 ## Statistical Experiment Design – Design of Experiment (DoE)

### Establishment of research hypotheses

#### What is the population?

• Scale levels: Nominal scale, Ordinal scale, Interval scale, Ratio scale
• Development of experimental plans: screening plans (complete experimental plans, partial factor plans), response surface plans
• Conception of surveys / questionnaires / tests
• Specify sample size / number of subjects / number of groups & control groups
• Conducting surveys / online surveys / experiments / experiments
• Save test results
• Documentation / logging of test results
• Cleanup of data / incorrect data
• Technical implementation of online surveys
• Import / export of data
• Convert data / various file formats
• Recognize first trends with descriptive / descriptive stat.
• Representation of data: histogram, boxplot, scatterplot, pie chart, Q-Q plot, etc.

## Absolute and relative frequencies

Calculation of measures / position parameters: arithmetic mean, median, mode, sums of squares, standard errors, variance, empirical variance, span, quantiles, empirical covariance, correlation coefficient, etc.

• Linear regression
• cluster analysis
• Data Analysis / Data Evaluation (Inductive Stat.)
• Formulate research hypotheses as null hypotheses and alternative hypotheses
• Set significance level
• Choice of statistical test
• Verification of distribution assumptions (eg, normal distribution assumption): chi2 fit test, Kolmogorov-Smirnow test, Shapiro-Wilk test, Anderson-Darling test, etc.
• Confidence intervals / Confidence areas / Confidence intervals / Expected ranges
• Decision rule: interpretation of test size / test statistic, critical value / p-value
• Power analysis, test strength
• Parametric hypothesis tests: t-test, z-test, F-test, chi2 homogeneity test, etc.
• Nonparametric Hypothesis Tests: Wilcoxon Mann Whitney Test, Kruskal Wallis Test, Wilcoxon Sign Rank Test, Friedman Test, etc.
• One-Factor Variance Analysis and Multi-Variance Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA)
• Correlation, partial correlation, pseudo-correlation, rank correlation, Pearson chi2 test
• Linear Regression, Nonlinear Regression, Logistic Regression, Multiple Regression, Moderator Analysis, Mediator Analysis, Generalized Linear Models, Multi-Level Analysis, Path Analysis / Structural Equation Models
• Factor analysis / main axis model / main component model, correspondence analysis
• Time series analysis
• Statistical Modeling
• Basics / background knowledge about stochastics
• Combinatorics, Bernoulli chains
• Random variables, events, probabilities, conditional probabilities, z-values, tabulation of probabilities
• Statistical independence and dependence
• Discrete distributions: binomial distribution, uniform distribution, Poisson distribution
• Continuous distributions: normal distribution, exponential distribution
• Test distributions: distribution, distribution, F distribution

### Courses for Statistical Data Analysis with SPSS, R, Stata, SAS & Excel

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• SPSS
• Stata
• SAS
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• Course Forms – Data Analysis Software Courses
• Group courses (small groups)
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