This session discusses key concepts in statistics. Classical and more recent exploratory data analysis techniques to efficiently summarise data and to detect outliers are presented. Statistical testing and decision-making in the presence of variation are also discussed.
Statistics for Engineers
Upon completion of this module, participants will be able :
- To distinguish descriptive from inferential statistics
- To appreciate the value of exploratory data analysis methods in preliminary data analysis & in experimental design construction
- To explore, characterise and identify problems and trends in data using plots
- To understand the concepts of hypothesis testing, confidence intervals, risk & power
- To understand the importance of sample size calculations and the required input parameters to estimate a sample size
- To perform simple data more quickly and more accurately
- To interpret results reliably and confidently
This session is aimed at engineers who must make decisions based on that data.
This session introduces the important ideas in statistics and data analysis. It assumes that participants either have no previous knowledge of statistics or that they have not used statistics for a long time.
- Why Do we Need Statistics?
- Descriptive or Exploratory Data Analysis "EDA"
- Overview and Goals
- Importance of Identifying the Type and Role of Variables in Studies
- Visualizing and Summarizing Data: The Concept of a Distribution
- Graphical Tools: histogram, Box-plot
- Numerical Tools: mean, median, standard deviation, standard error, etc.
- Exploring the relationship between two (2) variables
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plots: Scatter plots, box-plots, etc.
- Statistical inference or hypothesis testing
- Overview: What is statistical inference?
- Statistical Inference with Hypothesis Testing:
- Null and alternative hypotheses
- One-tailed vs. two-tailed tests
- Test statistics: t-test, F-tests, etc.
- Observed significance level or "p-value"
- Statistical significance and decision rules
- Risk involved in hypothesis testing
- Risks or type I and II errors
- Confidence level of a test
- Power of test
- The importance of sample size calculations and the required input parameters to estimate a sample size
- Statistical inference with confidence Intervals: interpretation and usage
- Statistical Inference for a Single Sample or Group: Hypothesis Testing vs. Confidence Interval Approach
Recommended Course Duration: 1 day
Course materials :