Reference no: EM132172345
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.
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.
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 = β0 + β1 X1 + β2 X2 + β3 X3 + β4 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.