Dear All, I have prepared this presentation to get a better understanding of Statistical Process Control (SPC). This is a very informative presentation and giving information about the History of SPC, the basics of SPC, the PDCA approach, the Benefits of SPC, application of 7-QC tools for problem-solving. You can follow this technique in your day to day business working to solve the problems. Thanking you.
What is MSA .
1. Why we Need MSA
2. How to use data.
3.Measurement Error Sources of Variation
• Precision (Resolution, Repeat ability, Reproducibility)
•Accuracy (Bias, Stability, Linearity)
4.What is Gage R&R?
5.Explain MSA Sheet
Dear All, I have prepared this presentation to get a better understanding of Statistical Process Control (SPC). This is a very informative presentation and giving information about the History of SPC, the basics of SPC, the PDCA approach, the Benefits of SPC, application of 7-QC tools for problem-solving. You can follow this technique in your day to day business working to solve the problems. Thanking you.
What is MSA .
1. Why we Need MSA
2. How to use data.
3.Measurement Error Sources of Variation
• Precision (Resolution, Repeat ability, Reproducibility)
•Accuracy (Bias, Stability, Linearity)
4.What is Gage R&R?
5.Explain MSA Sheet
Measuremen Systems Analysis Training ModuleFrank-G. Adler
The Six Sigma Measurement Systems Analysis (MSA) Training Module includes a MS PowerPoint Presentation including 62 slides covering an Introduction to Measurement Systems Analysis - Relevance - Discrimination - Accuracy - Stability - Linearity - Precision, Variable Gage R&R Study, and Attribute Gage R&R Study.
This presentation give you a brief knowledge of, how statistical process control applied in our daily lives, how it works and some of its important formulas,
ABOUT THE TRAINING PROGRAM :-
Failure Mode and Effects Analysis or FMEA is a structured technique to analyze a process to determine shortcomings and opportunities for improvement. By assessing the severity of a potential failure, the likelihood that the failure will occur, and the chance of detecting the failure, dozens or even hundreds of potential issues can be prioritized for improvement.
DESIGNED FOR :-
Sr. Engineer, Engineer, Supervisor and Foreman engaged in maintenance, operation, Store, Supply chain, Quality, Safety and Engineering activities.
OBJECTIVE :-
Employees completing this training will be able to effectively participate on an FMEA team and can make immediate contributions to quality and productivity improvement efforts.
Visit www.shakehandwithlife.in to buy this Book. This E-Book on 7QC tools is complete training workshop for Junior, Middle and Senior quality quality professionals. The USP of this workshop is the text and graphics in the book for understanding the tools while applying to solve the practial problems. Illustrative worked examples , Construction of tools in Excel like Histogram, Pareto Chart, Scatter Diagram, Control charts are beautifully explained in step step manner. A newcomer in the area of quality can easily understand how the tools be used and applied.
Measuremen Systems Analysis Training ModuleFrank-G. Adler
The Six Sigma Measurement Systems Analysis (MSA) Training Module includes a MS PowerPoint Presentation including 62 slides covering an Introduction to Measurement Systems Analysis - Relevance - Discrimination - Accuracy - Stability - Linearity - Precision, Variable Gage R&R Study, and Attribute Gage R&R Study.
This presentation give you a brief knowledge of, how statistical process control applied in our daily lives, how it works and some of its important formulas,
ABOUT THE TRAINING PROGRAM :-
Failure Mode and Effects Analysis or FMEA is a structured technique to analyze a process to determine shortcomings and opportunities for improvement. By assessing the severity of a potential failure, the likelihood that the failure will occur, and the chance of detecting the failure, dozens or even hundreds of potential issues can be prioritized for improvement.
DESIGNED FOR :-
Sr. Engineer, Engineer, Supervisor and Foreman engaged in maintenance, operation, Store, Supply chain, Quality, Safety and Engineering activities.
OBJECTIVE :-
Employees completing this training will be able to effectively participate on an FMEA team and can make immediate contributions to quality and productivity improvement efforts.
Visit www.shakehandwithlife.in to buy this Book. This E-Book on 7QC tools is complete training workshop for Junior, Middle and Senior quality quality professionals. The USP of this workshop is the text and graphics in the book for understanding the tools while applying to solve the practial problems. Illustrative worked examples , Construction of tools in Excel like Histogram, Pareto Chart, Scatter Diagram, Control charts are beautifully explained in step step manner. A newcomer in the area of quality can easily understand how the tools be used and applied.
