Singapore University of Social Sciences

Statistical Analysis

Statistical Analysis (MTH212)

Applications Open: 01 October 2024

Applications Close: 15 November 2024

Next Available Intake: January 2025

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1392 View More Details on Fees

Area of Interest: Science and Technology

Schemes: Alumni Continuing Education (ACE)

Funding: To be confirmed

School/Department: School of Science and Technology


Synopsis

Embark on the exploration into the world of data analysis through statistical fundamentals with MTH212 Statistical Analysis. This course introduces essential statistical concepts, inference methods for making decisions using data and the interpretation of data. Through the examination of real-world examples and practical exercises, students will develop the skills to understand, analyse and interpret data across diverse contexts. Furthermore, students will be guided in the application of software for practical data analysis and in the evaluation of the performance of various statistical methods. Upon completion of the course, students will possess a strong understanding of elementary statistics, enabling them to confidently analyse data, comprehend statistical methods, and make well-informed decisions based on their analytical findings.

Level: 2
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Descriptive statistics
  • Data visualisation
  • Discrete probability distributions
  • Continuous probability distributions
  • Sampling methods
  • Estimation population parameters
  • Sample statistics
  • Confidence intervals
  • Hypothesis testing
  • One-way analysis of variance
  • Correlation analysis
  • Linear regression models

Learning Outcome

  • Describe data using descriptive statistics
  • Show estimations for population parameters
  • Apply suitable hypothesis testing for making decisions using data
  • Interpret the results from hypothesis tests and confidence intervals
  • Implement linear regression models and evaluate the performance
  • Use software to perform data analysis
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