• Identify common and special causes of variation in medical processes.
• Use the seven main SPC tools to analyze and improve process quality.
• Construct, interpret and apply Control Charts by variables, including mean and range, mean and standard deviation, medians and range, and single values with moving ranges. median and range, and individual values with moving ranges.
• Manage Control Charts for attributes, such as fraction of nonconformances, number of nonconformances, and other attributes relevant to the medical industry. other relevant attributes in the medical industry.
• Contribute to the continuous improvement of medical processes by applying statistical techniques and tools learned during the course. learned during the course.
• Make informed decisions based on the interpretation of Control Chart results to maintain quality in medical industry processes. in medical industry processes.
• Apply the knowledge acquired to manage and improve the quality of processes in their specific working environment within the medical field.

1. Statistical Process Control and Continuous Improvement
   • Introduction to Statistical Process Control
   • Variation: Common Causes and Special Causes
   • The seven main tools of SPC
2. Control Charting, a Tool for Process Control
   • Benefits of Control Charting
   • Statistical Basis for Control Charting
   • Considerations for Sampling implementation
3. Control Charts by Variables
4. Control Charts for attributes
5. Considerations for Control Chart Implementation

At The End Of The Course You Will Be Able To

• Make decisions through the interpretation of data from applied examples.

• Determine the appropriate methods for data collection.

• Understand the distribution of normal data and the main problems of rounding, outliers, mixed populations, and truncated data.

• Evaluate the normality of the data through the application of examples.

• Learn about the process for performing capacity analysis.

• Know the appropriate statistical values for a process to be capable of meeting specifications.

• Know the main tools that can be used in software to determine the capacity of a process.

• Interpret through the application of examples, when a process is capable or not of complying with the specifications.

• Perform transformations on non-normal data.

• Apply the knowledge acquired to solve multiple real case situations throughout the class.

Course Content

1. Statistical Techniques Applied in Manufacturing

• Statistical Concepts
  • Confidence Level
  • Confidence Intervals
• Sampling Plans
  • Attribute
  • Variable
• Normality
  • Normality Assessment
  • Outliers
  • Rounding
  • Mixed Populations
  • Truncated data
  • Population shifts
  • Data Analysis
• Capability Analysis
  • Cp and Cpk Concepts
  • Pp and Ppk Concepts
  • Difference between Pp vs Cp & Ppk vs Cpk
• Data Transformation
  • Non normal data
  • Distribution fit Identification
  • Box-Cox Transformation
  • Johnson Transformation
  • Lognormal Transformation

• Know main concepts required for data collection and statistical analysis.

• Use and interpret descriptive metrics such as mean, mode, median, variance, standard deviation, quartile, percentile, and range.

• Fit data to probability distributions.

• Understand basic distributions characteristics and how it helps to describe the data under analysis using well known probability distributions.

• Use of graphs to describe and rationalize data behaviors.

• Recognize the graphs that are better fit to use under different situations.

• Use graphs to recognize suspicious data behaviors or data collection mistakes that could affect the data credibility, for instance outliers, data from mixed populations, truncated data or rounding problems.

• Identify if a sample mean is statistically similar or not to a reference value through hypothesis testing.

• Make decisions through the interpretation of data from applied examples.

Course Content

1. Introduction to statistics and basic concepts
   • Introduction to statistics and basic concepts
     • General concepts
     • Bias
     • Population and sample
     • Skewness
     • Kurtosis
     • p-value
     • Random Variables
   • Descriptive Statistics
     • Central tendency metrics
     • • Mean
       • Median
       • Mode
     • Variability Metrics
       • Standard deviation
       • Variance
       • Percentiles and quartiles
   • Inference statistics
     • Central limit theorem
       • Hypothesis test
         • One Tailed
         • Bilateral
         • Paired Observations
         • Normality test
2. Graphs and distributions
3. Commonly used graph for data analysis
   • Histogram
   • Boxplot
   • Bar Chart
   • Individual plot
   • Interval
   • Time series
   • Scatter plots
4. Commonly used Distributions

• • Discrete
    • Uniform
    • Bernoulli
    • Binomial
    • Negative Binomial
    • Geometric
    • Poisson
  • Continuous
    • Uniform
    • Exponential
    • Chi- square
    • Gamma
    • Beta
    • Weibull
    • Normal
    • Log-Normal
    • T-student

• Understand the main concepts related with the Design of Experiment process and analysis.
• Set an experimentation plan.
• Perform experiments in order to discriminate the variables that are relevant to the characteristic under analysis, including full factorials and fractional.
• Isolate the experiments from factors that could mislead the decision-making process and are not of interest in the analysis,    through the proper experiment planning and using blocking and confusion as experimentation tools.
• Correctly Interpret experiment results.
• Analysis of non-linear responses and optimization approaches.

Course Content

1. Introduction to DOE and terminology
2. 2n Factorials
3. Fractional
4. Blocking and Confusion
5. Center Points DOE
6. Surface Response
7. DOE Planning
8. DOE Practice