Inferential Statistics

This course is part of Methods and Statistics in Social Sciences Specialization

Instructors: Annemarie Zand Scholten +1 more

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Skills you'll gain

  •   Regression Analysis
  •   Sampling (Statistics)
  •   Statistical Methods
  •   Probability Distribution
  •   Quantitative Research
  •   Statistical Inference
  •   R Programming
  •   Probability & Statistics
  •   Statistical Analysis
  •   Statistical Hypothesis Testing
  • There are 8 modules in this course

    We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. Normally you would also learn how to perform these tests using freely available software R. Due to technical issues we are not able to do so. We will try to offer this again soon. For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).

    Comparing two groups

    Categorical association

    Simple regression

    Multiple regression

    Analysis of variance

    Non-parametric tests

    Exam time!

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