Week 2 Project - STAT 3001Student Name Type your name here.docxcockekeshia
Week 2 Project - STAT 3001
Student Name: <Type your name here>
Date: <Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
This assignment is worth a total of 60 points.
Part I. Histograms and Frequency Tables
Instructions
Answers
1. Open the file Diamonds using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about diamonds. What are the names of the variables in this file?
2. Create a histogram for the depth of the diamonds using the Auto-fit option. Paste the chart here. Once your histogram displays, click Turn on Labels to get the height of the bars.
3. Using the information in the above histogram, complete this table. Be sure to include frequency, relative frequency, and cumulative frequency.
Depth
Frequency
Relative Frequency
Cumulative Frequency
57-58.9
59-60.9
61-62.9
63-64.9
a. Using the frequency table above, how many of the diamonds have a depth of 60.9 or less? How do you know?
b. Using the frequency table above, how many of the diamonds have a depth between 59 and 62.9? Show your work.
c. What percent of the diamonds have a depth of 61 or more?
Part II. Comparing Datasets
Instructions
Answers
1. Create a boxplot that compares the color and clarity of the diamonds. Paste it here.
2. Describe the similarities and differences in the data sets. Please be specific to the graph created.
Part III. Finding Descriptive Numbers
Instructions
Answers
3. Open the file named Stowaway (using Datasets and then Elementary Stats, 9th Edition). This gives information on the number of stowaways going west vs east.List all the variables in the dataset.
4. Find the Mean, median, and midrange for the Data in Column 1.
5. Find the Range, variance, and standard deviation for the first column.
6. List any values for the first column that you think may be outliers. Why do you think that?
[Hint: You may want to sort the data and look at the smallest and largest values.]
7. Find the Mean, median, and midrange for the data in Column 2.
8. Find the Range, variance, and standard deviation for the data in Column 2.
9. List any values for the second column that you think may be outliers. Why do you think that?
10. Find the five-number summary for the stowaways data in Columns 1 and 2. You will need to label each of the columns with an appropriate measure in the top row for clarity.
11. Compare number of stowaways going west and east using a boxplot of Columns 1 and 2. Paste your boxplot here
12. Create a histogram for the
Column 1 data and paste it here.
13. Create a histogram for the
Column 2 data and paste it here.
Part IV. Interpreting Statistical Information
The Stowaway data contains two columns, both of which are mea.
MANAGEMENT OF DATABASE INFORMATION SYSTEM
Quering database
Queries are the fastest way to search for information in a database. A query is a database feature that enables the user to display records as well to perform calculations on fields from one or multiple tables.
You can analyze a table or tables by using:-
1. Select query or
2. An action query
Action query:-These are queries that are used to make changes in many records at once. They are mostly used to delete, update, add a group of records from one table to another, or create a new table from another table.
Types of action query in Microsoft Access are:-
1. Update-update data in a table.
2. Append query-add data in a table from one or more tables.
3. Make table Query-Creates a new table from a dynaset
4. Delete query-Delete specified records from one or more tables.
Select query
Is a type of query used for searching and analyzing data in one or more tables. It lets the user specify the search criteria and the records that meet those criteria displayed in a dynaset or analyzed depending on the user requirement.
Creating a selected query
1. Ensure that the database you want to create a query for is open
2. Click the query tab, then new
3. In the new query dialog box, choose either to create a query from in Designing view or using Wizard
4. To design from scratch, click design view. The show table dialo
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Projects
Project (.MPJ) files store the following items:
In a project, you can manipulate data, perform analyses, and generate graphs. Projects contain one or more worksheets.
Worksheets
Graphs
Session window output
Session command history
Dialog box settings
Window layout
Options
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worksheets
In a project, you can manipulate data, perform analyses, and generate graphs. Projects contain one or more worksheets.
Columns of data
Constants
Matrices
Models for response variables
Design objects
Column descriptions
Worksheet descriptions
Worksheet (.MTW) files store the following items:
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Numeric data
Numbers, such as 264 or 5.28125.
Text data
Letters, numbers, spaces, and special characters,
such as Test #4 or North America.
