Where do we apply Statistical Methods?

October 22th, 2010

Tags: statistical analysis, t test, anova, Design of Experiments, regression, multiple regression, linear regression, analytics

Before starting any type of analysis classify the data set as either continuous or attribute, and in many cases it is a blend of both types.  Continuous data is characterized by variables that can be measured on a continuous scale such as time, temperature, strength, or monetary value.  A test is to divide the value in half and see if it still makes sense.

Attribute, or discrete, data can be associated with a defined grouping and then counted.  Examples are classifications of good and bad, location, vendors’ materials, product or process types, and scales of satisfaction such as poor, fair, good, and excellent.  Once an item is classified it can be counted and the frequency of occurrence can be determined.

The next determination to make is whether the data is an input variable or an output variable.  Output variables are often called the CTQs (critical to quality characteristics) or performance measures.  Input variables are what drive the resultant outcomes.  We generally characterize a product, process, or service delivery outcome (the Y) by some function of the input variables X1,X2,X3,…Xn.  The Y’s are driven by the X’s.

The Y outcomes can be either continuous or discrete data.  Examples of continuous Y’s are cycle time, cost, and productivity.  Examples of discrete Y’s are delivery performance (late or on time), invoice accuracy (accurate, not accurate), and application errors (wrong address, misspelled name, missing age, etc.).

The X inputs can also be either continuous or discrete.  Examples of continuous X’s are temperature, pressure, speed, and volume.  Examples of discrete X’s are process (intake, examination, treatment, and discharge), product type (A, B, C, and D), and vendor material (A, B, C, and D).

Another set of X inputs to always consider are the stratification factors.  These are variables that may influence the product, process, or service delivery performance and should not be overlooked.  If we capture this information during data collection we can study it to determine if it makes a difference or not.  Examples are time of day, day of the week, month of the year, season, location, region, or shift.

Now that the inputs can be sorted from the outputs and the data can be classified as either continuous or discrete the selection of the statistical tool to apply boils down to answering the question, “What is it that we want to know?”  The following is a list of common questions and we’ll address each one separately.

  • What is the baseline performance?
  • Did the adjustments made to the process, product, or service delivery make a difference?
  • Are there any relationships between the multiple input X’s and the output Y’s?  If there are relationships do they make a significant difference?

That’s enough questions to be statistically dangerous so let’s begin by tackling them one at a time.

What is baseline performance?

  • Discrete Data
    Plot the data in a time based sequence using a P Chart (percent defective chart), C Chart (count of defects chart), nP Chart (Sample n times percent defective chart), or a U Chart (defectives per unit chart).  The centerline provides the baseline average performance.  The upper and lower control limits estimate 3 standard deviations of performance above and below the average, which accounts for 99.73% of all expected activity over time.  You will have an estimate of the worst and best case scenarios before any improvements are administered.  Create a Pareto Chart to view a distribution of the categories and their frequencies of occurrence.  If the control charts exhibit only normal natural patterns of variation over time (only common cause variation, no special causes) the centerline, or average value, establishes the capability.

    Minitab Statistical Software Tools are Attributes Control Charts and Pareto Analysis.

Did the adjustments made to the process, product, or service delivery make a difference?

  • Discrete X – Continuous Y
    To test if two group averages (5W-30 vs. Synthetic Oil) impact gas mileage, use a T-Test.  If there are potential environmental concerns that may influence the test results use a Paired T-Test.  Plot the results on a Boxplot and evaluate the T statistics with the p-values to make a decision (p-values less than or equal to 0.05 signify that a difference exists with at least a 95% confidence that it is true).  If there is a difference choose the group with the best overall average to meet the goal.

    To test if two or more group averages (5W-30, 5W-40, 10W-30, 10W-40, or Synthetic) impact gas mileage use ANOVA (analysis of variance).  Randomize the order of the testing to minimize any time dependent environmental influences on the test results.  Plot the results on a Boxplot or Histogram and evaluate the F statistics with the p-values to make a decision (p-values less than or equal to 0.05 signify that a difference exists with at least a 95% confidence that it is true).  If there is a difference choose the group with the best overall average to meet the goal.

    In either of the above cases to test to see if there is a difference in the variation caused by the inputs as they impact the output use a Test for Equal Variances (homogeneity of variance).  Use the p-values to make a decision (p-values less than or equal to 0.05 signify that a difference exists with at least a 95% confidence that it is true).  If there is a difference choose the group with the lowest standard deviation.

    Minitab Statistical Software Tools are 2 Sample T-Test, Paired T-Test, ANOVA, and Test for Equal Variances, Boxplot, Histogram, and Graphical Summary.
  • Continuous X – Continuous Y
    Plot the input X versus the output Y using a Scatter Plot or if there are multiple input X variables use a Matrix Plot.  The plot provides a graphical representation of the relationship between the variables.  If it appears that a relationship may exist, between one or more of the X input variables and the output Y variable, conduct a Linear Regression of one input X versus one output Y.  Repeat as necessary for each X – Y relationship.

