Data Science / Statistics / R Foundation / Statistics / Descriptive and Inductive Statistics

Statistics - Descriptive and Inductive Statistics

Details

ID 2858617
Classroom 5 days
Webinar 5 days
Method Lecture with examples and exercises.
Prequisite no
Audience Data Analysts

Services:

  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training

Summary

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.

Training Dates

  • 2020-Oct-30 - Oct-29
  • 2021-Jan-08 - Jan-07
  • 2021-Mar-19 - Mar-18
  • 2021-May-28 - May-27

1450 EUR +VAT

Location | Enrollment


Agenda

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, Histogram

Descriptive Statistics: Univariate Analysis

Location: Mean (Arithmetic, Geometric, Harmonic), Median, Mode - Dispersion: Range, Standard deviation, Coefficient of variation, Percentiles, Interquartile range - Shape: Variance, Skewness, Kurtosis, Moments

Descriptive 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 models

Inductive 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 diagram

Inductive 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 Power

Inductive Statistics: Specific Tests

Z (normal) - Student's t-test - F - Goodness of fit (Chi-squared) - ­Signed-rank (1-sample, 2-sample, 1-way anova)

Trainer

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 ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9
Projects

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.

Research

He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.