Date/time data
Dates, such as Mar-17-2013, 17-Mar-2013, 3/17/13, or 17/03/13. Times,
such as 08:25:22 AM. Date/time, such as 3/17/13 08:25:22 AM or 17/03/13 08:25:22.
Data types
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Overview
• To organize brainstorming information about the potential causes of a
problem.
• To compare the relative importance of different causes.
• It is also called a C&E diagram, a fishbone diagram, or an Ishikawa diagram.
For example, a hotel manager wants to investigate why guest rooms are not ready for check-in at 4:00 PM. The manager
brainstorms reasons with the improvement team and creates a cause-and-effect diagram to categorize the reasons. The
team can then prioritize problem areas and develop improvement ideas.
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Where to find this analysis
To create a cause-and-effect diagram,
choose
Stat > Quality Tools > Cause-and-Effect
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Data considerations
• The effect must be a dependent variable
For example, the response variable might be the wait time for service. In a cause-and-effect
diagram, the dependent variable is the variable (or effect) that is affected or measured.
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The engineers creates a cause-and-effect diagram
1.Open the sample data, SurfaceFlaws.MTW.
2.Choose Stat > Quality Tools > Cause-and-Effect.
3.Under Causes for Branch 1, select In column from the drop-down menu.
4.In the blank field under Causes for Branch 1, enter Man.
5.Repeat steps 3 and 4 for the next 5 branches.
Enter Machine, Material, Method, Measure, and Enviro in branches 2–6.
6.In Effect, type Surface Flaws.
7.Click OK.
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Sub-Branches
1.Open the sample data, SurfaceFlaws.MTW.
2. Choose Stat > Quality Tools > Cause-and-Effect.
3.Under Causes for Branch 1, select In column from the drop-down menu. In the blank field
under Causes for Branch 1, enter Man.
3. Repeat steps 3 and 4 for the next 5 branches. Enter Machine, Material, Method, Measure,
and Enviro in branches 2–6.
5. Click Sub for Branch 1. Under Causes for Sub-Branch 3, select In column from the drop-
down menu.
6.Click Sub for Branch 1. Under Causes for Sub-Branch 3, select In column from the drop-
down menu.
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Sub-Branches
8. Click Sub for Branch 2. Under Causes for Sub-Branch 4, select In column from the drop-
down menu.
9. In the blank field under Causes for Sub-Branch 4, enter Speed. Click OK.
10. Click Sub for Branch 5. Under Causes for Sub-Branch 1 select In column from the drop-
down menu.
11. In the blank field under Causes for Sub-Branch 1, enter Micrometers. Click OK.
12. In Effect, type Surface Flaws.
13. Click OK.
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Enter and label the causes
NOTE
You can change or add branches after you create a cause-and-effect diagram. Right click on the cause-
and-effect diagram and choose Graph Options.
1. Under Causes, select In column or Constants from the drop-down menu.
•In column: Enter the column that contains the causes. Values in the columns can contain up to 72
characters.
•Constants: Type the list of causes directly into the dialog box. Use a blank space between causes.
If a cause contains multiple words, enclose the cause in double quotation marks. For example,
"Operator training".
NOTE
Odd branches are on top (5, 3, then 1, left to right), and even branches are on the bottom (6, 4, then 2).
To display a diagram with the main branches but no causes, do not enter any columns or type any
causes.
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Enter and label the causes
NOTE
You can change or add branches after you create a cause-and-effect diagram. Right click on the cause-
and-effect diagram and choose Graph Options.
1. Under Causes, select In column or Constants from the drop-down menu.
•In column: Enter the column that contains the causes. Values in the columns can contain up to 72
characters.
•Constants: Type the list of causes directly into the dialog box. Use a blank space between causes.
If a cause contains multiple words, enclose the cause in double quotation marks. For example,
"Operator training".
NOTE
Odd branches are on top (5, 3, then 1, left to right), and even branches are on the bottom (6, 4, then 2).
To display a diagram with the main branches but no causes, do not enter any columns or type any
causes.
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Understanding control charts
What is a control chart?
A control chart indicates when your process is out of control and
helps you identify the presence of special-cause variation. When
special-cause variation is present, your process is not stable and
corrective action is necessary.
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I-MR Chart
1. Open the sample data, DetergentpH.MTW.
2. Choose Stat > Control Charts > Variables Charts for Individuals > I-MR.
3. In Variables, enter pH.
4. Click I-MR Options.
5. On the Tests tab, select 1 point > K standard deviations from center
line (Test 1) and K points in a row on same side of center line.If you are not
sure which tests apply in your specific situation, use Tests 1 and 2.
