Training offering

Fast Lane Deutschland

IBM SPSS Statistics: Adv. Topics in Regression and Discriminant Analysis V19

Information

Length: 1.0 Day
Course code: 0L019G
Delivery method: Classroom
Price: 790 EUR
This training is available on request.
Please contact us by phone or email at :
+49 40 253346-10
info@flane.de

Overview

IBM SPSS Statistics: Advanced Topics in Regression and Discriminant Analysis (V19) is a one day instructor-led course that provides a practical, application-oriented introduction to some of the advanced statistical methods available in IBM® SPSS® Statistics for data analysts and researchers. Students will review several advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret their output.

Public

This advanced course is for:

  • Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the more advanced statistical capabilities.
  • Anyone who has a solid understanding of statistics and wants to expand their knowledge of appropriate statistical procedures and how to set them up using IBM SPSS Statistics.
  • Analysts and Modelers

Prerequisits

You should have:

  • On the job statistical experience or completion of the Introduction to Statistical Analysis Using IBM SPSS Statistics course and/or Intermediate-level statistics oriented courses.
  • Knowledge of basic statistics, including linear regression.
  • IBM SPSS Statistics Standard, IBM SPSS Statistics Professional, IBM SPSS Statistics Premium.

Objective

Please refer to course overview for description information.

Topics

Discriminant Analysis

  • Explain the basic theory of discriminant analysis and how cases are classified
  • Specify a two-group discriminant analysis and interpret the resulting output
  • Complete additional analysis and validation of the discriminant model

Binary Logistic Regression

  • Explain the basic theory and assumptions of logistic regression
  • Specify a logistic regression analysis
  • Interpret model fit, logistic regression coefficients and model accuracy

Multinomial Logistic Regression

  • Explain the basic theory of multinomial logistic regression
  • Specify a multinomial logistic regression analysis
  • Interpret model fit, logistic regression coefficients and model accuracy

Cox Regression

  • Explain the general principles of Cox regression
  • Specify a Cox regression analysis and interpret the resulting tabular and graphical output
  • Test the assumption of proportional hazards
  • Specify a Cox regression with time-varying covariate for variables that don't meet the assumption of proportionality