Discover data visualisation tools, learn the principle underlying hypothesis testing and sample size calculations. Harness the power of design of Experiments "DOE" techniques to control variation and maximise data information.
Bundle Introduction to DOE
Upon completion of this module, participants will be able to:
- Understand the difference between descriptive & inferential statistics
- Appreciate the value of exploratory methods in preliminary data analysis & design of experiments «DOE»
- Explore, characterize and identify problems and trends in data using plots
- Use descriptive statistics to summarize data
- Understand the concepts of hypothesis testing: risks, p-value, confidence intervals, power
- Identify the appropriate statistical test based on the study objective
- Understand the importance of sample size calculations and the required input parameters[/li]
- Understand the importance of statistical design of experiments and benefits in R&D
- Learn the experimental designs most widely used in practice
- Choose an appropriate experimental design based on the study objectives
- Construct and implement the design selected
- Analyze the data collected based on the design used and its underlying assumptions
- Interpret the results of the experiment and report the conclusions
This module is aimed at all scientific staff who wish to design and implement efficient studies and experiments and who must make decisions based on the data collected.
This module introduces key concepts 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.
- The importance of statistics
- Descriptive statistics
- Importance of identifying the type & role of variables
- Visualising and summarising data distributions
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plotting Data: Histograms, Scatter, box-plots, bar charts
- What is statistical inference?
- Hypothesis testing principles: Null and alternative hyposhesis, one vs. two-tailed tests
- Risk involved in significance testing
- Test statistics: T-test, F-tests...
- Observed significance level or "p-value"
- Statistical significance & decision rules
- The importance of sample size calculations
- Statistical inference with confidence Intervals
- Numerical application to the single sample case
- Sources of Variation
- Why Design an Experiment?
- Measurement Variability and Error
- The Notion of Experimental Unit
- Controlling and Minimizing Variability: Replication, Randomization, Blocking and Controls
- Integrating Experimental & Budgetary Constraints into the Experimental Design
- Constructing Experimental Designs
- Two-Sample Designs (Complete Randomized Design, Paired Comparison Design)
- Factorial Designs for more than Two Groups (Unreplicated and Replicated)
- Statistical Analysis Tools
- Exploratory Analysis
- Student's T-Test (Independent and Paired T-Test)
- Analysis of Variance (ANOVA) / F-Test
- The Notion of Interactions between Factors
- Locating Statistical Differences with Multiple Comparison Techniques
- Understanding and Interpreting Results from Real Data
Recommended Duration: 3 days
posted by Lenita Johnson
GREAT class for the analyst who isn't, and never will be, a statistician. I learned so much in such a short time and have already had the opportunity to apply what I learned. Thanks for taking the complex math out of statistics and putting it into English for me.
posted by Zhenhao Qi
The instructor of the training course has made the complicated statistical theory a lot easy to understand and follow, I also find the course materials can be immediately applied on the real world cases.
posted by Mohammed Wasef
It was very educational to learn the stat stools available on Excel. The instructor was able to use real world and job specific examples to make the various statistical options more relevant.
posted by Murrray Galambos
I found the course very informative in that it put the whole theory of stats into a program that I could understand and make sense of. Natalie did an excellent job of explaining things using situations that applied to our business. I would recommend this course to anyone wanting to learn how to use stats to analyse data.
posted by Kelly.Henderson
This course was a great refresher in statistics for me. The presentation and organization of the course material allowed for a good understanding of the components useful to a food production company. I now can write better experiments, use descriptive and inferential statistics effectively and have more confidence reporting results and making conclusions.
I would highly recomend this course to anyone who wants to add more power to their project results and recommendations.
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