About the Programme

Many researchers can run a test but are less sure which test fits their data, or how to defend that choice to a reviewer. This masterclass covers the core statistics behind quantitative research, from understanding your data to testing differences and associations. It then moves to text analytics, where you will learn to find themes and sentiment in large volumes of text. All sessions are hands-on in JMP, using practice datasets you keep after the programme.

Resource Person

Dr Muralidhara Anandamurthy
Dr Muralidhara Anandamurthy is a member of the JMP Global Academic Team. He specialises in strategy, analytics, machine learning and Design of Experiments (DOE). He is a co-author of Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro (2nd edition, Wiley), with Galit Shmueli, Peter Bruce, Mia Stephens and Nitin Patel. His articles include Structured Spontaneity, DOE in Engineering and Business Experimentation.

Who Should Attend

• PhD scholars in management, social sciences, commerce and allied fields
• Faculty members who teach or supervise quantitative research
• Researchers working with survey data, reviews or open-ended responses
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Programme Schedule

Day 1 (20 Nov): Foundations of Statistics

• Types of data: nominal, ordinal, interval and ratio
• Probability distributions and why the normal distribution matters
• Checking normality: histograms, Q-Q plots, skewness, kurtosis and formal tests
• Choosing between parametric and non-parametric tests
• Univariate analysis: describing a single variable
• Bivariate analysis: examining relationships between two variables
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Day 2 (21 Nov): Testing Differences and Associations

• Independent samples t-test
• Paired samples t-test
• One-way ANOVA and post hoc comparisons
• Chi-square test of association
• Non-parametric alternatives, such as the Mann-Whitney U and Wilcoxon signed-rank tests
• Reading and reporting results in a research paper
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Day 3 (22 Nov): Text Analytics

• Preparing text data for analysis
• Topic modelling: identifying themes across large text collections
• Sentiment analysis: measuring tone in reviews, comments and open-ended responses
• Interpreting the output and using it in research
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What You Get

• Recordings of all sessions
• e-Certificate of participation
• Reference material
• Practice datasets