Whats New in Minitab 14

1
Whats New in Minitab 14
March 31, 2004Presented by
theITC Research Computing Support Group
Kathy Gerber, Ed Hall, Katherine
Holcomb, Tim F. Jost Tolson

• Whats New in Minitab Today!
• LabVIEW Question Answer session April 8, 230
  430 PM
• Computing with the IMSL Scientific Libraries
  Serial and Parallel April 14, 330 PM

2
Whats New in Minitab 14

• Getting started
• Graphing data
• Using data getting results
• Statistical process control
• Partial Least Squares Regression
• Design of Experiments

3
Getting Started

• Help Resources
• Meet MINITAB
• MINITAB Help
• MINITAB StatGuide
• Tutorials
• Help-to-Go http//www.minitab.com/support/docs/rel
  14/14helpfiles/default.aspx

4
Graph Features in MINITAB

• A pictorial gallery from which to choose a graph
  type
• Flexibility in customizing graphs, from
  subsetting of data to specifying titles and
  footnotes
• Ability to change most graph elements, such as
  fonts, symbols, lines, placement of tick marks,
  and data display, after the graph is created
• Ability to automatically update graphs

5
Enhancements to Specific Graphs

• Multiple levels of categorical variables.
• Contour plots use color ramps and label contour
  lines.
• Use summarized data in making a bar chart.
• Quartile, hinge, or percentile methods for
  boxplot.
• Fit regression lines and distributions to
  selected graphs.
•  

6
Empirical CDF

• You can use Empirical CDF (empirical cumulative
  distribution function) graphs to evaluate the fit
  of a distribution to your data or to compare
  different sample distributions.

7
Individual Value Plot

• View the distribution of individual values,
  with optional grouping by categorical variables.

8
Area Graph

• Use to evaluate trends in multiple time
  series as well as each series' contribution to
  the sum. Minitab can generate calendar values,
  clock values, or index values for the time scale,
  or you can use your own column of stamp values.

9
Residual fourpack
Display a layout of all four residual plots
instead of producing them separately.
10
Data Limits and Details

• A worksheet can contain up to 4000 columns, 1000
  constants, and up to 10,000,000 rows depending on
  how much memory your computer has.
• Three stored constants have default values (you
  can change them if you wish)
• K998 (missing), K999 2.71828 (e), and
  K1000 3.14159 (pi)

11
ReportPad

• The ReportPad acts as a simple text editor
  (like Notepad), from which you can quickly
  print or save in RTF (rich text) or HTML (Web)
  format.
• In ReportPad, you can
• Store MINITAB results and graphs in a single
  document
• Add comments and headings
• Rearrange your output
• Change font sizes
• Print entire output from an analysis
• Create Web-ready reports

12
Session Window

• Enabling the Command Line At the menu select
  Editor, Enable Commands
• Use in conjunction with History in the Project
  Management window

13
Statistical Process Control
14
Multivariate Control Charts

• Multivariate control charting has several
  advantages over creating multiple univariate
  charts
• The actual control region of the related
  variables is represented (elliptical for
  bivariate case).
• You can maintain a specific Type I error.
• A single control limit determines whether the
  process is in control.
• However, multivariate charts are more difficult
  to interpret than classic Shewhart control
  charts.

15
Example of T2 chart

• You are a hospital manager interested in
  monitoring patient satisfaction ratings through
  the month of January. You randomly ask 5 patients
  each day to complete a short questionnaire about
  their stay at the hospital before they check out.
  Because satisfaction and length of stay are
  correlated, you create a T2 chart to
  simultaneously monitor satisfactions ratings (on
  scale of 1-7) and length of stay (in days).

16
Example of T2 chart (cont.)

• 1    Open the worksheet HOSPITAL.MTW.
• 2    Choose Stat gt Control Charts gt Multivariate
  Charts gt Tsquared.
• 3    In Variables, enter Stay Satisfaction.
• 4    In Subgroup sizes, enter a number or a
  column of subscripts, then click OK.

17
Additional New Multivariate Control Charts

• Generalized variance control chart
• Multivariate exponentially weighted moving
  average chart
• Tsquared-generalized variance control chart

18
Partial Least Squares Regression

• Use partial least squares (PLS) to perform
  biased, non-least squares regression with one or
  more responses. PLS is particularly useful when
  your predictors are highly collinear or you have
  more predictors than observations and ordinary
  least squares regression either fails or produces
  coefficients with high standard errors. PLS
  reduces the number of predictors to a set of
  uncorrelated components and performs least
  squares regression on these components.
• PLS fits multiple response variables in a
  single model. Because PLS models the responses in
  a multivariate way, the results may differ
  significantly from those calculated for the
  responses individually. Model multiple responses
  together only if they are correlated.

19
Example of Partial Least Squares Regression

• You are a wine producer who wants to know how
  the chemical composition of your wine relates to
  sensory evaluations. You have 37 Pinot Noir wine
  samples, each described by 17 elemental
  concentrations (Cd, Mo, Mn, Ni, Cu, Al, Ba, Cr,
  Sr, Pb, B, Mg, Si, Na, Ca, P, K) and a score on
  the wine's aroma from a panel of judges. You want
  to predict the aroma score from the 17 elements
  and determine that PLS is an appropriate
  technique because the ratio of samples to
  predictors is low.

20
Example of Partial Least Squares Regression
(cont.)

• 1    Open the worksheet WINEAROMA.MTW.
• 2    Choose Stat gt Regression gt Partial Least
  Squares.
• 3    In Responses, enter Aroma.
• 4    In Predictors, enter Cd-K.
• 5    In Maximum number of components, type 17.
• 6    Click Validation, then choose Leave-one-out.
  Click OK.
• 7    Click Graphs, then check Model selection
  plot, Response plot, Std Coefficient plot,
  Distance plot, Residual versus leverage plot, and
  Loading plot. Uncheck Coefficient plot. Click OK
  in each dialog box.

21
Example of Partial Least Squares Regression
(cont.)

• Session Commands
• WOPEN "winearoma.mtw"
• PLS 'aroma' 'Cd'-'K'
• NComponents 17
• XValidation 1
• GSelectionPlot
• GFit
• GCCoefficient
• GDistance
• GLeverage
• GLoading
• RSelection.

22
Interpreting Results

• Predicted Residual Sum of Squares
• Example details provide interpretation of both
  the Session Window and the Graph Window outputs
• See Minitab Help for the PLS example

23
Upcoming Talks

• Talks are online at www.itc.virginia.edu/research/
  talks
• Computing with the IMSL Scientific Libraries.
  Wednesday, April 14