Course Outline

  • Getting started with SPSS
  • Obtaining, Editing, and saving Statstical output
  • Manipulating Data
  • Descriptive Statistics Procedures
  • Evaluating Score Distribution Assumptions
  • t Tests
  • Univariate Group Differences: Anova and Ancova
  • Multivariate Group Dfferences: Manova
  • Nonparametric procedures for ananlysing frequesncy data
  • Correlations
  • Regression with Quantitative Variables
  • Regression with Categorical Variables
  • Principal Components Analysys and Factor Analysis
 21 Hours

Testimonials (4)

Related Courses

Introduction to Data Visualization with Tidyverse and R

7 Hours

Advanced R

7 Hours

Algorithmic Trading with Python and R

14 Hours

Anomaly Detection with Python and R

14 Hours

Programming with Big Data in R

21 Hours

Cluster Analysis with R and SAS

14 Hours

Data and Analytics - from the ground up

42 Hours

Data Analytics With R

21 Hours

Data Mining with R

14 Hours

Deep Learning for Finance (with R)

28 Hours

Deep Learning for Banking (with R)

28 Hours

Data Mining & Machine Learning with R

14 Hours

Foundation R

7 Hours

Forecasting with R

14 Hours

Introduction to R with Time Series Analysis

21 Hours

Related Categories

1