 Part 1. Introduction to MM4XL
 Part 2. MM4XL Tools
 1. Strategic Tools
 BCG Matrix
 Brand Mapping
 Brand Switch
 Decision Tree
 Forecast Manager
 McKinsey Matrix
 Profile Manager
 Quality Manager
 Risk Analyst
 Risk Analyst Expert in a Few Minutes
 Introduction to Decision Analysis
 Introducing Risk Analyst with an example
 1. How to run Risk Analyst
 2. Simulation Never heard of it
 3. Examples
 4. Functions
 1. Property Functions
 2. Utility Functions
 3. Distribution Functions
 mmBETA (Scale, Shape)
 mmBETAGEN (Scale, Shape, [Optional: Lower], [Optional: Upper])
 mmBINOMIAL (Trials, Successes)
 mmCHI2 (Degrees)
 mmDISCRETE (InputRange, Probabilities)
 mmERF (Mean)
 mmERLANG (Scale, Shape)
 mmEXPON (Mean)
 mmEXTVAL (ModalValue, StDeviation)
 mmGAMMA (Scale, Shape)
 mmGAUSSINV (Mean, Scale)
 mmGEO (Trials)
 mmHYPERGEO (Sample, Defects, BatchSize)
 mmINTUNI (Lower, Upper)
 mmLOGISTIC (Mean, StDeviation)
 mmLOGNORMAL (Mean, StDeviation)
 mmNEGBIN (Failures, Successes)
 mmNORMAL (Mean, StDeviation)
 mmPARETO (Location, ModalValue)
 mmPARETO2 (Location, ModalValue)
 mmPERT (Lower, ModalValue, Upper)
 mmPOISSON (Mean)
 mmRANDBETWEEN (Lower, Upper)
 mmRAYLEIGH (ModalValue)
 mmSTUDENT (Degrees)
 mmTRI (Lower, ModalValue, Upper)
 mmUNIFORM (Lower, Upper)
 mmWEIBULL (Life, Shape)
 Probability functions
 Technicalities
 Sources
 2. Analytical Tools
 Business Formulas
 mmBASS, Bass Diffusion Model
 mmBEI, Brand Equity Index
 mmBEP, BreakEven Point
 mmBEPR, BreakEven Point with Fixed Rate of Return
 mmBUYRATE, Purchase Rate Model
 mmCAGR, Compound Annual Growth
 mmCHIp, Chi Squared Test
 mmCODING, Coding of variables
 1. Customer Satisfaction
 2. Database Functions
 mmDHMS, Number to Time
 mmEI, Evolution Index
 mmEXPECT, Expected values
 3. Forecast Errors
 mmGROWTH
 mmGROWTHBACK
 mmGRP, Gross Rating Points
 mmHERF, Herfindahl Index
 mmINTERPOLE, Linear Interpolation
 mmLEARN, Learning Curve
 mmMSAR, Market Share to Advertising Ratio
 4. Opportunity Index
 5. Performance Ranking
 6. Project Management
 mmPREMIUM, Price Premium
 mmPRESS, Product Performance Index
 7. Price Indexes
 8. Queuing Theory
 mmRANGE
 mmREBUY, Repeat Purchase Rate
 mmREBUYS, Estimated Number of RePurchases
 mmRELATIVE
 mmSAMPLE, Sample Size
 mmSAMPLEMIN, Minimum Sample for Significant Values
 mmSEASON, Seasonality Indexes
 mmSHARE
 mmSIGNIF, Significance Test
 mmVARc, Coefficient of Variation
 Cluster Analysis
 CrossTab
 Descriptive Analyst
 Gravitation Analysis
 Proportion Analyst
 Sample Manager
 Segmentation Tree
 Variation Analyst
 3. Charts and Maps
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 Part 1. Introduction to MM4XL
 Part 2. MM4XL Tools
 1. Strategic Tools
 BCG Matrix
 Brand Mapping
 Brand Switch
 Decision Tree
 Forecast Manager
 McKinsey Matrix
 Profile Manager
 Quality Manager
 Risk Analyst
 1. How to run Risk Analyst
 2. Simulation Never heard of it
 3. Examples
 4. Functions
 1. Property Functions
 2. Utility Functions
 3. Distribution Functions
 mmBETA (Scale, Shape)
 mmBETAGEN (Scale, Shape, [Optional: Lower], [Optional: Upper])
 mmBINOMIAL (Trials, Successes)
 mmCHI2 (Degrees)
 mmDISCRETE (InputRange, Probabilities)
 mmERF (Mean)
 mmERLANG (Scale, Shape)
 mmEXPON (Mean)
 mmEXTVAL (ModalValue, StDeviation)
 mmGAMMA (Scale, Shape)
 mmGAUSSINV (Mean, Scale)
 mmGEO (Trials)
 mmHYPERGEO (Sample, Defects, BatchSize)
 mmINTUNI (Lower, Upper)
 mmLOGISTIC (Mean, StDeviation)
 mmLOGNORMAL (Mean, StDeviation)
 mmNEGBIN (Failures, Successes)
 mmNORMAL (Mean, StDeviation)
 mmPARETO (Location, ModalValue)
 mmPARETO2 (Location, ModalValue)
 mmPERT (Lower, ModalValue, Upper)
 mmPOISSON (Mean)
 mmRANDBETWEEN (Lower, Upper)
 mmRAYLEIGH (ModalValue)
 mmSTUDENT (Degrees)
 mmTRI (Lower, ModalValue, Upper)
 mmUNIFORM (Lower, Upper)
 mmWEIBULL (Life, Shape)
 Probability functions
 Risk Analyst Expert in a Few Minutes
 Introduction to Decision Analysis
 Introducing Risk Analyst with an example
 Technicalities
 Sources
 2. Analytical Tools
 Business Formulas
 1. Customer Satisfaction
 2. Database Functions
 3. Forecast Errors
 4. Opportunity Index
 5. Performance Ranking
 6. Project Management
 7. Price Indexes
 8. Queuing Theory
 mmBASS, Bass Diffusion Model
 mmBEI, Brand Equity Index
 mmBEP, BreakEven Point
 mmBEPR, BreakEven Point with Fixed Rate of Return
 mmBUYRATE, Purchase Rate Model
 mmCAGR, Compound Annual Growth
 mmCHIp, Chi Squared Test
 mmCODING, Coding of variables
 mmDHMS, Number to Time
 mmEI, Evolution Index
 mmEXPECT, Expected values
 mmGROWTH
 mmGROWTHBACK
 mmGRP, Gross Rating Points
 mmHERF, Herfindahl Index
 mmINTERPOLE, Linear Interpolation
 mmLEARN, Learning Curve
 mmMSAR, Market Share to Advertising Ratio
 mmPREMIUM, Price Premium
 mmPRESS, Product Performance Index
 mmRANGE
 mmREBUY, Repeat Purchase Rate
 mmREBUYS, Estimated Number of RePurchases
 mmRELATIVE
 mmSAMPLE, Sample Size
 mmSAMPLEMIN, Minimum Sample for Significant Values
 mmSEASON, Seasonality Indexes
 mmSHARE
 mmSIGNIF, Significance Test
 mmVARc, Coefficient of Variation
 Cluster Analysis
 CrossTab
 Descriptive Analyst
 Gravitation Analysis
 Proportion Analyst
 Sample Manager
 Segmentation Tree
 Variation Analyst
 3. Charts and Maps
