See the deakin referencing guide integrity

ASSESSMENT DETAILS

Due: Friday 3rd May 2019 11.30 pm AEST Weighting: 30%
Reference style: Harvard

Learning Outcomes


ULO2 – assessed through the student ability to analyse datasets by interpreting summary statistics, model and function parameters. ULO4 - assessed through student ability to develop software codes to solve computational problems for real world analytics.

The work is individual. Solutions and answers to the assignment must be explained carefully in a concise manner and presented carefully. Use of books, articles and/or online resources on share price related to SIT718 Real World Analytics is allowed. Students are expected to refer to the suitable literature where appropriate.

The assessment consists of FOUR tasks. Students must attempt all tasks and provide an individual written report in appropriate word processor.

• Assignment (a report in pdf format, software code and/or data) must be submitted via the assignment folder in the unit site (accessed via the unit Program page)
No e-mail or hardcopy submissions are accepted.•

Extension requests

Hardship/Trauma e.g. sudden loss or gain of employment, severe disruption to domestic
arrangements, victim of crime. Note: Misreading the timetable, exam anxiety or returning home will not be accepted as grounds for consideration.

Special consideration

Referencing

You must correctly use the Harvard method in this assessment. See the Deakin referencing guide.

	2	FutureLearn

Plagiarism occurs when a student passes off as the student’s own work, or copies without

acknowledgement as to its authorship, the work of any other person or resubmits their own work from

assuring academic integrity of submissions: https://www.deakin.edu.au/students/study-

support/referencing/academic-integrity

Total Marks 100, Weighting 30%

Energy Prediction of Domestic Appliances Dataset

(i) Download the txt file (Energy19.txt) from Future Learn and save it to your R

working directory.

my.data <- the.data[sample(1:671,300),c(1:6)]

2. Transform the data [20 marks]

(i) Choose any four from the five variables (X1, X2,..,X5). Make appropriate

transformed.txt" using

write.table(your.data,"name-transformed.txt")

3. Build models and investigate the importance of each variable [40 marks]

(i) Download the AggWaFit718.R file (from Future Learn) to your working directory

• Weighted power means (WPM) with p = 0.5, and p = 2,

• An ordered weighted averaging function (OWA), and

(iv) Compare and interpret the data in your tables. Comment on

a. How good the model is,

d. Better models favour higher or lower inputs.

(1-3 paragraphs for part 3(iv))

(ii) Give your result and comment on whether you think it is reasonable. (1-2 sentences).

(iii) Comment on the best conditions (in terms of your chosen four variables) under which a low Energy use of appliances will occur. (1-2 sentences).

http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction

REMINDER!!

	3	FutureLearn