I have submitted all the files including assessment brief. thanks
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 BrisbaneSydneyMelbourne IndexAnnual % changeIndexAnnual % changeIndexAnnual % change 2002–0352.6n.a.78.2n.a.54.1n.a. 2003–0469.732.587.511.960.111.2 2004–0572.64.284.13.961.21.8 2005–0675.43.981.6363.94.4 2006–0783.110.283.62.570.410.2 2007–0898.818.989.16.684.119.5 2008–0997.4–1.485.83.783.5–0.7 2009–10105.78.597.814100.220 2010–11104.6–1.0102.24.5104.84.6 2011–12100–4.41002.2100–4.6 2012–13101.81.8104.44.4100.50.5 2013–141086.1120.415.3110.39.8 2014–15113.24.814016.3118.17.1 2015–16118.44.6157.312.4131.211.1 2016–17123.24.1175.411.5148.913.5 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 IndexAnnual % changeTotal number of square metersAge of house (years) 2002–0363078.20160.535 2003–0465187.511.9248.945 2004–0569984.13.9155.320 2005–0676881.63240.432 2006–0773983.62.5188.425 2007–0877989.16.6155.814 2008–0974985.83.7174.88 2009–1078097.814310.510 2010–11790102.24.5168.228 2011–128341002.224730 2012–13795104.44.41822 2013–14839120.415.3214.36 2014–1579714016.3212.114 2015–16845157.312.4248.59 2016–17960175.411.52301 n.a. = not available. (a) Established houses. (b) Base of each index: 2011–12 = 100. (c) Average four quarters.Sale PriceSelling price in $ 000s Land sizeLand 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). b) Critically evaluate summary statistics against suitable benchmarks. 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 broadly 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 correctly 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 2 Page 4 of 6 Learning Rubric: 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 referencing 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 research, 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 referencing system is used and sources are appropriately acknowledged. http://www.tua.edu.au/media/50742/a240_grading-scheme.pdf STAT6003_Assessent 2 Page 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. Correctly 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: b) Identify and apply appropriate frameworks and tools to the problems and challenges Demonstrates no understanding of the framework and concepts relevant to the data analysis. Demonstrates little understanding of the framework and concepts relevant to the data analysis. Demonstrates good knowledge of the framework and concepts relevant to the data analysis. Demonstrates correct knowledge of the framework and concepts relevant to the data analysis.