Statistics with Minitab
Three Day Training Course
IT IS NOT POSSIBLE TO UTILISE SAMPLE DATA WITHOUT STATISTICS. Much time and effort is devoted to the collection of data in industry, for example; quality control measurements, data collected for validation of manufacturing processes, incoming and outgoing inspection data, data produced in the development of products in R&D, etc. It is not possible to get value from this data without using statistics. Many people who do actually use statistical tools such as Statistical Process Control, Design of Experiments, sampling standards, gauge R&R, and other applications don’t understand the underlying statistics. This course is intended to provide that essential understanding so that people will choose the appropriate statistical tools for data analysis and understand the outcome of the analysis.
There are several brands of reasonably priced computer statistical software packages available to assist in the application of statistics, and most people with a reasonable background in maths (example, pass leaving certificate level) can be readily trained to use this software so as to utilise data for continual process improvement, and better decision making.
Minitab software will be used throughout the training course. Delegates will be trained to use both the main menus and the Assistant in Minitab to undertake the analysis that will be met in the Programme set out below. Where the course is presented in-company the programme can be modified to include specific statistical applications.
On successful completion of this training course, delegates should be able to:
- Undertake statistical analysis using Minitab software
- Select appropriate statistical tests such as two-sample t, F-test, ANOVA, etc. for comparing data means and variances
- Calculate and interpret confidence intervals on population parameters
- Determine sample sizes for statistical tests
- Model data using regression analysis
Who Should Attend
- Engineers, technicians, laboratory, R&D, and scientific staff
- All personnel involved in quality control
- All personnel who have a role in analyzing and understanding manufacturing and business data
- Inspection staff
- Personnel who use process improvement techniques in their work
- People planning to attend Six Sigma Black Belt training courses
- People studying for MBA’s and other examinations involving statistics
A prior knowledge of statistics is not required, but participants should have an understanding of mathematical principles; for example, Leaving Certificate maths.
- Outline of the applications of statistics such as Statistical Process Control, Design of Experiments, Sampling, and the relationship with the underlying statistics.
- Explanation of how statistics are used to obtain valuable information on processes from sample data
- Description of statistical terms including population, parameter, random sample, expected value
- Types of data – continuous (variables) and discrete (attributes) data
- Construction of a histogram and explanation of the meaning of frequency distributions, cumulative frequency distributions, measures of dispersion and central tendency
- Graphical methods – box-and-whisker plots, scatter plots
- The normal distribution – testing for normality – Anderson Darling and Ryan Joiner tests
- Normal and Weibull probability plots
- Dealing with non-normal data – Box-Cox and Johnson transformation, distribution fitting using Weibull, Smallest Extreme Value, Largest Extreme Value, etc.
- Central limit theorem and sampling distribution of the mean
- Calculation of the confidence interval for the mean in variables and attribute data.
- Hypothesis testing – tests for means, variances and proportions – Z-test, t-test, 2-sample t-test, F-test, meaning of significance level
- Meaning of the P-value in hypothesis testing and how the rules for assessing P are derived
- Type I and Type II error – difference between statistical and practical significance
- Sample sizes for hypothesis testing – the effect on Power
- Goodness-of-fit tests
- Analysis of variance (ANOVA) – analysis of a designed experiment illustrating the ANOVA – using Tukey’s multiple-sample comparison to compare population means
- Simple and multiple linear regression and correlation. Calculation of the regression equation. Hypothesis testing of the regression statistics. Using the regression model for estimation and prediction. R-squared and R-squared adjusted – the difference between these two statistics.
Minitab will be demonstrated as part of the training so if delegates are in a position to bring along a laptop with Minitab 16 or Minitab 17 pre-loaded (free 30 day trial of Minitab 17 available on www.minitab.com) they can utilise this during the training. If delegates don’t have a laptop, they will still benefit greatly from the programme.
Course Tutor Albert Plant
Albert Plant is a chartered engineer (FIEI) and has a B.Sc. degree in statistics. He has twenty years practical experience training and consulting in statistical applications, including Design of Experiments, SPC, and Sampling. His work covers a wide range of industries including the chemical, pharmaceutical and medical devices industries, food industry, electronics, automotive parts, and other manufacturing.
Albert has used Minitab software since 1993. He is a volunteer tester for Minitab on new versions and he was involved in testing version 17 of Minitab in 2013.
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Course Times 9.00am - 5.00pm
Public Course Cost €995 (includes course documentation, lunch and refreshments)
For In-House courses the tutor will contact you in advance to discuss the course programme in more detail in order to tailor it specifically for your organisation.
Delegates will receive a very comprehensive course manual written by the course tutor. The manual incorporates many exercises that the participants will complete during the training course, and these worked examples, along with the relevant graphical material, will serve as a useful reference when the participants return to their workplace.