STAT6003 : Statistics for Financial Decisions a) Analyse and present data graphically usingspreadsheet software (Excel).b) Critically evaluate summary statistics against suitablebenchmarks.c) Apply judgment to select appropriate methods of dataanalysis drawing on knowledge of regression analysis,probability, probability distributions and samplingdistributions.d) Select and apply a range of data analysis tools toinform problem solving and decision making.e) Conduct quantitative research both individually and aspart of a team and articulate and present findings to awide range of stakeholders, from accounting and nonaccounting backgrounds.Submission Module 6.2 (Week 12) Context:The main aims to develop students’ competency in statistical literacy for decision making in thelocal and global business environment. It reviews statistical techniques for the quantitativeevaluation of data in Financial applications. Students will develop analytical and statistical skills toenable 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 fromSTAT6003 lectures between week 9, 10 and 11. You will specify a regression model for this assignment. This model can be based on atheory, several theories, your experience, and/or ideas. Please use Excel for statistical analysis in this assignment. Relevant Excel statistical outputmust be properly analysed and interpreted. Please provide a number for every table, graph or figure used and make clear reference tothe table/graph/figure in your discussion. The assessment is to be submitted in a business report format with a word limit of 2,000words 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 mainnavigation menu in STAT6003. The Learning Facilitator will provide feedback with reference tothe 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 andMelbourne, 2002–03 to 2016–17.V1) Market Price ($000)V2) Sydney price IndexV3) Annual % changeV4) Total number of square metersV5) Age of house (years)1) Module 5 topic – Regression AnalysisYou 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 considera regression model that is of interest to you or one that is related to your profession or onethat you have knowledge about.(a) Using Ordinary Least Square (OLS), estimate the model (below is a template fordeveloping 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 requiredto: 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 thedependent and independent variables (including their unit of measurement) in thismodel.(2) Plot the dependent variable against each independent variable using scatter plot/dotfunction 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 thedependent and independent variables. Interpret this value accurately and in a meaningfulway.(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 marketprice and the land size in total number of square meters.(9) Compare the original model (question 1) and re-estimated model (question 2) andevaluate the goodness of fit between them (Hint: Use R2and Coefficient ofdetermination 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. View Less >>
Executive Summary This report is aimed to develop understanding of regression analysis and its implications in data analysis to make decisions. In this report, impact of different independent variables Sydney Price Index, Annual Percentage Change, Land size and Age of house on the house prices in Sydney market has been analyzed. From regression analysis, it is analyzed that there is a positive linear relationship between Sydney price index, land size and annual percentage change with the market prices of Houses in Sydney. but at the same time, there is a negative linear relationship between age of house and market price of houses. In addition, multiple regression analysis shows that Sydney Price Index shows the moderate fit or reliable value. Moreover, it can be identified from hypothesis testing that there is a significant relationship between dependent and independent variables.  IntroductionThe use of statistical analysis is significant to make decisions in the business efficiently. In relation to this, the presented case study is also based on the utilization of linear regression model for determining relationship between variables including dependent and independent variables. Apart from this, the use of regression analysis is also significant to determine the degree of impact of independent variable on the dependent variable (Newbold et al., 2012). It is also effective to predict the values of one variable in future based on the change in another variable. The sample size of the given data set is 15 observations during 2002-03 and 2016-17 Get solution

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