Course Outline

Using the program

  • The dialog boxes
    • input / downloading data
    • the concept of variable and measuring scales
    • preparing a database
    • Generate tables and graphs
    • formatting of the report
  • Command language syntax
    • automated analysis
    • storage and modification procedures
    • create their own analytical procedures

Data Analysis

  • descriptive statistics
    • Key terms: eg variable, hypothesis, statistical significance
    • measures of central tendency
    • measures of dispersion
    • measures of central tendency
    • standardization
  • Introduction to research the relationships between variables
    • correlational and experimental methods
  • Summary: This case study and discussion

Requirements

Motivation to learn

 14 Hours

Testimonials (5)

Related Courses

Knowledge Discovery in Databases (KDD)

21 Hours

Introduction to Data Visualization with Tidyverse and R

7 Hours

Econometrics: Eviews and Risk Simulator

21 Hours

HR Analytics for Public Organisations

14 Hours

Statistical Analysis using SPSS

21 Hours

Talent Acquisition Analytics

14 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

R Fundamentals

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

Related Categories