MIS771 Descriptive Analytics and Visualization Assignment Brief

MIS771 Descriptive Analytics and Visualization Assignment Brief

MIS771 Descriptive Analytics and Visualization Assignment Brief

This solution is based on MIS771 descriptive analysis and visualization assignment in which we discuss about exploratory, descriptive and regression analysis to gain a comprehensive understanding of house prices in a city.

Background

This is an individual assignment, which requires you to analyse a given data set, interpret, draw conclusions from your analysis, and then convey your conclusions in a written report to a person with little or no knowledge of Business Analytics.

The assignment must be submitted by the due date electronically in Cloud Deakin. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in Cloud Deakin. Please note that we will not accept any hard copies or part of the assignment submitted after the deadline or via Email.

Extensions of time are not permitted. A penalty of 10% of the 40 marks allocated to this assessment task will be deducted for each day or part day that the assessment is late. Penalties include weekend days.

The assignment uses the file Shirazt.xlsx which can be downloaded from Cloud Deakin. Analysis of the data requires the use of techniques predominately studied in Module 2 (but will also require a degree of knowledge from Module 1).

Case Study

Shiraz is a (fictitious) local government area (called a 'city') within greater Melbourne, Australia. It consists of some different suburbs, all with their history of development. The city grew in different stages, with new suburbs gradually emerging. It covers some wealthy suburbs and some not so wealthy. The city is located on the Bay and about 60,000 people live in the suburbs of Shiraz.

The main objective is to conduct exploratory, descriptive and regression analysis to gain a comprehensive understanding of house prices in the Shiraz region and an understanding of the most important factors that impact prices. Your analysis will be based on a random sample of 120 houses from the city. Note that for the purpose of the assignment the unit of analysis is a ‘House’. It is defined as a stand-alone dwelling. That is, flats, apartments, etc. are not included in the database.

The Data

The cross-sectional data collected contains a number of categorical and numerical variables which are described below:

Variable Name

Description

Price

Selling price of house in $'000

Rooms

Number of main rooms in the house

Lot Size

Area of the block of land (lot) in square metres

Age

Age of the house in years

Area

Area of the house in square meters

To Train

Distance of the house to the nearest train station (kilometers)

To Bus

Distance of the house to the nearest bus stop (kilometers)

To Shops

Distance of the house to the nearest shopping centre (kilometers)

Street

Street appeal as evaluated by the real estate agency:

 

ranges from 0 (lowest appeal) to 10 (highest appeal)

Storeys

Number of storeys or levels in the house

Style

Style of the House

Bedrooms

Number of bedrooms

Bathrooms

Number of bathrooms

Heating

Central or other heating system installed

Kitchen

Style of kitchen

 

AirCon

Air conditioning installed: No AC (No AirCon) = 0, AC (Yes AirCon) = 1

Bay Views

Proportion of views of the Bay from a prominent part of the property:

 

ranges from 0 = Nil views up to 1 = Full views

Weekly Rent $

Actual or estimated weekly rent in $.

In addition, time series data is available on Quarterly Median House Prices

Time Period

Time Period Index

Quarter

Quarter Description

Median House Price ($'000)

Median House price in $'000

Task One – Summary of House Prices

Only analyse Price by itself. The importance of other variables is considered in other tasks. You should, at the very least, thoroughly investigate relevant summary measures (and their reliability) for this variable. Also, there may well be suitable tables and graphs that will illustrate, further and more clearly, other important features of house prices. In your report you should comment, where relevant, on data location, central tendency, variability, shape and outliers for this variable.

Task Two – Factors influencing house prices

Analyse house prices against all other variables included in the dataset. Use appropriate descriptive techniques such as cross-tabulations, comparative summary measures, scatter diagrams to identify key relationships. In your report you should only include the most important factors that impact house prices (approximately between 3 – 5 factors).

Task Three – Development of a multiple regression model for house price

You should follow the model building process outlined in topic 5. You are only required to consider linear relationships in the model. Each stage of developing your model should be included in your analysis. You will notice in the Shiraz spreadsheet that there are tabs called Q3.1, Q3.2, etc. These are where you place each version of your model. Note that if you have undertaken more iterations of the model then add more worksheets.

The report should only include your final model and a description of its overall strength as well as the influence of each variable.

Task Four – Time series analysis

Quarterly median house prices in Shiraz from Q1, 2012 to Q4, 2015 are given in QtrPriceDataworksheet. Develop a multiplicative time series model to forecast median house prices for the next 4 quarters (Q1, 2016 to Q4, 2016).

If the observed values for those 4 quarters are as below, calculate the MAPE of the forecast.

Time Period

Quarter

Observed

17

2016-Q1

950

18

2016-Q2

1320

19

2016-Q3

1500

20

2016-Q4

1090

Task Five – Critique the Business Research Approach

Discuss the suitability of the general business research approach taken. In your response, include possible alternative approaches and other sources of (secondary) data. If the analysis was to be repeated in the future, would you recommend a different approach? Note that no actual analysis is required for this task