CloseHelpPrint
Kies de Nederlandse taal
Course module: 2DMT00
2DMT00
Applied statistics
Course info
Course module2DMT00
Credits (ECTS)5
Category-
Course typeGraduate School
Language of instructionEnglish
Offered byEindhoven University of Technology; Mathematics and Computer Science; Mathematics;
Is part of
Computer Science and Engineering
Data Science in Engineering (CSE)
Industrial and Applied Mathematics
Contact persondr. N. Nooraee
Telephone-
E-mailn.nooraee@tue.nl
Lecturer(s)
Co-lecturer
dr. A. Di Bucchianico
Other course modules lecturer
Responsible lecturer
dr. N. Nooraee
Other course modules lecturer
Contactperson for the course
dr. N. Nooraee
Other course modules lecturer
Academic year2016
Period
2  (14/11/2016 to 05/02/2017)
Starting block
2
TimeslotC: C - Tu 1-4, Fr 5-8, Fr 9-10
Course mode
Fulltime
Remarks-
Registration openfrom 15/06/2016 up to and including 23/10/2016
Application procedureYou apply via OSIRIS Student
Explanation-
Registration using OSIRISYes
Registration open for students from other department(s)Yes
Pre-registrationNo
Waiting listNo
Number of insufficient tests-
Number of groups of preference1
Learning objectives
Students should be able to understand the underlying concepts of the statistical analysis methods and the underlying statistical assumptions. They should be able to apply them to real data sets and should know which method to choose when they are confronted the certain data sets
Content
Researchers are often interested in finding out if
  • groups of units (people, products, processes, etc) are different from each other in one or a few number of characteristics
  • variables are related or unrelated to each other
  • a set of variable can be described by one or more other variables
    To answer these questions, researchers should select appropriate statistical approaches. The selection depends on certain assumptions that the researcher is willing to make. The course provides an overview of many of the different parametric and nonparametric methods that are available to analyze data and answer these questions. The statistical methods that will be discussed, after a short overview of basic statistical methods, are measures of effect sizes, measures of correlation, linear regression, goodness-of-fit measures, normality tests, outlier tests, several nonparametric tests, analysis of variance, etc. An overview of topics

    Non-parametric tests for one sample
     
  • Normality test
  • Tests for skewness and kurtosis
  • Chi-square goodness-of-fit test
  • Kolmogorov-Smirnov goodness-of-fit test
  • Runs tests for serial correlation
  • Outlier tests

    Non-parametric tests for two samples (independent and paired)
     
  • Chi-square test
  • Fisher exact test
  • McNemar test
  • Sign test
  • Wilcoxon signed rank test
  • Mann-Whitney test

    More than two samples
     
  • Analysis of variance (balanced and unbalanced)
  • Cohen’s d
  • Diagnostics (homogeneity of variance tests, outlier tests)
  • Multiple comparisons
  • Kruskal-Wallis test
  • Correlation/association/agreement
  • Pearson correlation coefficient
  • Spearman rank
  • Kendall’s tau
  • Kendall coefficient of concordance
  • Odds ratio
  • The contingency coefficient
  • The phi coefficient
  • Cramer’s phi coefficient
  • Cohen’s Kappa
  • Intraclass correlation coefficient

    Regression analysis & AN(C)OVA
     
  • Simple linear regression
  • Multiple linear regression
  • Diagnostics
  • Transformations
  • Two-way anova
  • Ancova
Entrance requirements
Entrance requirements tests
-
Assumed previous knowledge
Basic knowledge in probability and statistics. Student should be aware of hypothesis testing, t-test, estimation, simple linear regression.
Previous knowledge can be gained by
-
Resources for self study
-
Short promotional description of the course
The course focuses on statistical analysis of different types of data. The course will provide both parametric and nonparametric methods for analyzing this type of data
Short promotional description of the course
The course focuses on statistical analysis of different types of data. The course will provide both parametric and nonparametric methods for analyzing this type of data
Bachelor College or Graduate School
Graduate School
Follow-up subjects
2MMS20 Statistics for big data--2DI70 Statistical learning theory--2DD23 Time series analysis and forecasting
Follow-up subjects
2MMS20 Statistics for big data--2DI70 Statistical learning theory--2DD23 Time series analysis and forecasting
Required materials
-
Recommended materials
SAS/STAT 9.2 User’s Guide: Introduction to Nonparametric Analysis, SAS Institute Inc., 2008
SAS/STAT 9.2 User’s Guide: The GLM Procedure, SAS Institute Inc., 2009
SAS/STAT 9.2 User’s Guide: The NPAR1WAY Procedure, SAS Institute Inc., 2009
Outlier Analysis, Charu Aggarwal, Springer, 2013, eBook 978-1-4614-6396-2
Power point slides, real data sets from practice, and handouts from book chapters that are electronically available from the library or from internet.
Data Analysis and Data Display, Richard Heiberger and Burt Holland, Springer, 2004, eBook 978-1-4757-4284-8
Handbook of Parametric and Nonparametric Statistical Procedures, Third Edition, David J. Sheskin. Chapman and Hall/CRC, 2003, eBook ISBN: 978-1-4200-3626-8.
The statistical methods will be explained in SAS (and in R)
Instructional modes
College / course

General
-

Remark
-
Tests
Written
Test weight100
Minimum grade6
Test typeWritten
Number of opportunities2
OpportunitiesBlock 2, Block 3
Test duration in minutes180

Assessment
-

Remark
The assignments will be conducted in groups. The three grades are averaged to obtain a final grade, under the condition

CloseHelpPrint
Kies de Nederlandse taal