Multivariate Methods

University of Otago

Course Description

  • Course Name

    Multivariate Methods

  • Host University

    University of Otago

  • Location

    Dunedin, New Zealand

  • Area of Study

    Mathematics, Statistics

  • Language Level

    Taught In English

  • Prerequisites

    STAT 110 or STAT 115 or BSNS 102 or BSNS 112

  • Course Level Recommendations

    Upper

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • Credit Points

    18
  • Recommended U.S. Semester Credits
    3 - 4
  • Recommended U.S. Quarter Units
    4 - 6
  • Overview

    Tests of significance for multivariate data, Fisher discriminant function analysis, testing multivariate distances, cluster analysis, principal component analysis, factor analysis both exploratory and confirmatory, discrimination using logistic models when categorical predictors present, canonical correlation analysis, multidimensional scaling and other methods of ordination, correspondence analysis.

    This is a paper in advanced statistical methods. Applications are widespread in the analysis of psychological, sociological and other types of behavioural data, including market research. Other areas of application include medicine, ecology, environmental science, geography and the biological sciences in general. Rather than concentrating on the mathematical aspects of the methods covered, the paper emphasises applications and data analysis through the use of the statistics package SPSS 22 and the AMOS 22 package with a use of R for some of the procedures.

    Course Structure
    Main topics:

    • Multivariate analysis of variance
    • Fisher Discriminant function analysis
    • Logistic and multinomial regression for discrimination
    • Cluster analysis
    • Principal component analysis
    • Exploratory factor analysis
    • Confirmatory factor analysis using AMOS 22
    • Discrimination with logistic models if some categorical predictors
    • Canonical correlation analysis
    • Measures of distance
    • Methods of scaling and ordination
    • Correspondence analysis
    • Repeated measures

    Learning Outcomes
    Students who successfully complete the paper will develop an ability to explore and summarise large data sets.

Course Disclaimer

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