Data Analysis Techniques for Engineers & Technologies
Data Analysis Techniques for Engineers & Technologies
OBJECTIVES
- To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and representation methods for numerical data.
- To give delegates the ability to recognize which types of analysis are best suited to particular types of problems.
- To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions.
- To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field.
- To introduce some basic statistical methods and concepts.
- To explore the use of Excel 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack.
WHO SHOULD ATTEND?
The course has been designed for professionals whose jobs involve the manipulation, representation, interpretation and/or analysis of data. Familiarity with a PC and in particular with Microsoft Excel (2003, 2007, 2010 or 2013) is assumed.
The course involves extensive computer-based data analysis using Excel 2010 and therefore delegates will be expected to be numerate and to enjoy working with numerical data on a computer.
COURSE OUTLINE
Setting the Statistical Scene
- Statistics as an evidence-based management decision support tool
- The process of evidence-based statistical decision making
- Overview of the components of the discipline of Statistics
- Applications review of Statistics for Engineers and Technologists
- Data – Raw material of Statistics – Factors affecting data quality / integrity (sources of data, data gathering approaches (sampling), data types (categorical and numeric), data preparation issues) (all impact on the validity, accuracy, completeness and representativeness of data to address management issues.
Exploratory Data Analysis – Tools to Profile and Describe Sample Data
- Summary and Visualization Tools (Frequency Analysis)
- Pivot tables (One, two-dimensional segmentation of categorical data)
- Visualization of categorical measures (Bar charts and Pie charts)
- Frequency Distributions; Histograms; Cumulative Percentage Charts
- Pareto Analysis (sorted histogram) (80/20 Rule)
- Descriptive Tools to Profile Sample Data (Intensity Analysis)
- Measures of central and non-central location (mean, median, mode; quartiles; percentiles);
- Measures of dispersion (variance, standard deviation, quartile deviation);
- Measure of skewness (skewness coefficient); Identifying and dealing with outliers.
- Visualization of numerical measures (box plot)
- Segmentation analysis of numeric measures (using pivot tables)
Basic Probability Concepts – Measuring Uncertainty
- Basic Probability Types, Rules and Concepts
- Normal Probability Distribution
Inferential Analysis – Statistical Decision Making
- Confidence Intervals – To estimate likely population measures
- Application of Confidence Intervals to Statistical Quality Control (process control charts (R-charts, x(bar)-charts; S-charts); control charts for attribute data; process capability indexes; (SPC XL for Excel)
- Hypothesis Testing – Tests for statistical relationships between measures
- from two samples (t-tests for independent and matched samples)
- from multiple samples (Analysis of Variance ANOVA)
- test for independence of association (the chi-squared test)
Statistical Modelling – Building Models for Prediction Purposes
- The model building process
- Correlation analysis
- Multiple Linear Regression models (Stepwise Regression modelling)
- Modelling with categorical measures
- Curve fitting (polynomial and auto-regressive models)
Basics of Data Mining
- Overview of data mining
- Review of data mining approaches and techniques (Descriptive data mining and Predictive data mining modelling approaches)