Statistics - Inductive Statistics
Details
| ID | 2858620 |
| Duration | 3.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | General knowledge of math |
| Target group | Data Analysts |
Overview
In statistics, statistical inference is the process of drawing conclusions from data that is subject to random variation, for example, observational errors or sampling variation. Statistical induction helps describing systems of procedures that can be used to draw conclusions from datasets arising from systems affected by random variation, such as observational errors, random sampling, or random experimentation. It is then used to test hypotheses and make estimations using sample data. This training covers all the fundamentals of inductive statistics (probability theory, probability distributions and hypotheses testing) which can be used in marketing, controlling and engineering. You will learn theory and the mathematical foundations in lectures with examples and you will train your new knowledge in practical hands-on labs and exercices.
Dates
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Your benefits at a glance
- Flexible preferred date
- Customized content
- Intensive exchange
- High practical relevance
Comelio Media
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Services
- Lunch / catering
- Help with hotel / travel
- Comelio certificate
- Flexible: free cancellation up to one day before
Content
Probability Theory
Probability axioms - Probability space Sample space - Elementary event - Random variable - Probability measure - Complementary event - Joint probability - Marginal probability - Conditional probability - Independence - Conditional independence - Law of total probability - Law of large numbers - Bayes' theorem - Venn diagram - Tree diagramProbability Distributions
Introduction: Probability mass function, Probability density function, Probability distribution function - Discrete univariate distributions: Binomial, Poisson, Geometric, Hypergeometric - Continuous univariate distributions: Uniform, Exponential, Normal (Gaussian)Frequentist Inference
Unbiased estimators (Mean unbiased minimum variance, Median unbiased) - Confidence interval - Testing hypotheses - Alpha-/Beta-Error and PowerSpecific Tests
Z (normal) - Student's t-test - F - Goodness of fit (Chi-squared) - Signed-rank (1-sample, 2-sample, 1-way anova)Instructor
Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.Publications
- Grundlagen empirische Sozialforschung (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9