Workshop: Statistical Analysis with R
Workshop/ Training/ Webinar

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Date
01 - 08 Feb 2023
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Organiser
ITS
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Time
14:30 - 17:00
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Venue
Online (MS Teams)
Enquiry
IT HelpCentre (Hotline) 2766 5900 / (WhatsApp/ WeChat) 6577 9669
Summary
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
Prerequisite:
- 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