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ASSESSMENT TASKS
Task 1 FORMATIVE TASK Linear Regression in Principle
FORMATIVE TASK
Instruction: Produce a briefing document for your line manager that explains linear regression and its potential uses in your organization. The report must contain the following:
- An outline of what is understood by simple, multiple and polynomial regression, and of the assumptions within linear
- Identify and explain the Ordinary Least Squares method, and the formulae used to calculate the intercept and slope coefficient in simple linear regression
- Discuss the potential uses for linear regression within your organisation, and a judgment as to its overall value to the organisation
Task 2 SUMMATIVE TASK Linear Regression in your Organisation
SUMMATIVE TASK
Instruction: Carry out an evaluation of a linear regression undertaken within your organisation. Your report must contain the following:
- An outline of how Python was used to create the linear regression, including the regression metrics used and how the intercept and slope coefficient were calculated (LO 1, 3.1, 4.1)
- Identify and explain how these regression metrics were calculated accurately (LO 2, 3.2)
- A judgment as to the conclusions that can be drawn from the data, and the accuracy of these conclusions in making future decisions (LO 4. 5.2)
| Learning Outcomes: To achieve this unit, the learner must be able to: | Assessment Criteria: Assessment of these learning outcomes will require a learner to demonstrate that they can: |
| 2. Understand regression metrics and how to evaluate a regression model. | 2.1 Explain the regression metrics: – The Total Sum of Squares (TSS) – The Residual Sum of Squares (RSS) – The Explained Sum of Squares (ESS) – The Mean Squared Error (MSE) – The Root Mean Square Error (RMSE) – The coefficient of determination (????2). – The Adjusted ????2 2.2 Explain how to interpret each of the regression metrics listed in 2.1. |
| 3. Be able to perform regression calculations and analysis. | 3.1 Calculate correctly the intercept and slope coefficient in simple linear regression. 3.2 Calculate correctly the regression metrics in a linear regression model. 3.3 Interpret the calculated metrics and draw reasoned conclusions. |
| 4. Be able to create linear regression models. | 4.1 Use Python to build accurate simple linear regression and multiple linear regression models for given datasets. 4.2 Use Python to evaluate the accuracy of the models built in 4.1. and analyse the results. |