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

Related Courses

Knowledge Discovery in Databases (KDD)

  21 hours

Statistical and Econometric Modelling

  21 hours

Statistical Analysis using SPSS

  21 hours

HR Analytics for Public Organisations

  14 hours

Talent Acquisition Analytics

  14 hours

Econometrics: Eviews and Risk Simulator

  21 hours

Introduction to Data Visualization with Tidyverse and R

  7 hours

R for Data Analysis and Research

  7 hours

Introduction to R

  21 hours

Forecasting with R

  14 hours

Marketing Analytics using R

  21 hours

Neural Network in R

  14 hours

Advanced R Programming

  7 hours

Data Mining with R

  14 hours

Programming with Big Data in R

  21 hours