Course Identification

Statistical principles in the analysis of research data
20183082

Lecturers and Teaching Assistants

Dr. Ron Rotkopf
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Course Schedule and Location

2018
Second Semester
Monday, 11:15 - 13:00, WSoS, Rm B
19/03/2018

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; 2.00 points
Life Sciences (Systems Biology Track): 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): 2.00 points

Comments

This course aims to familiarize students with executing a variety of statistical tests: t-tests, ANOVA and its different variations, multiple linear regression and more. The final assignment will contain several datasets, for which you will have to determine the appropriate statistical tests, execute the tests and report the results.

There will be no lectures on 14/5, 18/6 and 25/6.
There will be a lecture on 21/5.

Prerequisites

The course "Introduction to Statistics for Life Sciences" is a recommended, but not obligatory, prerequisite for this course.

Restrictions

40

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

100%

Evaluation Type

Final assignment

Scheduled date 1

N/A
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-
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Estimated Weekly Independent Workload (in hours)

3

Syllabus

This course aims to familiarize students with executing a variety of statistical tests: t-tests, ANOVA and its different variations, multiple linear regression and more. The principle aim of the course is to enable students to understand which analysis is applicable for each type of data, and execute the proper analyses using R or other statistical software. The main focus will be on usability and application of statistical knowledge in answering research questions, and less on the mathematical background of the statistical methods. No background in programming is needed.
 
Topics by week:
  1. Introduction to R
  2. Descriptive statistics
  3. Comparing two populations: t-test.
  4. Comparing two populations - non-parametric tests
  5. One-way ANOVA
  6. Multiple comparisons and contrasts
  7. Two-way ANOVA
  8. Experimental design - randomized block, nested, split-plot
  9. Experimental design - repeated measures
  10. Data transformation, power calculations
  11. Linear regression
  12. Correlation + selected topics

Learning Outcomes

Upon successful completion of this course students should be able to:

  1. Determine the appropriate statistical analysis for a given experimental setting.
  2. Plan experiments while considering statistical power, sufficient sample size, and avoiding potential statistical "pitfalls".
  3. Execute a variety of common statistical tests (e.g., t-tests, ANOVA and its different variations, multiple linear regression, non-parametric analysis) using R (although the knowledge obtained will be applicable for commercial statistical software as well).
  4. Critically review statistical analyses conducted in published papers.

Reading List

  1. Sokal, Robert R. Biometry: the principles and practice of statistics in biological research.
  2. Jolicoeur, P. Introduction to Biometry (available as e-book).
  3. Software: The course will use R, which is freely accessible. Commercial statistics packages you can use on your own include Statistica, Systat, SPSS, JMP, and Stata. Less user-friendly options are SAS, which is available at Weizmann via a site license, and Matlab.

Website

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