6. Click OK in each dialog box.
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Scale for I-MR Chart
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Time
Index
Label the x-axis with the number of the subgroup.
Stamp
Label the x-axis with values from a column. In Stamp
columns (1-3, innermost first), enter up to three columns
that contain date/time, numeric, or text values for the scale.
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Axes and ticks
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Axes and Ticks
A: Y-scale axis lines
B: Y-scale major tick
C: Y-scale major tick label
D: Y-scale minor tick
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Gridlines
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Gridlines
A: Horizontal gridline at Y major tick
B: Horizontal gridline at Y minor tick
C: Vertical gridline at X major tick
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Reference lines
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Reference lines
Show reference lines at Y values
Enter the y-scale values to display reference lines at.
Show reference lines at time scale positions
Enter the x-axis values to display reference lines at.
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Overview
Use Xbar-R Chart to monitor the mean and variation of a process
when you have continuous data and subgroup sizes of 8 or less. Use
this control chart to monitor process stability over time so that you can
identify and correct instabilities in a process.
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Data considerations
• The data should be continuous
• The data should be in time order
• The data should be collected at appropriate time intervals
• The data should be in rational subgroups
• The subgroup size should be 8 or fewer observations
• Collect an appropriate amount of data
• The data do not need to be normally distributed
• The observations within each subgroup should not be correlated with
each other
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Xbar-R.
1. Open the sample data, CamshaftLength.MTW.
2. Choose Stat > Control Charts > Variables Charts for Subgroups > Xbar-R.
3. From the drop-down list, select All observations for a chart are in one column and enter Machine 1 Machine
2 Machine 3.
4. In Subgroup sizes, enter Subgroup ID.
5. Click Xbar-R Options.
6. On the Tests tab, select 1 point > K standard deviations from center line (Test 1), K points in a row on
same side of center line (Test 2), and K points in a row within 1 standard deviation of center line (either
side) (Test 7).If you are not sure which tests apply in your specific situation, use Tests 1, 2, and 7 when you first
establish the control limits based on your data. After the control limits are established, you can use the known
values of those limits and Test 7 is no longer needed.
7. Click OK in each dialog box.
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Reference lines
Stat > Control Charts > Variables Charts for Individuals > I-MR > Scale > Reference lines
Show reference lines at Y values
Enter the y-scale values to display reference lines at.
Show reference lines at time scale positions
Enter the x-axis values to display reference lines at.
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Multiple graphs
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Multiple Graphs
Default
Same Y
If you enter more than one column, Minitab
creates a separate control chart for each
column. Select Same Y to use the same y-scale
for each control chart.
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Xbar-R Options : Historical Parameters
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Xbar-R Options > Parameters
Mean
Standard deviation
You can enter a single mean for the entire chart, or you can enter a mean for each stage. Minitab uses the
mean to calculate the center line on the Xbar chart.
You can enter a single standard deviation for the entire chart, or you can enter a standard deviation for
each stage. The standard deviation is used to calculate the control limits on both charts and the center line
on the R chart.
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Specify how to estimate the parameters
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Xbar-R Options > Estimate
Specify subgroups to estimate parameters
1. From the drop-down list, specify whether you want to list the subgroups to omit or the subgroups to
include.
2. List the subgroups. Use a colon to indicate a range of subgroups. Leave a space between each subgroup
or range of subgroups.
Method for estimating standard deviation
Subgroup size > 1
1. Rbar: Rbar is the average of the subgroup ranges. The Rbar method is a common estimate of the
standard deviation and works best with subgroup sizes from 2 to 8.
2. Pooled standard deviation: The pooled standard deviation is the weighted average of subgroup
variances, which gives larger subgroups more influence on the overall estimate.
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Modify the control limits
Stat > Control Charts > Variables Charts for Subgroups > Xbar-R > Xbar-R Options > Limits
Display additional σ limits at
1. From the drop-down list, specify whether you want to list the subgroups to omit or the subgroups to
include.
2. List the subgroups. Use a colon to indicate a range of subgroups. Leave a space between each subgroup
or range of subgroups.