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mytest > help > Part 2. MM4XL Tools > 1. Strategic Tools > Quality Manager > 1. SPC Attribute Charts > Uchart
Quality Manager Uchart The Uchart works the same way as the Cchart, but is used to control nonconformities in lots rather than in single units. For instance, the daily number of errors in a whole newspaper, the weekly number of orders won with cold calls, the monthly number of transactions in a store, and so on. User selections The picture below shows a Uchart drawn with MM4XL's Quality Manager tool. After selecting Chart type, as shown in the following picture, if you have selected a range with more than one variable (column) in input, choose the variables for Num inspected and Num defectives to analyze, otherwise, the tool will show automatically the data of the first two input series available. If an input range was not selected in window 1, the Cchart will not be available in the list of chart types and the right side of the window below will be blank. Click on Next to go to the window where you select options for printing the results on sheet. Technical notes The control concept of the Uchart is the same as for the Cchart. Read also the material in this help file concerning the Cchart in order to get a clear view of how the Uchart works and what assumptions it sets. The control limits are placed at 3 standard deviations (see field Z ) in the window above and below the average (Ubar) of nonconformities. Measurements falling outside control limits indicate a change in the process. Items beyond limits are highlighted with a red, round marker, as shown in the window above. Input data The input data for the Uchart requires two columns of counts. The picture below shows a suitable data series in the range A1:B26, mind the hidden rows. These can be negative or positive nonconformities (defects) in lots of a given sizes. Tip: In order to speed up the tool, uncheck the Simulate data option when working with long data series. Output results The Uchart can show in output two charts and three tables according to the user selection in the third window (see section Introduction to Quality Manager). The first table, shown below, contains indexes describing the input data in terms of:  Size of the variable: Max, Min, Sum, Range and Counts
 Central tendency: Average, Median, Mode and Standard deviation (of a variable)
 Chart limits: Upper Control Limit (UCL), Cbar, Lower Control Limit (LCL), Sigma
 Z stands for the number of standard deviations where the control limits should be placed
 Sigma is the standard deviation of a subgroup
For the sake of brevity, the second table is not shown here. In 8 columns it shows the details of the chart limits by item. In the picture below, the small, red triangle in the upper right corner of the first column label is a Comment which displays a short message. A number of comments are created by Quality Manager. Place the mouse pointer on the red triangle to display the message. The column Fr/def stands for the fraction of defectives.Sigma stands for the standard deviation, and is found with =SQRT(Ubar/Num inspected). The U Control Chart in the picture below refers to an input variable (thick blue line) presenting one observation outside of the UCL while all other points lie within limits. The thin green line on the left refers to simulated random data produced by Quality Manager, in this case, according to the Poisson distribution. Comparing the random data to the user input data can help you get a visual understanding of the departure of the input data from normality. In this case, both simulated and user data take a shape that does not show any particular sign of an existing trend. Should the peak exceed the control limit again, you should explain the reasons for departure and consider whether to stabilize the process, removing the noise, if any. For a better way to assess normality, read also the material on the Process Capability tool available in Quality Manager. The third table is made of four columns relating to the data fordrawing the histogram chart. The Levels column refers to the intervalsclasses. n, the column on the right, shows the counts for each class. Column Count Exp reports the expected number of items in each class for a normally distributed variable. The histogram in the picture below shows two series:  The blue bars refer to the observed frequency of count classes in the input data. The first bar, for instance, tells us that there are 2 items with 1 nonconformity. The second bar shows 1 count for two nonconformities in the data, and so on. The axis value below the second bar is 2.2 due to a rounding effect. To remove the decimal place, select the horizontal axis with the mouse, doubleclick on it, and on the Figures page of the window that opens, set the Decimal places option to zero.
 The bellshaped red line shows the expected normal curve for avariable with the same range as the input data, and it helps toverify with a quick visual inspection whether the input data followa normal distribution or not. Ucharts, however, follow the Poissondistribution that they tend to approximate normality only with a large number of observations.
