

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Coun

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Pareto Chart of Defects

PARETO CHARTDefects CountsMissing Screws 274Missing Clips 59Defective Housing 19Leaky Gasket 43Scrap 4Unconnected Wire 8Missing Studs 6Incomplete Part 10

SECOND LEVEL PARETOS We can generate a second level Pareto using the ByBy statement

This breaks down the overall Pareto by time of day

SECOND LEVEL PARETOFlaws PeriodScratch DayScratch DayPeel DayPeel DaySmudge DayScratch DayOther DayOther EveningPeel EveningPeel EveningPeel EveningPeel EveningScratch EveningScratch EveningPeel NightScratch NightSmudge NightScratch NightPeel NightPeel NightPeel NightPeel NightOther NightOther NightScratch NightScratch NightPeel NightScratch NightSmudge NightScratch NightOther NightScratch NightScratch NightPeel WeekendPeel WeekendPeel WeekendSmudge WeekendSmudge WeekendSmudge WeekendOther Weekend

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0

SmudgeOtherScratchPeel

8

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0SmudgeOtherScratchPeel

Period = Day

Flaws

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t

Period = Evening

Period = Night Period = Weekend

PeelScratchOtherSmudge

Flaws

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Pareto Chart of Flaws by Period

MULTI-VARI CHART: MULTI-VARI CHART:

STAT > QUALITY TOOLS > MULTI-VARI CHART

MULTI-VARI CHART: STAT > QUALITY TOOLS > MULTI-VARI CHART

Multi-vari charts are a way of presenting analysis of variance data in a graphical form. The chart displays the means at each factor level for every factor.

Open worksheet Sinter.MTWSinter.MTW

MULTI-VARI CHART

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MetalType

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ngth

100150200

SinterTime

Multi-Vari Chart for Strength by SinterTime - MetalType

SCATTERPLOT: SCATTERPLOT:

GRAPH > SCATTERPLOT

SCATTERPLOT: STAT > SCATTERPLOT Scatterplots study the relationship

between two variablesOpen worksheet Batteries.MTWBatteries.MTW

SCATTERPLOT

1.51.41.31.21.11.00.9

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7.0

6.5

6.0

5.5

5.0

4.5

4.0

3.5

VoltsAfter

Flash

Reco

v

5.25

Scatterplot of FlashRecov vs VoltsAfter

SCATTERPLOT – BY A VARIABLE

1.51.41.31.21.11.00.9

7.5

7.0

6.5

6.0

5.5

5.0

4.5

4.0

3.5

VoltsAfter

Flash

Reco

v

5.25

NewOld

Formulation

Scatterplot of FlashRecov vs VoltsAfter

HISTOGRAM: HISTOGRAM:

GRAPH > HISTOGRAM

CREATING A HISTOGRAM WITH A NORMAL CURVE Graph > Histogram > With Fit

Histograms examine the shape and spread of data

Open worksheet Camshaft.MTWCamshaft.MTW

SMOOTHED (NORMAL) DISTRIBUTION

We can view the data as a smoothed distribution (red line), in this example using the “normal distribution” assumption. It provides an approximation of how the data might look if we were to collect an infinite number of data points. DOES THE DATA FIT THE CURVE??? If not, does another type of distribution fit the data?

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Length

Freq

uenc

yMean 600.1StDev 1.335N 100

Histogram of LengthNormal

SMOOTHED (SKEWED) DISTRIBUTION

We can view the data as a smoothed distribution (red line), in this example using the “skewed distribution” assumption. It provides an approximation of how the data might look if we were to collect an infinite number of data points. DOES THE DATA FIT THE CURVE??? If not, look for groups that may explain the shape of the data?

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uenc

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Loc 600.7Scale 1.068N 100

Histogram of LengthSmallest Extreme Value

CREATING A HISTOGRAM WITH GROUPS Graph > Histogram > With Outline

and Groups Data for the 2 different suppliers is

available.

Still using worksheet Camshaft.MTWCamshaft.MTW


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Supp1Supp2

Variable

Histogram of Camshaft LengthsCamparison of Supplier 1 vs Supplier 2

100 Parts Plotted for Each Supplier


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Histogram of Camshaft LengthsCamparison of Supplier 1 vs Supplier 2

100 Parts Plotted for Each Supplier

BOXPLOT: BOXPLOT:

GRAPH > BOXPLOT

BOXPLOTS: GRAPH > BOXPLOT There is another method of looking at the data that

may be easier to see differences in the distributions Boxplots show the spread and center of the data BE CAREFUL!BE CAREFUL!

The center of the Boxplot is the MEDIANMEDIAN, not the MEANMEAN

Open worksheet Carpet.MTWCarpet.MTW

22.5

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10.0

7.5

5.0

Dura

bility

Boxplot of Durability

75th Percentile

50th Percentile or Median

25th Percentile

NOTE: Outliers will be

displayed as *

BOXPLOTS

We can also generate boxplots by a variable to look at the variation due to that variable

75% to 100%

0% to 25%

Average

BOXPLOTS W/ GROUPS We can also generate boxplots by a variable to look at

the variation due to that variable Data for 4 Experimental Carpet types is available.

Still using worksheet Carpet.MTWCarpet.MTW

BOXPLOTS W/ GROUPS

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Carpet

Dura

bilit

y

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14.4825

Boxplot of Durability

INDIVIDUAL VALUE INDIVIDUAL VALUE PLOT: PLOT: GRAPH > INDIVIDUAL VALUE PLOT

INDIVIDUAL VALUE PLOT: GRAPH > INDIVIDUAL VALE PLOT

Individual Value Plots also show the spread and center of the data

Open worksheet Billiards.MTWBilliards.MTW

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Elast

icIndividual Value Plot of Elastic

INDIVIDUAL VALUE PLOT

We can also generate Individual Value Plots by a variable to look at the variation due to that variable

Average

INDIVIDUAL VALUE PLOT W/ GROUPS We can also generate Individual Value Plots by a

variable to look at the variation due to that variable Data for 2 Additives is available.

