INFORMATION
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The course gives an overview of the basics of applied statistics for researchers,
engineers and students. It is specifically dedicated to those, who having no special
knowledge in statistics, use or would like to use statistical methods in their
practice. The course is built in the way that even a person with no background in
statistics is able to follow it. Special attention is given to understanding of the
basic rules and objects of a statistical investigation and their application. For this
purpose examples will be given throughout the lectures. The course is divided
into 6 three-hour sections, each followed by a small test for a self control. Practical
parts (conducted in Excel) are integrated into the lectures, allowing immediate
application of the theory.
Lecture 1. Data presentation and numerical measures
• Data and statistics• Descriptive statistics: tabular and graphical presentation • Descriptive statistics: numerical measures (mean, median, standard deviation,variance, correlation) • z-score. Chebyshev's theorem. Detection of outliers Lecture 2. Introduction to probability
• Introduction to probability• Calculation of probability • Bayes' theorem Section II. WEDNESDAY November 11 Lecture 3. Probability distributions
• Discrete probability distributions (uniform, binomial, Poisson, hypergeometric)• Continues probability distributions (uniform, Gaussian, exponential) Lecture 4. Practical work with Excel
• Case problem. Usage of the information about distributionSection III. THURSDAY November 12 Lecture 5. Sampling statistics
• Sampling and sampling distribution• Central limit theorem • Means of sum of two normal random variables. Lecture 6. Statistical inference about mean (part 1)
• Interval estimation for the means• Hypothesis tests and decision making • Statistical inference about means and proportions of two populations • Case problem. Formulation and test of hypotheses Section IV. TUESDAY November 24 Lecture 7. Statistical inference about mean (part 2)
• Nonparametric methods for the comparison of means• Number of replicates • Means of sum, product and ratio of two random variable • Hypothesis test in the case of multiple experiments Lecture 8. Statistical inferences about variance
• Interval estimation for the variance• Hypothesis test for the variances of two population • Tests of goodness of fit and independenc Section V. WEDNESDAY November 25 Lecture 9. Analysis of variance (ANOVA)
• One-way ANOVA• Two-way ANOVA Lecture 10. Topics in applied data analysis
• Multi-factor ANOVA• Experimental design • Principle component analysis (PCA) Section VI. THURSDAY November 26 Lecture 11. Regression
• Linear regression. Estimation of the parameters• Nonlinear regression • Quality control • Forecasting Lecture 12. Final remarks
• Verification of the tests• Questions For further information:Dr. Petr NAZAROVMicroarray Center, CRP-Sante, Luxembourg Tel. (+352) 26 970 283 petr.nazarov(at)crp-sante.lu Aurelia DERISCHEBOURGCRP-Sante, Luxembourg Tel. (+352) 26 970 893 aurelia.derischebourg(at)crp-sante.lu |

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