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1 event found
Target audience: researchers
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Predictive Analytics for Data Science: Linear and Non-Linear Modelling
28 - 29 March 2025
Predictive Analytics for Data Science: Linear and Non-Linear Modelling https://www.acspri.org.au/master-class-march-2025-predictive-analytics-data-science-online https://dresa.org.au/events/predictive-analytics-for-data-science-linear-and-non-linear-modelling-e8d89bd0-1e2e-44ba-b3bf-5f3094a2130f This masterclass is an introduction to linear and non-linear predictive models. It will provide an interactive step-by-step guide to running these models and key diagnostics using the R software platform. Regression modelling is a foundation in data science and a must for anyone wanting to venture into this space. Understanding when and how to use linear and non-linear regression models in everyday research is an essential skill for any analyst. Linear and non-linear regression models are commonly used to quantify the relationship between two or more variables by predicting a key outcome of interest. These models are used as effective and powerful tools to control for the potential confounding effect of extraneous variables and/or developing highly predictive models. Linear regression relates to continuous outcomes and is a fundamental regression technique in data science. Logistic regression is used when the outcome of interest is categorical and a fundamental classification technique in data science. When there is no theoretical or mechanistic model to suggest a particular functional form to describe the relationship between two or more variables of interest, Generalized Additive Models (GAMs) can used as they fit a nonparametric curve to the data without requiring pre-defining any particular mathematical model to describe the nonlinearity. Gaining a sound understanding of all these models is essential to understand when it is appropriate to use these techniques. 2025-03-28 09:30:00 UTC 2025-03-29 17:00:00 UTC ACSPRI online, Australia online Australia ACSPRI info@acspri.org.au [] researchersPhD studentsHDR students 12 workshop open_to_all Predictive modelsPredictive AnalyticsData Sciencesocial data science
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Predictive Analytics for Data Science: Linear and Non-Linear Modelling
28 - 29 March 2025
Predictive Analytics for Data Science: Linear and Non-Linear Modelling https://www.acspri.org.au/master-class-march-2025-predictive-analytics-data-science-online https://dresa.org.au/events/predictive-analytics-for-data-science-linear-and-non-linear-modelling-e8d89bd0-1e2e-44ba-b3bf-5f3094a2130f This masterclass is an introduction to linear and non-linear predictive models. It will provide an interactive step-by-step guide to running these models and key diagnostics using the R software platform. Regression modelling is a foundation in data science and a must for anyone wanting to venture into this space. Understanding when and how to use linear and non-linear regression models in everyday research is an essential skill for any analyst. Linear and non-linear regression models are commonly used to quantify the relationship between two or more variables by predicting a key outcome of interest. These models are used as effective and powerful tools to control for the potential confounding effect of extraneous variables and/or developing highly predictive models. Linear regression relates to continuous outcomes and is a fundamental regression technique in data science. Logistic regression is used when the outcome of interest is categorical and a fundamental classification technique in data science. When there is no theoretical or mechanistic model to suggest a particular functional form to describe the relationship between two or more variables of interest, Generalized Additive Models (GAMs) can used as they fit a nonparametric curve to the data without requiring pre-defining any particular mathematical model to describe the nonlinearity. Gaining a sound understanding of all these models is essential to understand when it is appropriate to use these techniques. 2025-03-28 09:30:00 UTC 2025-03-29 17:00:00 UTC ACSPRI online, Australia online Australia ACSPRI info@acspri.org.au [] researchersPhD studentsHDR students 12 workshop open_to_all Predictive modelsPredictive AnalyticsData Sciencesocial data science
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