Applied Statistics

Backing up decisions by means of classical statistical tools and modern alternatives

Course set-up 

This 3-day course will guide the participants through a correct statistical analysis of their results, originating from experiments or other sources. Theory will alternate with hands-on computer exercises. No prior statistical knowledge is required, but affinity with numbers is a definite plus. It is a master’s level course.

Course Contents

  • Descriptive statistics
    • Graphical techniques: scatter plots, histogram, dotplot, boxplot, normal probability plot
    • Descriptive statistics: means, median, variance, IQR, …
    • Describing the similarity between variables: covariance & correlation
    • Autocorrelation
  • Good data collection practice
    • Sampling strategies
    • Paired comparisons
  • Dealing with random variables (probability distributions)
    • Properties of distributions of random variables
    • Distributions for discrete and continuous variables: Binomial, Poisson, normal distribution, Weibull, …
    • Functions of random variables: the z-distribution, χ2, t and the F-distribution
    • Confidence intervals for means, difference in means, variances, proportions, capability indices, …
  • Hypothesis testing
    • Hypothesis testing with confidence intervals
    • Classical hypothesis testing
    • Statistical significant versus practical relevant
    • Type I and Type II errors
    • Power and sample size calculations
  • One-way ANOVA
  • Random effects and Nested ANOVA – Variance Components Analysis (R&r study)
  • Two-way ANOVA
  • Simple Linear Regression
  • Polynomial Regression

Some cases & applications

  • Detecting and proving a change in a process
  • Quantifying and judging the difference between two products or systems
  • Deciding on the equivalence of analysis methods
  • Setting a specification taking the customers’ measurement error into account
  • Calculating the effect of variation in addition and adjustment of a component on the process performance
  • Calculating the number of data needed to detect a certain improvement
  • Investigating the effect of different types of constituents on the product properties
  • Identifying the major source of variation
  • Investigating the effect of a process parameter on a characteristic

Additional benefits

  • Free individual follow-up coaching
  • One year free access to the Statistics in Practice guide through the Sherpa-app; a step-by-step guidance when setting up experiments or analyzing data


Applied Statistics - CQ Consultancy

Table of Contents

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