37357 Advanced Statistical Modelling
SAS Assignment 2 Group Assignment
Statistical Modelling代写 You may use ANY model-fitting techniques you have learned in this course that you think are appropriate for the given situation.
This assignment is worth 35% of your final mark and is due on 4th November at 3.00pm. Assignments may be submitted via email in a pdf format to torrington.callan@uts.edu.au, BEFORE the due date/time. Late submissions will attract a penalty of 25% for every 24 hours or part thereof.
The assignment uses two datasets which can be found in the SAS course library (and UTSOnline). You may use ANY model-fitting techniques you have learned in this course that you think are appropriate for the given situation.
Submission Guidelines statistical-modelling代写
- Assignments are to be typed (not handwritten), printed and stapled (no folders or plastic sleeves).
- Assignments must include a signed cover sheet (can be found onUTSOnline).
- Questions are to be answered in
- Each question must be clearly distinguishable – do not answer multiple questions in one paragraph.
- Miscellaneous plots tacked onto the end of the assignment will not be considered. When you are asked for a specific plot, you must provide it within the relevant
- You are welcome to copy relevant bits of output into your answers if you think it supports your argument, but you will be marked down if you simply copy and paste
- Written answers must be succinct. You may lose marks for answers that are excessively long.Statistical Modelling代写
SECTION 1 statistical-modelling代写
Heart Disease Dataset [15 marks]
The dataset heart in the SAS course library contains data on 303 tests for the diagnosis of heart disease. The variables are Age (in years), sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved during an exercise test, exercise induced angina, ST depression induced by exercise, the number of major blood vessel coloured by fluoroscopy, blood disorder and the presence of disease
The dataset is obtained and modified from UCI Machine Learning Repository. The full dataset is described and analysed in:Statistical Modelling代写
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304–310.
Our OBJECTIVE is to explain the presence of severe heart disease based on the variables available. We would like to develop a model to assist clinicians and hospital staff in determining a patient’s outcome.
This allows us to treat at-risk patients, whilst reducing the need for unnecessary treatments for other patients.
- [8 marks] Present your final model by pasting the table from SAS that contains the coefficients. Briefly discuss any decisions you made in refining your models – this includes variablesselection, the assumption of proportional odds and assessing model You are not required to find a model that is a perfect fit to the data (which may not be possible using the techniques you have covered so far in this course). Simply choose ONE reasonable model to present and discuss.Statistical Modelling代写
- [5marks] Interpret the impact of each variable on the probability of a patient experiencing heart disease, and presenting with more severe heart
- [2 marks] In order to prevent severe heart disease, what characteristics indicate that the patient should receive increased treatment from medicalstaff?
SECTION 2 statistical-modelling代写
Survival Dataset [15 marks]
The dataset survival in the SAS course library contains the survival of patients on the waiting list for the Stanford heart transplant program.
| Variable Name | Description |
| start, stop | Entry and exit time in days |
| event: | 1=if the patient died during the observation window |
| age: | age-48 years |
| year: | year of acceptance (in years after 1 Nov 1967) |
| surgery: | prior bypass surgery 1=yes |
| transplant: | received transplant 1=yes |
| id: | patient id |
The dataset was obtained from
J Crowley and M Hu (1977), Covariance analysis of heart transplant survival data. Journal of the American Statistical Association, 72, 27–36.
[8 marks] Present your final model by pasting the table from SAS that contains the coefficients. Briefly discuss any decisions you made in refining your models. You are not required to find a model that is a perfect fit to the data (which may not be possible using the techniques you have covered so far in this course). Simply choose ONE reasonable model to present and
[5 marks] Interpret the impact of each variable on the mortality
[2 marks] In order to reduce the mortality rate, what patients should be prioritised for transplant?
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