Reliable data collection and statistical analysis methods typically play an important role in the planning and management functions of public and private business enterprises. Government, marketing agencies and industry specific groups regularly survey consumers, suppliers and other targeted demographics in order to gain a better understanding of general consensus opinion and trends. While important, having a sound empirical understanding of the data is perhaps not as valuable as the potential to model and forecast trends and market shifts. Nonetheless the two go hand in hand, as reliable models and forecasts are based on accurate characterization of empirical data.
The deliverable for the Major Project assignment is to compile a typed report in response to the provided case study brief. The data set required to put together your report is provided along with detailed instructions on what should be included in your report.
While your data set is exhaustive (i.e. you need not add to it), you may like to ‘spice’ up your report by adding relevant industry specific insights to your discussion and conclusions. Most importantly, ensure that in addition to your statistical analysis of the data you provide a critical perspective on any modeling issues you encounter and the relevant solutions you apply to these problems.
Submit your final project to the dropbox below by Day 7 of this week. This assignment comprises 50% of your overall course grade.
You are required to provide a comprehensive summary of the data set contained in the provided data file. How you choose to do this is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods while also noting any peculiarities within the data set.
You have been hired by the wealthy owner of a house on Elm Street in Purple Hill (not included in the data set) to predict the price at which her house will sell. Her house has four stories, is in Mayfair, is 15 square reports large, has 9 rooms, is not near a hotel or pub or bus stop or housing trust property and is 10 km from the university. Some other features of the property can be seen below:
Views of and from the house whose sale price you are to predict
You are expected to build a regression model of house prices using as many explanatory variables as possible and to use this model to obtain for the Elm Street house:
A point prediction of the sales price which it can be expected to fetch
A 95% interval prediction for this sale price
An estimate of the marginal effect of house size on this sale price
Financial advice on whether the owner should use “W&M” or “A&B” to sell the house. “W&M” charges a commission of 5% and “A&B” charges a commission of 10% on the final sale price.
The owner who claims to have some knowledge of regression analysis has stressed that she thinks you should use a regression model with an R2 of at least 70%.
Please provide a reflective discussion of how you executed Task 2 of the project above. Specifically consider the following:
Did you encounter any regression model misspecification errors? If so, which ones?
If you found errors, what did you do to ensure that they have only a minimal impact on your results in Task 2 above?
Were there any other oddities in the data set or your model? Explain.
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