This course covers the fundamental principles and concepts in statistics as well as classical and more recent exploratory data analysis (EDA) techniques to describe data with numerical and graphical tools. The various uses of these methods like outlier detection will be presented. With the help of real-life examples the principles underlying statistical testing and decision-making in the presence of uncertainty will be reviewed. The workshop will also cover risks involved (alpha and beta), p-values and statistical significance. The use and interpretation of confidence intervals will also be discussed. This course is an excellent introductory solid basis for all other training courses.
- The importance of statistics
- Descriptive statistics
- Importance of identifying the type & role of variables
- Visualising and summarising data distributions
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plotting Data: Histograms, Scatter, box-plots, bar charts
- What is statistical inference?
- Hypothesis testing principles: Null and alternative hyposhesis, one vs. two-tailed tests
- Risk involved in significance testing
- Test statistics: T-test, F-tests...
- Observed significance level or "p-value"
- Statistical significance & decision rules
- The importance of sample size calculations
- Statistical inference with confidence Intervals
- Numerical application to the single sample case
- Understand the difference between descriptive & inferential statistics
- Appreciate the value of exploratory methods in preliminary data analysis & design of experiments «DOE»
- Explore, characterise and identify problems and trends in data using plots
- Use descriptive statistics to summarise data
- Understand the concepts of hypothesis testing: risks, p-value, confidence intervals, power
- Identify the appropriate statistical test based on the study objective
- Understand the importance of sample size calculations and the required input parameters
- Analyse data more quickly and more accurately
- Interpret results reliably and confidently
Recommended Duration: 1 day(s)