Classical Methods in Data Analysis
Topics are: types of data, location and variability measures, samples and populations, distributions, confidence intervals, hypothesis testing, comparing two or more means or proportions (parametric and non-parametric methods), and relationships between two variables (correlation, simple linear regression). The course also includes an extensive discussion of the multiple linear regression model. This is an ideal course for anyone who wishes to further his/her medical education by getting a better understanding of data analysis.
Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.
By the end of the course you will…
- have insight in the √n law and its consequences for sample size
- have insight in the general principles of decision procedures (“testing”), and be able to apply these procedures in practice using common statistical packages (SPSS, R)
- understand the principles of the following statistical analysis techniques: Student T tests (1-sample, 2-sample and paired), Analysis of Variance (1-way and 2-way ANOVA), Simple and multiple linear regression analysis, 1-sample, 2-sample and paired proportion tests (χ 2 test for goodness-of-fit, Pearson’s χ 2 test and McNemar’s χ 2 test)
- know in which situations these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
- be able to apply these techniques using common statistical packages (SPSS, R)
- have insight in the Kolmogorov Smirnov test (normal distribution) and the Fisher test for equality of variances and be able to apply these tests in practice using common statistical packages (SPSS, R)
- understand the results obtained with these techniques, and be able to apply these results in practice (e.g. in answering a study questions
- be familiar with the terms ‘explained variance’ and multi-collinearity
- understand the principles of model reduction in regression analysis
- understand the basic principles of the technique of logistic regression analysis
- be able to choose the appropriate non-parametric technique to be applied in case of non-normally distributed data, and understand the principles of these methods.