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Workshop: Statistical Analysis with R

Workshop/ Training/ Webinar

Statistical Analysis with R
  • Date

    01 - 08 Feb 2023

  • Organiser


  • Time

    14:30 - 17:00

  • Venue

    Online (MS Teams)  


Date: 1 Feb 2023 (Wed) & 8 Feb 2023 (Wed)

Time: 14:30 - 17:00

Venue: Online via MS Teams

Target Audience: All Students

Medium of Instruction: English


  • Basic R programming skill
  • Basic knowledge of statistics

Course Description:

  • Understand how to summarize and organize characteristics of a dataset by finding means, medians, standard deviations, frequencies and proportions for variables
  • Understand how to inspect the data graphically and statistically by R plotting functions
  • Understand how to find p-values for validating a hypothesis against observed data
  • Understand how to calculate confidence intervals for measuring the degree of uncertainty or certainty
  • Understand how to perform t-tests, z-tests, one factor ANOVA and Chi-square tests for drawing conclusions based on your data
  • Understand how to examine the association between two variables


Course Outline:

  • Lesson 1
  • Importing data into R from an external file
  • Accessing individual variables from an imported data
  • Creating categorical variables from an imported data
  • Finding means, medians and standard deviations for all the variables in the dataset
  • Finding means and standard deviations for subgroups
  • Finding frequencies and proportions for categorical variables
  • Inspecting the data graphically and statistically by R plotting functions
    • Histogram
    • Box Plot
    • Bar Chart
    • Scatter Plot
  • Statistical table functions in R
    • The normal distribution
    • The standard normal distribution
    • T-distribution
    • Chi-squared distribution
  • Confidence intervals
    • Single Group
    • Comparing Means
    • Comparing Frequencies
  • Lesson 2
  • T-tests for means to determine whether a process actually has an effect on the population of interest, or whether two groups are different from one another
    • The one-sample t-test for a mean
    • The independent samples t-test to compare two means
    • The paired samples t-test
  • Z-tests for proportions to evaluate whether a finding or an association is statistically significant or not
    • One-sample z-test for a proportion
    • Two-sample z-test comparing two proportions
  • One factor ANOVA comparing means across several groups to determine whether there are any statistically significant differentiations
  • Chi-square tests for categorical outcomes to compare observed results and expected results
  • Examining the association between two measurement variables
    • Scatter Plots
    • Correlation
  • Practical coursework throughout the workshop

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