house-price-index-bris-syd-melb House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2016–17 Financial year (c)Capital city Brisbane Sydney Melbourne IndexAnnual %...

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house-price-index-
is-syd-mel
    House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2016–17
    Financial year (c)    Capital city
        Brisbane        Sydney        Melbourne
        Index    Annual % change    Index    Annual % change    Index    Annual % change
    2002–03    52.6    n.a.    78.2    n.a.    54.1    n.a.
    2003–04    69.7    32.5    87.5    11.9    60.1    11.2
    2004–05    72.6    4.2    84.1    3.9    61.2    1.8
    2005–06    75.4    3.9    81.6    3    63.9    4.4
    2006–07    83.1    10.2    83.6    2.5    70.4    10.2
    2007–08    98.8    18.9    89.1    6.6    84.1    19.5
    2008–09    97.4    –1.4    85.8    3.7    83.5    –0.7
    2009–10    105.7    8.5    97.8    14    100.2    20
    2010–11    104.6    –1.0    102.2    4.5    104.8    4.6
    2011–12    100    –4.4    100    2.2    100    –4.6
    2012–13    101.8    1.8    104.4    4.4    100.5    0.5
    2013–14    108    6.1    120.4    15.3    110.3    9.8
    2014–15    113.2    4.8    140    16.3    118.1    7.1
    2015–16    118.4    4.6    157.3    12.4    131.2    11.1
    2016–17    123.2    4.1    175.4    11.5    148.9    13.5
    n.a. = not available.
    (a) Established houses.
    (b) Base of each index: 2011–12 = 100.
    (c) Average four quarters.
    
    Source: ABS 6416.0, Residential Property Price Indexes: Three Capital Cities.
house-price-index and sales
    House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2016–17
    Financial year (c)
            Market Price ($000)    Sydney price Index    Annual % change    Total number of square meters    Age of house (years)
    2002–03        630    78.2    0    160.5    35
    2003–04        651    87.5    11.9    248.9    45
    2004–05        699    84.1    3.9    155.3    20
    2005–06        768    81.6    3    240.4    32
    2006–07        739    83.6    2.5    188.4    25
    2007–08        779    89.1    6.6    155.8    14
    2008–09        749    85.8    3.7    174.8    8
    2009–10        780    97.8    14    310.5    10
    2010–11        790    102.2    4.5    168.2    28
    2011–12        834    100    2.2    247    30
    2012–13        795    104.4    4.4    182    2
    2013–14        839    120.4    15.3    214.3    6
    2014–15        797    140    16.3    212.1    14
    2015–16        845    157.3    12.4    248.5    9
    2016–17        960    175.4    11.5    230    1
    n.a. = not available.
    (a) Established houses.
    (b) Base of each index: 2011–12 = 100.
    (c) Average four quarters.        Sale Price    Selling price in $ 000s
             Land size    Land size in Square meters
    Source: ABS 6416.0, Residential Property Price Indexes: Three Capital Cities.        Year Built     Year house was built

