| Responsible Department | Department of Basic Science and Environment | ||||||||||||||||
| Earliest Possible Year | BSc. 1 year to MSc. 2 year | ||||||||||||||||
| Duration | One block | ||||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||||
| Level of Course | Joint BSc and MSc | ||||||||||||||||
| Examination | Final Examination written examination Written Exam in Lecturehall All aids allowed Description of Examination: Four hours written exam Weight: 100% 7-point scale, external examiner Dates of Exam: 28 January 2011 | ||||||||||||||||
| Block Placement | Block 2 Week Structure: B | ||||||||||||||||
| Language of Instruction | English | ||||||||||||||||
| No Credit Points With | Statistisk Dataanalyse 1 Other first courses in statistics | ||||||||||||||||
| Optional Prerequisites | 210002 Mathematics and Data Processing Mathematics at the level of differentiation and integration. R is not assumed known. | ||||||||||||||||
| Restrictions | None | ||||||||||||||||
| Course Content | |||||||||||||||||
| Probabilities, binomial distribution, two-way count tables, normal distribution, fundamental statistical concepts: variation, statistical model, estimation, test, confidence intervals. More advanced analyses use a statistical program (R) and comprise analysis of variance and regression analysis. Emphasis is on a model-based formulation of problems that are based on experimental data or non-experimental investigations, and on the interpretation of the results, including interpretation of significance. As a part of the statistical analysis requested at the exam, the student will need to read and interprete R-programs and output. Supplementary calculations on pocket calculator to a minor extent are expected. | |||||||||||||||||
| Teaching and learning Methods | |||||||||||||||||
| Four types of teaching will be employed: lectures, examples/cases, "classroom exercises" and take-home exercises. At the lectures the general theory will be introduced and reviewed. Emphasis is on understanding the problems, choice of statistical method, and interpretation of the results of the statistical analysis. Examples/cases are first studied (in groups) unsupervised and then followed by discussed in plenum. The classroom exercises are mainly based on biologically relevant problems and often comprise analysis of data. For some of the exercises a PC and the statistiscal program R is used. The homework exercises integrate the use of R with the statistical analysis. Students are encouraged do the homework in groups. | |||||||||||||||||
| Learning Outcome | |||||||||||||||||
| The student should obtain an overview of fundamental statistical concepts and methods and after the course be able to explain and use basic statistical and probabilistic principles, concepts and methods. Thus, after the course, the student should be able to: Knowledge: - explain the concepts probability, distribution, population vs. sample, variation - explain fundamental statistical concepts (for example statistical model, parameter estimation, confidence interval, null hypothesis, test, p-value, significance Skills: - use definitions of and computational rules for probabilities - identify different types of data - use fundamental statistical principles and concepts of statistics including computations of means, standard deviations and variances, estimates, test statistics, confidence intervals and prediction intervals. - use the statistical program R for statistical analyses. Qualifications: - formulate basic scientific questions as statistical hypotheses - perform basic statistical analyses comprised by the course, for example analyses of two-way count tables, one- and two-way analysis of variance, regression analysis - interpret results of statistical analyses and draw conclusions - assess the results of statistical analyses, the assumptions behind them and their limitations. | |||||||||||||||||
| Course Literature | |||||||||||||||||
| The book C. Ekstrøm and H. Sørensen: "Introduction to statistical data analysis for the life sciences" (to appear), used also for the course 210005 "Statistisk Dataanalyse 1", will be used. | |||||||||||||||||
| Course Coordinator | |||||||||||||||||
| Ib Skovgaard, ims@life.ku.dk, Department of Basic Sciences and Environment/Statistik, Phone: 353-32340 | |||||||||||||||||
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