Applied Statistics

The main purpose of this course is to present categorical data analysis techniques and clustering techniques. In this context regression models of categorical dependent variables (probit, logit, logarithmic regression) and control methods of independent categorical variables will be presented.The methods of multivariate analysis, such as principal components analysis, factor analysis, discriminant analysis and cluster analysis will also be presented. The application of the above will be done by using the statistical package SPSS and Minitab.

Objectives

Upon completion of the learning process the student will be able To know the techniques of clustering data To identify recognize the techniques of analyzing categorical data To properly apply the above techniques on data To combine the techniques according to the data To evaluate and interpret the results of data analysis.

Prerequisites

Adequate knowledge of basic statistics (statistics a and b)

Syllabus

Introduction to Multivariate Statistics Principal Component Analysis Factor Analysis Cluster Analysis Discriminant Analysis Qualitative and Quantitative Data Analysis (Mc Neman Test, Contingency Tabls, Fisher Τest) Weighted Least Squares Method Two stage Regression Analysis Logit/Probit Models, Log Linear Models, Ordinal Models