Introduction
Prior to attempting this coursework assignment, learners must familiarise themselves with the following policies:
Plagiarism and Collusion
In submitting the assignment Learner’s must complete a statement of authenticity confirming that the work submitted for all tasks is their own. The statement should also include the word count.
Your accredited study centre will direct you to the appropriate software that checks the level of similarity. Qualifi recommends the use of as a part of the assessment.
Plagiarism and collusion are treated very seriously. Plagiarism involves presenting work, excerpts, ideas or passages of another author without appropri te referencing and attribution.
Collusion occurs when two or more learners submit work which is so alike in ideas, content, wording and/or structure that the similarity goes beyond what might have been mere coincidence
Please familiarise yourself on Qualifi’s Malpractice and Maladministration policy, where you can find further information
1. Carry out global and individual testing of parameters used in defining predictive models.
1. Evaluate dependent variables and predictors.
2. Develop linear models using the lm function in R and the .ols function in Python.
3. Interpret signs and values of estimated regression coefficients.
4. Interpret output of global testing using F distributions.
5. Identify significant and insignificant variables.
2. Validate assumptions in multiple linear regression
1. Resolve multicollinearity problems.
2. Revise a model after resolving the problem.
3. Assess the performance of the ridge regression model.
4. Perform residual analysis – graphically & using statistical tests to analyse results.
5. Resolve problems of non-normality of errors and heteroscedasticity.
3. Validate models via data partitioning, out of sample testing and cross-validation
1. Develop models and implement them on testing data in accordance with the specification.
2. Evaluate the stability of the models using k-fold cross validation.
3. Evaluate influential observations using Cook’s distance and hat matrix.
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