2. Univariate Data Analysis

• 2.1. Univariate data analysis in context
  • 2.1.1. Usage examples
  • 2.1.2. What we will cover
• 2.2. References and readings
• 2.3. What is variability?
  • 2.3.1. The high cost of variability in your final product
  • 2.3.2. The high cost of variability in your raw materials
  • 2.3.3. Dealing with variability
• 2.4. Histograms and probability distributions
• 2.5. Some terminology
• 2.6. Binary (Bernoulli) distribution
• 2.7. Uniform distribution
• 2.8. The normal distribution and checking for normality
  • 2.8.1. Central limit theorem
  • 2.8.2. Independence
  • 2.8.3. Formal definition for the normal distribution
  • 2.8.4. Checking for normality: using a q-q plot
  • 2.8.5. Introduction to confidence intervals from the normal distribution
• 2.9. The t-distribution
  • 2.9.1. Calculating the t-distribution
  • 2.9.2. Using the t-distribution to calculate our confidence interval
• 2.10. Poisson distribution
• 2.11. Confidence intervals
  • 2.11.1. Interpreting the confidence interval
  • 2.11.2. Confidence interval for the mean from a normal distribution
• 2.12. Testing for differences and similarity
  • 2.12.1. Comparison to a long-term reference set
  • 2.12.2. Comparison when a reference set is not available
• 2.13. Paired tests
• 2.14. Other types of confidence intervals
  • 2.14.1. Confidence interval for the variance
  • 2.14.2. Confidence interval for the ratio of two variances
  • 2.14.3. Confidence interval for proportions: the binomial proportion confidence interval
• 2.15. Statistical tables for the normal- and t-distribution
• 2.16. Exercises