Start Dates: TBC
Duration: 4 Days
Full Fee: €2,695
Network Members Fee: €1,610
This course aims to provide sufficient basic knowledge to analyse data in the best way without making any of the common errors. The main headings we will cover during the course are described below. Each section will first be explained theoretically, followed by practical exercises using example data (or data that is provided by the customer). We will use Minitab data analysis software to practice the various analysis. Each participant will be provided with a free 30 day trial version of Minitab. Some of the topics will also be illustrated using MS EXCEL. It is advised that each participant has access to a laptop or desktop PC with Windows 7 or 8.1 installed.
Course Objectives/ Learning outcomes
At the end of the course participants will know what the data analytics
At the end of the course participants will have a fundamental
understanding of how to use the various tools listed below. Even
though we will practice as much as possible, it is advised that the
participants keep practicing.
The knowledge acquired will provide an understanding of how Data
Analysis can be integrated into the company’s reporting and decision
Who is the course for
This course is recommended for all those in an organisation who want
to get a good understanding of Data Analysis, the methodology, the
main tools and tests and when to use them and when not.
In addition, we will spend some time looking at various common errors
you must be aware to avoid wrong results
Introductory Section – first 2 days of the course
Data Analysis Methodology – CRISP-DM (Cross-Industry Standard
Process for Data Mining)
- Business Understanding
- Data understanding
- Data Preparation
Introduction to Minitab and Excel (History in Minitab & Change settings
- Types of data: Continuous, Attribute (Ordinal, Discrete and
- Normal Distribution
- Binomial distribution
- Poisson distribution
Hypothesis testing – Part I
When you want to compare averages or medians of some sample of
data to decide if they are statistically different.
When you want to compare the standard deviation of some sample of
data to decide if their variation is statistically different.
When you want to compare proportions or percentages that came
from different samples of data to decide if they are statistically
- Principles of Hypothesis testing: What is it and why and when
do we use hypothesis testing
- 1 Sample T-Test: for comparing the averages of one sample
against a specific target or historical average
- 2 Sample T-Test: for comparing the averages of two samples
against each other.
- One way ANOVA: for comparing the averages of 3 or more
samples against each other
Pared T-Test: for comparing the averages of two samples that
contain data that is linked in pairs.
Exploratory data analysis via graphical tools
- Time series
- Box and Whiskers
- advanced Section – Continued for another 2 days
Days 3 & 4
Measurement System Analysis (MSA)
This is a technique for understanding the quality of data by challenging
its sources and their potential errors.
- Gage R&R (Repeatability and Reproducibility) study for
- Attribute R&R study for attribute data
Hypothesis Testing – Part II
What is it and why and when do we use hypothesis testing
Levenes Test: for comparing the standard deviations of 2 or more
samples that are not normally distributed
F-Test: for comparing the standard deviations of 2 samples that are
Bartletts Test: for comparing the standard deviations of 3 or more
samples that are normally distributed.
1 Proportion Test: for comparing a proportion against a specific target
or historical proportion
2 Proportions Test: for comparing 2 proportions against each other
Chi-Square Test: for comparing 3 or more proportions against each
Graphical Tools – Part II
Histogram: The data is summarised into bars with the most frequent
values being represented by the higher bars. The overall shape of the
distribution can be assessed.
Probability Plot: used to decide if a sample data fits a specific
Matrix plot: produces and array of scatter plots
Box Plot: Shows the distribution of a sample data as a box and whiskers
Individual value Plot: for comparing the distribution of several samples
against each other
Fitted line Plot: is a scatter plot in which the relationship between the
input and the output is represented mathematically by a single line (a
regression line) can be linear or curved.
Statistical Process Control (SPC)
I-MR chart for analysing individual data points of continuous data
U- Chart: for analysing the count or defects per unit
Xbar R Chart: for analysing the averages of small sub-groups (2 to 5)
P chart: for analysing the proportions or percentages
Xbar S Chart: for analysing the averages of large sub-groups (more than
- Simple regression: model the relationship between one X
variable and a response variable Y
- Multiple Regression: to model the relationship between two to
five X variables and a response variable Y
- Optimise response: using multiple regression to model the
relationship between two to five X variables and a response
variable Y and identify X values that optimise Y.
- Capability Analysis: determine whether the process is capable
of producing output that meets customer requirements.
- Binomial Capability: determine whether the % defective meets
- Poison Capability: determine whether the defect rate (DPU)
meets customer requirements