### 2.2 Develop models for nominal and ordinal scaled dependent variable in R and Python correctly.

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# Unit DS04: Advanced Predictive Modelling

Level 7 Diploma in Data Science

Unit code: Y/618/4973
RQF Level: 7

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Aim
In this unit, learners are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and this unit covers detailed model building processes for binary dependent variables. In addition, multinomial models and ordinal scaled variables will also be discussed.

Learning Outcomes and Assessment Criteria

 Learning Outcomes. When awarded credit for this unit, a learner will be able to: Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can: 1. Develop models using binary logistic regression and assess their performance. 1.1 Evaluate when to use Binary Linear Regression correctly. 1.2 Develop realistic models using functions in R and Python. 1.3 Interpret output of global testing using Linear Regression Testing in order to assess the results. 1.4 Perform out of sample validation that tests predictive quality of the model. 2. Develop applications of multinomial logistic regression and ordinal logistic regression. 2.1 Select method for modelling categorical variable. 2.2 Develop models for nominal and ordinal scaled dependent variable in R and Python correctly. 3. Develop generalised linear models and carry out survival analysis and Cox regression. 3.1 Evaluate the concept of generalised linear models. 3.2 Apply the Poisson regression model and negative binomial regression to count data correctly. 3.3 Model ‘time to event’ variable using cox regression.

Assessment Guidance
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:

• Formative: Weekly assignments focussing on knowledge and understanding of technical skills using sample data sets over a period of 3 weeks and participation in weekly live classrooms and discussion groups;
• Summative: 1. Formal timed exam testing technical knowledge 2. Component of two individual course projects based on real word data analytics

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