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- STATISTICAL TOOLS FOR DATA ANALYSIS R INSTALL
- STATISTICAL TOOLS FOR DATA ANALYSIS R UPDATE
- STATISTICAL TOOLS FOR DATA ANALYSIS R FULL
STATISTICAL TOOLS FOR DATA ANALYSIS R INSTALL
If your version of R is older than that, download and install the latest version of R from the R project website for Windows, for MacOS, or for Linux
STATISTICAL TOOLS FOR DATA ANALYSIS R UPDATE
If your R version is 4.0.0 or later, you don’t need to update R for this lesson. Alternatively, you can type sessionInfo() into the console. When you open RStudio your R version will be printed in the console on the bottom left.Meijer.If you already have R and RStudio installed, first check if your R version is up to date: In case the assessment has to take place online, the written (digital) exam will be adjusted to an online written (digital) exam from home. In addition the final exam grade must be higher than 5.5 to pass the course.Īt least 50% of the problems need to be completed to pass the course. The total grade (60% exam, 30% project, 10% Perusall) must be higher than 5.5. The course unit prepares students for the research projects in the second year, in which the learning objectives attained are required as prior knowledge. Priority for students from the MSc EES for whom this course is obligatory. The course unit is compulsory for the MSc EES. Preknowledge: a Bachelor's degree in natural sciences or a related field.The course unit assumes some basic prior knowledge about probability theory and data analysis.įirst-year master students from the MSc EES are participating in the course unit. (This book will be provided via Perusal)ĭanielle Navarro University of New South R and R studio version 1.0 Approximately three questions also containing sub-questions about interpretation of statistical outcomes) Report (30%, project report) Assignment 1 (0%, exercise problems at least 50% of the problems must be completed for a pass of the course) Assignment 2 (10%, Perusall reading assignments).)īook (chapters) (mandatory): Learning statistics with R: A tutorial for psychology students and other beginners edition 6.0 Open textbook library.
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(Written exam (60% computer exam during which a data set must be analysed in R. Opdracht (AST), Schriftelijk tentamen (WE), Verslag (R) (Lectures (25h, computer lectures) Tutorials (16h, Tutorial/Question hours) Assignment 1 (35h, practice problems) Assignment 2 (30h, data set project) Assignment 3 (20h, reading assignment perusall) and Self-study (14h).) Hoorcollege (LC), Opdracht (ASM), Werkcollege (T) The exercises and the project will prepare the students for the computer-based exam. These are reserved for help on the exercises and the project, and will be given by TAs.ĭuring the course, the students will work on individual projects, where every student analyzes a real data set using methods learned in this course and writes a small report. Sections from the book will be assigned to prepare for the lectures.Ĭomputer exercises (not graded) will be assigned to practise the material learned in the lectures.
STATISTICAL TOOLS FOR DATA ANALYSIS R FULL
Bringing a laptop or tablet to the lecture is necessary to tale full advantage of them. The lectures consist of short lecture segments followed by computer exercises to illustrate and try out the concepts explained. The rest of the course covers introductory statistical concepts:Ĭontent: Probability distributions and their properties, the central limit theorem, confidence intervals, basic hypothesis testing (means, variances, 1way ANOVA), experimental uncertainties and error propagation, linear regression. The first two weeks are focused on learning R and exploratory data analysis methods using tidyverse packages. It is less focused on equations and derivation and more on practical exploration of the underlying statistical concepts and their application. This course gives a very hands on introduction to data analysis and statistics using R. Discuss and explain statistical methods covered in this course and the underlying theory. Correctly interpret the outcome of statistical tests and explain limitations of statistical methods.ĥ. Assess which techniques are appropriate for a certain problem in a realistic data set related to energy and environmental science.Ĥ. Solve basic data analysis problems related to probability distributions, error analysis, hypothesis testing, and linear regressions in R.ģ. Use the programming language R to explore large data sets with various filtering and graphical methods.Ģ. At the end of the course, the student is able to:ġ.