Still using worksheet Billiards.MTWBilliards.MTW

INDIVIDUAL VALUE PLOT W/ GROUPS

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Individual Value Plot of Elastic

BAR CHART: BAR CHART:

GRAPH > BAR CHART

BAR CHART: GRAPH > BAR CHART

Bar Charts can be created from:1) Data that needs

to be counted2) Functions of

data(e.g. avg, min, max) OR

3) a Table

BAR CHART: GRAPH > BAR CHART (COUNTS OF UNIQUE VALUES)

Use to chart counts of unique values, clustered by grouping variables.

Open worksheet Exh_QC.MTWExh_QC.MTW

BAR CHART: GRAPH > BAR CHART (COUNTS OF UNIQUE VALUES)

Flaws

Period

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Chart of Flaws, Period

BAR CHART: GRAPH > BAR CHART (A FUNCTION OF A VARIABLE)

Use to chart counts of unique values, clustered by grouping variables.

Still using worksheet Exh_AOV.MTWExh_AOV.MTW

BAR CHART: GRAPH > BAR CHART (A FUNCTION OF A VARIABLE)

TemperatureGlassType

150125100321321321

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n of

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htOu

tput

886.667

13131386

1054.6710351087.33

573.333553572.667

Chart of Mean( LightOutput )

BAR CHART: GRAPH > BAR CHART (VALUES FROM A TABLE)

asdfa

Open worksheet Tires.MTWTires.MTW


CausesBQtr

Leak

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eatin

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eak

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em L

eak

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ture

Q4Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1

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Repa

irsChart of Repairs

We can easily switch the X-axis so that CauseB is plotted within Qtr.


Qtr

CausesB

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Q1

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em L

eak

Valv

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Chart of Repairs

We can easily stack the Causes B into one bar on the X-axis still plotted within Qtr.


Qtr Q4

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Repa

irs

Valve Stem LeakValve Core LeakPunctureLeak From SeatingDamaged SidewallDamaged Liner

CausesB

Chart of Repairs

PIE CHART: PIE CHART:

GRAPH > PIE CHART

PIE CHART: GRAPH > PIE CHART Use to display the proportion of each data

category relative to the whole data set.

Open worksheet Tires.MTWTires.MTW

PIE CHART: GRAPH > PIE CHART

Leak From Seating7.0%

Damaged Liner9.2%

Valve Core Leak12.8%

Damaged Sidewall14.6%

Valve Stem Leak27.6%

Puncture28.8%

Pie Chart of CausesA

3D SCATTERPLOT: 3D SCATTERPLOT:

GRAPH > 3D SCATTERPLOT

3D SCATTERPLOT: GRAPH > 3D SCATTERPLOT

Use to evaluate relationships between three variables at once by plotting data on three axes.

Open worksheet Reheat.MTWReheat.MTW

3D SCATTERPLOT: GRAPH > 3D SCATTERPLOT

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Quality

Time

Temp

AB

Operator

3D Scatterplot of Quality vs Time vs Temp

Us the 3D Graph Tools to Enlarge & Rotate Graph (Check Tools >Toolbars >3D Graph Tools).

CONCENTRATION DIAGRAMS CANNOT BE CREATED IN MINITAB Concentration Diagrams provide a visual

display of occurrences to identify trends Usually a pictorial representation (drawing) of

the product is used as the basis Occurrences are marked on the drawing where

they were noticed for all units reviewed Take a look at the following examples…

A Concentration Diagram is a great tool to Investigate the nature of surface defects

LOOKING FOR PAINT DEFECTS Top View of a Cooktop

x

X = 1 defect

xxx

xx

x xx xx x

xxxxx

xx

xx

x

x

x

x

xx

x

x

xxxx

x

ANNOTATING GRAPHS: ANNOTATING GRAPHS: • To Change Title: Double click on Title, Change Font or

Text, Click ‘OK’.• To Add Subtitle or Footnote: Left Click anywhere on

Graph, Click Add, Select Option to be added. • To Underline Legend Title: Double Click on Legend

box, Left click on ‘Header Font’ tab, Check Underline.• To add data labels: Right Click anywhere on graph,

Left click on ‘Add’, Left click on ‘Data Labels’, Left click on ‘OK’.

• To add Groups to data: Double Click on any Data Point, Select Groups tab, Select column to group by

• To Delete Legend Box: Right click on Legend box, Left Click on ‘Delete’.

• To move the position of a Label: Right Click to select the label you want to move. You may have to Right Click more than once. Right Click, hold and drag the label to the new position.

• To Unslant X-axis Labels: Double click X-axis, select Alignment tab, enter 90 for text angle, Click on ‘OK’.

• To Add Jitter to Data Points: Double click any Data Point, select the Jitter tab, Check Add jitter to direction, Click on ‘OK’.

CONCLUSIONS

Results need to be Supported by data Not based on conjecture or intuition Shown in 1) Graphical & 2) Statistical 1) Graphical & 2) Statistical

formatformat Make sense from an 3) Engineering 3) Engineering

standpointstandpoint

Good Conclusions RequireGood Conclusions Require

Data and Hard Evidence!!Data and Hard Evidence!!

2) graphing using minitab

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