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The course provides an introduction to statistical methodology and supplies a number of statistical techniques important for practical data analysis. Examples from the medical and biological fields will be used in exercises. Datasets will be analyzed on the computer using the statistical package SPSS. The first part covers the "Basics of Biostatistics", statistical testing for one and two samples, confidence intervals, simple linear regression and correlation, one way analysis of variance, binomial distribution and proportions, analysis of contingency tables and non-parametric statistics. The second part covers an "Introduction to Modeling" and introduces the most important regression models used in biomedical research, that can be used in the study of the relation between a number of explanatory variables on the one hand, and the occurrence of an outcome on the other: multiple regression and logistic regression.

*Note*: Some basic statistical notions will be assumed known, such as measures of location (mean, median) and scale (variance, standard deviation), the normal distribution, standard error of the mean. They will not explicitly be repeated in the course, but will be used implicitly. If necessary, participants should refresh their knowledge using a statistics book used in previous courses, or using the statistics e-books from CAST.**Course dates 2013-2014: **2 December 2013 - 13 December 2013 (Exam will be on 20 December 2013)**Max. participants: **25. *The maximum number of participants is reached! You will be placed on a waiting list.*

**Introduction**The course provides an introduction to statistical methodology and supplies a number of statistical techniques important for practical data analysis. Examples from the medical and biological fields will be used in exercises. Datasets will be analyzed on the computer using the statistical packages SPSS. Some of these examples are:

- Is there a superiority of streptomycin in the treatment of patients with tuberculosis?
- Take babies longer to learn to crawl in cold months, when they are often bundled in clothes that restrict their movement, than in warmer months?
- Is there an effect of temperature on the size of the cobweb from the spider
*Achaearanea tepidariorum*? - Are there any differences on the maximum concentration, time to maximum concentration and total amount of drug between immediate release codeine (IRC) and sustained release codeine (SRC) for pain relief medications?
- Are the size and weight of your brain indicators of your mental capacity?
- What are the risk factors for the Proventricular Dilatation Disease (PDD) disease in parrots?
- What is the effect of global distribution of physicians and nurses on the maternal mortality rate?
- Which factors influence the level of mercury contamination in our water?
- and many more

**Time schedule**The first part covers the "Basics of Biostatistics", statistical testing for one and two samples, confidence intervals, simple linear regression and correlation, one way analysis of variance, binomial distribution and proportions, analysis of contingency tables and non-parametric statistics. The second part covers an "Introduction to Modeling" and introduces the most important regression models used in biomedical research, that can be used in the study of the relation between a number of explanatory variables on the one hand, and the occurrence of an outcome on the other: multiple regression and logistic regression. The programme per lecture is as follows:

- Lecture 1: confidence intervals, theory of testing, one sample t-test, t-test for 2 independent samples, paired t-test;
- Lecture 2: comparisons of more than 2 samples: one-way analysis of variance, factorial designs, multiple comparisons, assumptions, nonparametric statistics (Mann-Whitney, Wilcoxon signed rank test, Kruskal-Wallis test);
- Lecture 3: association: correlation, simple regression analysis, link between regression and ANOVA, assumptions, Spearman rank correlation;
- Lecture 4:.. comparing two proportions in a unpaired or paired design, chi-square test, assumptions, Fisher exact test, chi-square goodness-of-fit test;
- Lecture 5: modelling continuous data: multiple regression, modelbuilding, multicollinearity, assumptions;
- Lecture 6: modeling binary data: logistic regression, modelbuilding, likelihood, odds ratio, dummy variables;
- Lecture 7: Case study with presentation.

The other three days of the course will be scheduled for self study, exam preparation and the final exam.

**Study material**During the course there will be a workbook available with all the transparencies of the lectures and the exercises (with answers) of the computer sessions. A few days before the start of the course you can buy the workbook from the studysociety M.B.V. Mebiose.

Besides the workbook you can use also one of the following statistical books:

- W.W. Daniel,
*Biostatistics: Basic Concepts and Methodology for the Health Sciences,*International student version, 9th Edition. John Wiley & Sons, 2010; - P. Armitage, G. Berry and J.N.S. Matthews,
*Statistical Methods in Medical Research,*4^{th}edition. Wiley-Blackwell, 2001; - M.C. Whitlock and D. Schluter.
*The Analysis of Biological Data*. Roberts and Company Publishers, 2009; - J.H. Zar,
*Biostatistical Analysis,*5th Edition. Pearson Education International, 2010.

The first two books have more medical examples, whereas the last two books more biological examples. These reference books remain useful long after you have completed the course. They can be obtained from scientific bookstores. You are not obliged to use any of these books during the course.

Brief manuals of SPSS are included in the workbook of the course. If you are interested in more detailed manuals we recommend one of the following books:

- A. de Vocht,
*Basishandboek SPSS 16,*Bijleveld, 2008; - A. Field,
*Discovering Statistics Using SPSS,*3^{rd }edition. SAGE Publications Ltd, 2009; - The very extended help function of SPSS itself.

An overview of all kinds of statistical hyperlinks (books, applets, software et cetera) on the internet is also given in the workbook of the course.**Prerequisite knowledge**Although active statistical knowledge is not a prerequisite, we assume some basic knowledge on statistics and mathematics acquired through, for example, courses in biostatistics in the bachelor programme or self study.

The basic knowledge we assume are: 1) the concepts of population and sample; 2) histogram, boxplot, frequency table, scatterplot, contingency table; 3) mean, median, mode; 4) variance, standard deviation, range, interquartile range, standard error of the mean; 5) probability, probability distributions (especially the normal distribution).

If you want to refresh your basic knowledge we recommend one of the following sources of information (see also study material):

- Chapters 1 till 5 of the book of W.W. Daniel;
- Chapters 1 till 3 of the book of P. Armitage, G. Berry and J.N.S. Matthews;
- Chapters 1 2, 3, 5 and 10 of the book of M.C. Whitlock and D. Schluter;
- Chapters 1 till 6 of the book of J.H. Zar;
- Module A and B of the Dutch E-learning software COSO(ComputerOndersteund StatistiekOnderwijs). It requires the (free) software authorware to run;
- Module I, II, III, V of the very advanced online textbook Online Statbook;
- Modules 1 and 2 (e-book) and/or module 1 (exercises) of CAST (Computer-Assisted Statistics Textbooks) that consists of a collection of electronic textbooks (e-books).

Two weeks before the start of the course there will be a test available for testing your basic knowledge on statistics and mathematics. It is then also possible to ask questions with respect to the basic knowledge to the lecturers. Both will be features of the Blackboard environment of this course.

**Learning objectives**At the end of the course the student:

- has knowledge of the role that statistics plays in academic research;
- has knowledge of basic statistical techniques that are used to analyze data, and knows the conditions under which they are appropriate;
- has insight in which techniques are applicable in which situation;
- can apply these techniques by hand and by using statistical software (SPSS);
- is able to interpret the results from the statistical analysis;
- can report these results in the context of the research question.

**Examination**The examination consists of two parts, namely: 1) a case study with presentation and 2) a final test consisting of 3 - 4 open questions. The case study counts for 25% and the final test for 75% in the final grade. The final grade must be at least 5.5 to pass for the course. An individual part may have a lower grade than 5.5.