    The Linear Regression Model provides an R2 statistic, an F statistic, and the p-value.  To be significant for a single X-Y relationship the R2 should be greater than 0.36 (36% of the variation in the output Y is explained by the observed changes in the input X), the F should be much greater than 1, and the p-value should be 0.05 or less.

    Minitab Statistical Software Tools are Scatter Plot, Matrix Plot, and Fitted Line Plot.
  • Discrete X – Discrete Y
    In this type of analysis categories, or groups, are compared to other categories, or groups.  For example, “Which cruise line had the highest customer satisfaction?” The discrete X variables are (RCI, Carnival, and Princess Cruise Lines).  The discrete Y variables are the frequency of responses from passengers on their satisfaction surveys by category (poor, fair, good, very good, and excellent) that relate to their vacation experience.

    Conduct a cross tab table analysis, or Chi Square analysis, to evaluate if there were differences in levels of satisfaction by passengers based upon the cruise line they vacationed on.  Percentages are used for the evaluation and the Chi Square analysis provides a p-value to further quantify whether or not the differences are significant. The overall p-value associated with the Chi Square analysis should be 0.05 or less.  The variables that have the largest contribution to the Chi Square statistic drive the observed differences.

    Minitab Statistical Software Tools are Table Analysis, Matrix Analysis, and Chi Square Analysis.
  • Continuous X – Discrete Y
    Does the cost per gallon of fuel influence consumer satisfaction?  The continuous X is the cost per gallon of fuel.  The discrete Y is the consumer satisfaction rating (unhappy, indifferent, or happy).  Plot the data using Dot Plots stratified on Y.  The statistical method is a Logistic Regression.  Once again the p-values are used to validate that a significant difference either exists, or it doesn’t.  P-values that are 0.05 or less mean that we have at least a 95% confidence that a significant difference exists.  Use the most frequently occurring ratings to make your determination.

    Minitab Statistical Software Tools are Dot Plots stratified on Y and Logistic Regression Analysis.

Are there any relationships between the multiple input X’s and the output Y’s?  If there are relationships do they make a difference?

  • Continuous X – Continuous Y
    The graphical analysis is a Matrix Scatter Plot where multiple input X’s can be evaluated against the output Y characteristic.  The statistical analysis method is multiple regression.  Evaluate the scatter plots to look for relationships between the X input variables and the output Y.  Also, look for multicolinearity where one input X variable is correlated with another input X variable.  This is analogous to double dipping so we identify those conflicting inputs and systematically remove them from the model.

    Multiple regression is a powerful tool, but requires proceeding with caution.  Run the model with all variables included then review the T statistics (T absolute value <=1 is not significant) and F statistics (F <=1 is not significant) to identify the first set of insignificant variables to remove from the model.  During the second iteration of the regression model turn on the variance inflation factors, or VIFs, which are used to quantify potential multicolinearity issues (VIFs <5 are OK, VIFs > 5 to 10 are issues).  Review the Matrix Plot to identify X’s related to other X’s.  Remove the variables with the high VIFs and the largest p-values, but only remove one of the related X variables within a questionable pair. Review the remaining p-values and remove variables with large p-values >>0.05 from the model.  Don’t be surprised if this process requires a few more iterations.

    When the multiple regression model is finalized all VIFs will be less than 5 and all p-values will be less than 0.05.  The R2 value should be 90% or greater.  This is a significant model and the regression equation can now be used for making predictions as long as we keep the input variables within the min and max range values that were used to create the model.

    Minitab Statistical Software Tools are Regression Analysis, Step Wise Regression Analysis, Scatter Plots, Matrix Plots, Fitted Line Plots, Graphical Summary, and Histograms.
  • Discrete X  and Continuous X – Continuous Y
    This situation requires the use of designed experiments.  Discrete and continuous X’s can be used as the input variables, but the settings for them are predetermined in the design of the experiment.  The analysis method is ANOVA which was previously mentioned.

    Here is an example.  The goal is to reduce the number of unpopped kernels of popping corn in a bag of popped pop corn (the output Y).  Discrete X’s could be the brand of popping corn, type of oil, and shape of the popping vessel.  Continuous X’s could be amount of oil, amount of popping corn, cooking time, and cooking temperature.  Specific settings for each of the input X’s are selected and incorporated into the statistical experiment.

    Minitab Statistical Software Tools are DOE, Factorial Plots, Pareto Effect Plots, ANOVA, Histograms, and Response Optimizer.

You are now ready to tackle some data, answer some questions, and become statistically dangerous.

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