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

### Course Details

## Learning Objectives

Learning Objectives:

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

## Target Audience

Target Audience:

This session is aimed at engineers who must make decisions based on that data.

## Prerequisite

Prerequisite:

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.

## Course Outline

Course Outline:

- 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

- Summary

## Practical Info

Practical Info:
Course notes on the statistical techniques
Sample datasets

**Recommended Course Duration:** 1 day

**Course materials : **