Method for estimating standard deviation
Subgroup size > 1
1. Rbar: Rbar is the average of the subgroup ranges. The Rbar method is a common estimate of the
standard deviation and works best with subgroup sizes from 2 to 8.
2. Pooled standard deviation: The pooled standard deviation is the weighted average of subgroup
variances, which gives larger subgroups more influence on the overall estimate.
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Xbar-S Chart
1. Open the sample data, CanWeight.MTW.
2. Choose Stat > Control Charts > Variables Charts for Subgroups > Xbar-S.
3. From the drop-down list, select All observations for a chart are in one column, enter Weight.
4. In Subgroup sizes, enter Subgroup ID.
5. Click Xbar-S Options.
6. On the Tests tab, select 1 point > K standard deviations from center line (Test 1), K points in a row
on same side of center line (Test 2), and K points in a row within 1 standard deviation of center
line (either side) (Test 7).If you are not sure which tests apply in your specific situation, use Tests 1, 2,
and 7 when you first establish the control limits based on your data. After the control limits are
established, you can use the known values of those limits and Test 7 is no longer needed.
7. Click OK in each dialog box.
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Xbar-S Chart of Weight
Test Results for Xbar Chart of Weight
TEST 1. One point more than 3.00 standard deviations from center line. Test Failed
at points: 3 * WARNING * If graph is updated with new data, the results above may no
longer be correct.
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The supervisor for a call center wants to evaluate the process for answering
customer phone calls. The supervisor records the total number of incoming
calls and the number of unanswered calls for 21 days. The supervisor
creates a P chart to monitor the proportion of unanswered calls.
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P Chart
1. Open the sample data, UnansweredCalls.MTW.
2. Choose Stat > Control Charts > Attributes Charts > P.
3. In Variables, enter Unanswered Calls.
4. In Subgroup sizes, enter Total Calls.
5. Click P Chart Options.
6. On the Tests tab, select 1 point > K standard deviations from center line (Test 1) and K points in a
row on same side of center line (Test 2).If you are not sure which tests apply in your specific situation,
use Tests 1 and 2 when you first establish the control limits based on your data.
7. Click OK in each dialog box.
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The director of quality for a group of hospitals wants to assess the medication
error rate. Examples of errors include delivering medication at the wrong time,
delivering the wrong dose, and delivering the wrong medication. The director
records the number of patients and the number of medication errors each week for
32 weeks.
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1. Open the sample data, MedicationErrors.MTW.
2. Choose Stat > Control Charts > Attributes Charts > U Chart
Diagnostic.
3. In Variables, enter Errors.
4. In Subgroup sizes, enter Patients.
5. Click OK.
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Overview
Use Histogram to examine the shape and spread of your
data. A histogram divides sample values into many
intervals and represents the frequency of data values in
each interval with a bar.
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Data considerations
The sample size should be approximately 20 or greater
A histogram works best when the sample size is at least 20. If the sample size is too small, each bar on
the histogram may not contain enough data points to accurately show the distribution of the data. If the
sample size is less than 20, consider using an Individual value plot instead.
The sample data should be selected randomly
In statistics, random samples are used to make generalizations, or inferences, about a population. If your
data were not collected randomly, your results may not represent the population
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Histogram
A quality control engineer needs to ensure that the caps on shampoo bottles are fastened correctly. If the
caps are fastened too loosely, they may fall off during shipping. If they are fastened too tightly, they may be
too difficult to remove. The target torque value for fastening the caps is 18. The engineer collects a random
sample of 68 bottles and tests the amount of torque that is needed to remove the caps..
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1. Open the sample data, CapTorque.MTW.
2. Choose Graph > Histogram > Simple.
3. In Graph variables, enter Torque.
4. Click OK.
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Histogram with fit lines and groups
1. Open the sample data, PistonLength.MTW.
2. Choose Graph > Histogram > With Fit and Groups.
3. In Graph variables, enter Length.
4. In Categorical variables for grouping (0-3), enter Supplier.
5. Click OK.
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Create a histogram with groups
Graph > Histogram > With Groups
1. In Graph variables, enter the numeric or date/time
column that you want to graph.
2. In Categorical variables for grouping (0-3), enter up
to three columns that define the groups.
1. In Graph variables, enter multiple numeric or
date/time columns that you want to graph.
