Descriptive and Inductive Statistics
Details
| ID | 2858617 |
| Duration | 5.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | no |
| Target group | Data Analysts |
Overview
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of data, or the quantitative description itself. Statistical inference (or inductive statistics) is the process of drawing conclusions from data that is subject to random variation, for example, observational errors or sampling variation. This training provides you with a substantial overview of both descriptive and inductive statistics. All topics are firstly explained in presentations with the fundamental mathematical theory and examples followed secondly by hands-on exercices.
Dates
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- 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
Introduction to Statistics
Descriptive and Inductive Statistics - Uni-/Bi- and Multi-variate Statistics - Summary tables: Grouped data, Frequency distributions, Contingency tables - Statistical graphics: Bar chart, Biplot, Box plot, HistogramDescriptive Statistics: Univariate Analysis
Location: Mean (Arithmetic, Geometric, Harmonic), Median, Mode - Dispersion: Range, Standard deviation, Coefficient of variation, Percentiles, Interquartile range - Shape: Variance, Skewness, Kurtosis, MomentsDescriptive Statistics: Bivariate Analysis
Dependence: Pearson product-moment correlation, Rank correlation (Spearman's rho, Kendall's tau), Partial correlation, Scatter plot - Linear regression: Simple linear regression, Ordinary least squares - Regression analysis: Errors and residuals, Regression model validation, Mixed effects modelsInductive Statistics: 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 diagramInductive Statistics: Probability 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)Inductive Statistics: Frequentist Inference
Unbiased estimators (Mean unbiased minimum variance, Median unbiased) - Confidence interval - Testing hypotheses - Alpha-/Beta-Error and PowerInductive Statistics: Specific 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