A Highly Interactive Training Course On

Statistical Analysis for Scientific Research

Methods, Tools, and Interpretation Techniques for Academic Excellence

Course Schedule
Certificate
  • Coventry Academy Certificate of Attendance will be provided to delegates who attend and complete the course

Training Overview

Training Overview

Statistical analysis is a key component of scientific research, providing the foundation for data interpretation, validation, and effective communication of results. As research becomes more data-driven, it is increasingly important for researchers, faculty, and postgraduate students to develop strong skills in statistical methods to ensure the credibility and relevance of their work. This Statistical Analysis for Scientific Research training course equips participants with practical experience and knowledge in applying statistical techniques using tools like SPSS, Excel, and R, specifically designed for scientific research across diverse disciplines.

What are the goals?

By the end of this training course, participants will be able to:

  • Understand the role of statistics in the research process
  • Distinguish between descriptive and inferential statistics
  • Conduct appropriate statistical tests and interpret outputs
  • Utilize software tools for data entry, cleaning, and analysis
  • Translate statistical results into meaningful research conclusions

Who is this Training Course for?

This training course is suitable to a wide range of professionals but will greatly benefit:

  • Academic researchers
  • Postgraduate students
  • University faculty
  • Data analysts in research institutions

How will this Training Course be Presented?

This training course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension and retention of the information presented. This includes an interactive mixture of lecture-led learning & group discussions.

The Course Content

Day One: Foundations of Statistical Thinking in Research
  • Introduction to statistics in scientific inquiry
  • Types of data and scales of measurement
  • Designing data collection and sampling techniques
  • Understanding variables and hypotheses
Day Two: Descriptive Statistics and Data Visualization
  • Measures of central tendency and dispersion
  • Graphical representation of data (charts, histograms, boxplots)
  • Identifying patterns and outliers
  • Using Excel/SPSS for data summarization
Day Three: Inferential Statistics and Hypothesis Testing
  • Probability theory basics and normal distribution
  • Confidence intervals and margin of error
  • T-tests, Chi-square, ANOVA
  • Statistical significance and p-values
Day Four: Correlation, Regression, and Advanced Techniques
  • Correlation analysis (Pearson/Spearman)
  • Simple and multiple regression models
  • Logistic regression and categorical data
  • Introduction to multivariate analysis
Day Five: Application Using SPSS and R – Case Studies
  • Hands-on exercises using datasets
  • Interpreting output tables and charts
  • Reporting findings in academic papers
  • Group presentations and feedback

Providers and Associations

Anderson
Anderson
Aztech Training
Aztech Training