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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.

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.

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...

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...

SOLUTION.PDF## Answer To This Question Is Available To Download

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