2. Select Graph variables form groups.
Groups are graph variables
Groups are defined by values in categorical variables
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Overview
Use Pareto Chart to identify the most frequent defects, the most
common causes of defects, or the most frequent causes of
customer complaints. Pareto charts can help to focus improvement
efforts on areas where the largest gains can be made.
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Pareto Chart
1. Open the sample data, ClothingDefect.MTW.
2. Choose Stat > Quality Tools > Pareto Chart.
3. In Defects or attribute data in, enter Defect.
4. In Frequencies in, enter Count.
5. Select Combine remaining defects into one category after
this percent, and enter 95.
6. Click OK.
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A weighted Pareto Chart
1.Open the sample data, ClothingDefect.MTW.
2.Choose Stat > Quality Tools > Pareto Chart.
3.In Defects or attribute data in, enter Defect.
4.In Frequencies in, enter Count*Cost.
5.Select Combine remaining defects into one category after this percent, and enter 95.
6.Click OK.
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Pareto Chart with a By variable
1.Open the sample data, PaintFlaws.MTW.
2.Choose Stat > Quality Tools > Pareto Chart.
3.In Defects or attribute data in, enter Flaws.
4.In BY variable in, enter Shift.
5.Select Default (all on one graph, same ordering of bars).
6.Select Combine remaining defects into one category after this percent, and enter 95.
7.Click OK.
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Overview
A run chart represents your process data over time. Use a run chart to look for evidence of
special-cause variation in your process.
Run Chart to assess differences in measurements
between different operators and different parts
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Data considerations
Operators should measure parts in a random order
Operator and Part factors must be crossed
To ensure that the data collection order does not influence the results, each operator should
measure all parts randomly within a replicate. After all operators measure all parts one time, you
repeat the process for all replicates.
Two factors are crossed when each level of one factor occurs in combination with each level of the
other factor.
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Run Chart
1. Open the sample data, GageData.MTW.
2. Choose Stat > Quality Tools > Gage Study > Gage Run Chart.
3. In Part numbers, enter Part.
4. In Operators, enter Operator.
5. In Measurement data, enter Measurement.
6. Click OK.
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Overview
Use Scatterplot to investigate the relationship between a pair of continuous variables. A
scatterplot displays ordered pairs of x and y variables in a coordinate plane.
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Scatterplot
A medical researcher studies obesity in adolescent girls. Because body fat percentage is difficult
and expensive to measure directly, the researcher wants to determine whether the body mass
index (BMI)—a measurement that is easy to take—is a good predictor of body fat percentage.
The researcher collects BMI, body fat percentage, and other personal variables of 92 adolescent
girls.
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Scatterplot
1. Open the sample data, BodyFatPercentage.MTW.
2. Choose Graph > Scatterplot > Simple.
3. Under Y variables, enter %Fat.
4. Under X variables, enter BMI.
5. Click OK.
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Scatterplot with regression and groups
1. Open the sample data, FlashRecoveryTime.MTW.
2. Choose Graph > Scatterplot > With Regression
and Groups.
3. Under Y variables, enter Flash Recovery.
4. Under X variables, enter Volts After.
5. In Categorical variables for grouping (0-3),
enter Formulation.
6. Click OK.
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Value stream map
Use a value stream map to illustrate the flow of materials and information as a
product or service moves through the value stream.
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Create a value stream map
Insert a value stream map
1. Choose Insert > Value Stream Map.
2. Click a value stream map template, and then click Create. Some templates start with blank shapes and
some do not.
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Add shapes and connectors
To connect the shapes, click Connector, and then choose a connector. To ensure
that shapes stay connected even when you move them, connect the shapes at
their anchor points. Anchor points turn green when they are connected to the
shape.
Start point
End point
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Add shapes and connectors
To connect the shapes, click Connector, and then choose a connector. To ensure
that shapes stay connected even when you move them, connect the shapes at
their anchor points. Anchor points turn green when they are connected to the
shape.
Start point
End point
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Format shapes and connectors
Option Description
Text Change the font type, font style, font size, and color of the text on a shape or
a connector.
Paragraph Change the alignment or apply bullets or numbering to the text on a shape or
a connector.
Lines Change the color, type, and size of a connector or of a shape's outline.
Fill Change the color, pattern, pattern color, or shadow color of the shape or the
connector.
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Add the same shape or connector multiple times
Choose Map > Multi-Insert, and then, in the Shapes gallery, select the shape or connector to add.