ASSESSMENT BRIEF
Subject Code and Title STAT6003 : Statistics for Financial Decisions
Assessment Assessment 4 – Case Analysis
Individual/Group Individual
Length 2000 Words (+/- 10%)
Learning Outcomes a) Analyse and present data graphically using
spreadsheet software (Excel).
) Critically evaluate summary statistics against suitable
enchmarks.
c) Apply judgment to select appropriate methods of data
analysis drawing on knowledge of regression analysis,
probability, probability distributions and sampling
distributions.
d) Select and apply a range of data analysis tools to
inform problem solving and decision making.
e) Conduct quantitative research both individually and as
part of a team and articulate and present findings to a
wide range of stakeholders, from accounting and non-
accounting backgrounds.
Submission Module 6.2 (Week 12)
Weighting 30%
Total Marks 100 marks
Context:
The main aims to develop students’ competency in statistical literacy for decision making in the
local and global business environment. It reviews statistical techniques for the quantitative
evaluation of data in Financial applications. Students will develop analytical and statistical skills to
enable them to transform data into meaningful information for the purpose of decision making.
Objectives:
 To more
oadly understand the statistical literacy for decision making.
 Interpret statistical results and communicate their statistical analysis in business reports.
Instructions:
This individual assignment requires you to apply statistical knowledge and skills learned from
STAT6003 lectures between week 9, 10 and 11.
 You will specify a regression model for this assignment. This model can be based on a
theory, several theories, your experience, and/or ideas.
 Please use Excel for statistical analysis in this assignment. Relevant Excel statistical output
must be properly analysed and interpreted.
 Please provide a number for every table, graph or figure used and make clear reference to
the table/graph/figure in your discussion.
 The assessment is to be submitted in a business report format with a word limit of 2,000
words excluding Excel output. Both Excel and the report files are to be submitted.
Submit copy of presentation Report in .docx, or .pdf format via the Assessment link in the main
navigation menu in STAT6003. The Learning Facilitator will provide feedback with reference to
the criteria below via the Grade Centre in the LMS portal. Feedback can be viewed in My Grades.
Assignment tasks:
The variables for this assignment are as follows: House Price Index (a)(b): Brisbane, Sydney and
Melbourne, 2002–03 to 2016–17.
V1) Market Price ($000)
V2) Sydney price Index
V3) Annual % change
V4) Total number of square meters
V5) Age of house (years)
1) Module 5 topic – Regression Analysis
You will specify a regression model for this assignment. This model can be based on a theory,
several theories, your experience, and/or ideas from research article(s). Suggest you consider
a regression model that is of interest to you or one that is related to your profession or one
that you have knowledge about.
(a) Using Ordinary Least Square (OLS), estimate the model (below is a template for
developing your regression model):
Y = 0 + 1 X1 + 2 X2 + 3 X3 + 4 X4 + .
In your model, there must be one dependent variable and four independent variables.
(b) For statistical analysis involving any hypothesis test in this assignment, you are required
to:
 Formulate the null and alternative hypotheses.
 State your statistical decision using significant value (?) of 5% for each test.
 State your conclusion in context.
Assignment tasks:

(1) Provide an introduction section on the rationale of your model , sample size, and the
dependent and independent variables (including their unit of measurement) in this
model.
(2) Plot the dependent variable against each independent variable using scatter plot/dot
function in Excel. Describe the relationship from the plots.
(3) Present the full model in your assignment.
(4) Write down the least squares regression equation and co
ectly interpret the equation.
(5) Interpret the estimated coefficients of the regression model and discuss their sig values.
(6) What is the value of the coefficient of determination for the relationship between the
dependent and independent variables. Interpret this value accurately and in a meaningful
way.
(7) State the 95% confidence intervals for each parameters and interpret these intervals.
(8) Estimate the linear regression model to investigate the relationship between the market
price and the land size in total number of square meters.
(9) Compare the original model (question 1) and re-estimated model (question 2) and
evaluate the goodness of fit between them (Hint: Use R2 and Coefficient of
determination to evaluate the goodness of fit of the model).
(10) Predict the market price of a house (in $) with a building area of 400 square meters.



STAT6003_Assessent XXXXXXXXXXPage 4 of 6
Learning Ru
ic: Environmental Scan Report – Part A
Assessment
Attributes
Fail (Unacceptable)
(0-49)
Pass
(Functional)
(50-64)
Credit
(Proficient)
(65-74)
Distinction
(Advanced)
(75-84)
High Distinction
(Exceptional)
(85-100)






Grade
Description
(Grading Scheme)
Fail grade will be awarded
if a student is unable to
demonstrate satisfactory
academic performance in
the subject or has failed to
complete required
assessment points in
accordance with the
subject’s required
assessment points.
Pass is awarded for work
showing a satisfactory
achievement of all learning
outcomes and an adequate
understanding of theory and
application of skills. A
consistent academic
eferencing system is used and
sources are appropriately
acknowledged.
Credit is awarded for work
showing a more than satisfactory
achievement of all learning
outcomes and a more than
adequate understanding of
theory and application of skills. A
consistent academic referencing
system is used and sources are
appropriately acknowledged.
Distinction is awarded for
work of superior quality in
achieving all learning
outcomes and a superior
integration and
understanding of theory
and application of skills.
Evidence of in-depth
esearch, reading, analysis
and evaluation is
demonstrated. A consistent
academic referencing
system is used and sources
are appropriately
acknowledged.
High Distinction is awarded for
work of outstanding quality in
achieving all learning
outcomes together with
outstanding integration and
understanding of theory and
application of skills. Evidence
of in‐depth research, reading,
analysis, original and creative
thought is demonstrated. A
consistent academic
eferencing system is used and
sources are appropriately
acknowledged.
http:
www.tua.edu.au/media/50742/a240_grading-scheme.pdf

STAT6003_Assessent XXXXXXXXXXPage 5 of 6

Data Analysis using Excel
45%
SLO addressed:
a) Examine the
statistical
analysis through
Excel




Limited or no understanding
of the statistical data
analysis.










