# Training Data analysis

##### Look for cause-and-effect relationships & correlations in a table of data
Data analysis
e-learning, stats

Duration 14 Hours

250 excl. tax / person

E-Learning has a high drop-out rate, so why do our courses have a success rate of 90 %? The key? Our content! No handouts to put you to sleep.
Kayla is a Green Belt at the B&B factory in Baumé, and is facing some real problems. We need to help her find solutions.
Still need support? We offer tutoring sessions with our Master Black Belts trainers to support you!

### Knowing how to analyse data

The Data Analysis course is lively and interactive. The resources provided will enable you to master the subject perfectly.

## Descriptive statistics, Graphs

• Understand the importance of graphical representation
• Quantitative variables
• Qualitative variables
• Mixed quantitative/qualitative case

## Descriptive statistics, discrete laws

• Fundamentals of probability
• Binomial distribution, Hypergeometric distribution and Poisson distribution
• Control by simple sampling

## Descriptive statistics, continuous laws

• Origin of Gauss's law
• The parameters of a Gaussian distribution
• Validating the normality hypothesis
• Testing for the presence of outliers
• Student's law
• Normality analysis Skewness and Kurtosis
• Law of distribution of averages and confidence interval
• Law of variance distribution and confidence interval

## Inferential Statistics

• The different hypothesis tests
• Alpha and beta risks
• Power of a test

## Frequency comparison

• Various tests
• Comparing a frequency with a theoretical frequency (1P)
• Comparing two frequencies (2P)
• Comparing more than two frequencies (independence table)

## Comparison of averages

• Compare an Average to a theoretical Average (z and theoretical t)
• Compare two averages (t)
• Compare more than two averages (ANAVAR)
• Knowing how to separate matched cases

## Variance comparison

• Comparing a Variance to a Theoretical Variance
• Comparing two Variances
• Compare more than two Variances

## Non-parametric tests

• Understanding the benefits of non-parametric tests
• Principle of the main non-parametric tests
• Simple examples of non-parametric tests (signs and B to C)
• Application to sensory measurements
• Theoretical and matched comparison: Wilcoxon test
• Comparison of two populations: Mann Whitney test
• Comparison of more than two populations Krustal-Wallis, Mood, Friedman, Page test

## Simple regression

• Principles and calculations
• Hypothesis testing of coefficients
• Interpretation of R²
• Non-linear regression

## Multiple regression

• Principles, calculations and interpretation
• The benefits of multiple regression
• Non-linear responses
• Qualitative factors

## Objectives

• Prove the validity of a hypothesis using a statistical test and interpret the result.
• Understand the use of descriptive and inferential statistics.
• Know how to use the right statistical test.
• Understand the difference between simple regression and multiple regression (linear and non-linear).

## For whom

This e-learning course in data analysis is designed for engineers, supervisors and technicians who need to interpret production or test results, or who are looking for cause-and-effect relationships or correlations in a table of data.

## Prerequisites

• Basic use of the Internet and a web browser.
• Level 4 diploma and/or 2 years' initial professional experience.

## Duration

14 hours of animated 100% e-Learning, certification practice quizzes, implementation of theoretical points on industrial simulators. The e-Learning course is available 24/7 for 1 month.