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QUALIFI Level 7 Diploma in Data Science
Unit code: Y/618/4973 RQF
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.
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:
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