Identifies a proportion of the
understanding of the statistical
data analysis.









Identifies a majority of the
understanding of the statistical
data analysis.











Co
ectly identifies all of the
analytical techniques and
understanding of the statistical
data analysis.









Not only identifies all of the
analytical techniques with
good understanding of the
statistical data analysis.



Application of Framework
45%
SLO addressed:
) Identify and
apply
appropriate
frameworks and
tools to the
problems and
challenges






Demonstrates no
understanding of the
framework and concepts
elevant to the data analysis.








Demonstrates little
understanding of the
framework and concepts
elevant to the data analysis.








Demonstrates good knowledge
of the framework and concepts
elevant to the data analysis.







Demonstrates co
ect
knowledge of the framework
and concepts relevant to the
data analysis.






Answered Same DayAug 20, 2020STAT6003Torrens University Australia

Solution

Pooja answered on Aug 21 2020
49 Votes
1)
The two models are created using a simple linear regression model and a multiple linear regression model. The dependent variable in both the model is the market price which is measured in thousand Dollars. The independent variable for simple linear regression is the age of the house. However, the independent variables for multiple linear regression model are price Index, Annual % change, Total number of square meters, Age of house (years).
All the variables are measured by the ratio scale of measurement and are continuous variables. The sample size is 15. In other words, there are 15 observations for each variable.
2)
For Sydney, the 4 scatterplots are given below.
There is a strong positive linear relationship between market price and price index. This indicates as the value of the Sydney price index increases the value of the market price also increases.
There is a very weak positive linear relationship between annual percentage change and market price. As the value of annual percentage change increases, the value of market price increases slightly.
There is a weak negative relationship between the number of squares and market price. As the value of the total number of squares increases, the value market price increases slightly.
There is a moderate negative linear relationship between each of the house and market price. As the value of the age of the house increases, the value of market price decreases.
For Brisbane, the 4 scatterplots are given below.
There is a strong positive linear relationship between market price and price index. This indicates as the value of the Sydney price index increases the value of the market price also increases.
There is a very weak negative linear relationship between annual percentage change and market price. As the value of annual percentage change increases, the value of market price decreases slightly.
There is almost no linear relationship between the number of squares and market price. As the value of the total number of squares increases, there is not much effect on the value market price.
There is a moderate negative linear relationship between each of the house and market price. As the value of the age of the house increases, the value of market price decreases.
For Melbourne, the 4 scatterplots are given below.
There is a strong positive linear relationship between market price and price index. This indicates as the value of the Sydney price index increases the value of the market price also increases.
There is almost no linear relationship between annual percentage change and market price. As the value of annual percentage change increases, there isn’t much effect on market price.
There is almost no linear relationship between the number of squares and market price. As the value of the total number of squares increases, there is not much effect on the value market price.
There is a moderate negative linear relationship between each of the house and market price. As the value of the age of the house increases, the value of market price decreases.
3)
The regression equation for the Linear model is expected to be in the form of Market Price = b0 + b1 * Age
Regression equation for multiple regression is expected to be in the form of Market Price = b0 + b1 * Sydney Price Index + b2 * Annual % Change + b3 * Total No of Sqm + b4 * Age
4)
Sydney: Market Price = 548.98 + 1.96 * Sydney Price Index – 5.62 * Annual % Change + 0.52 * Total No of Square meters – 2.49 * Age
The null hypothesis, Ho: The model is not significant. An alternative hypothesis, h1: Model is significant. With (F=9.4, P<5%), the null hypothesis is rejected at the 5% level of significance and it can be concluded that the model is significant.
Brisbane: Market Price...
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