Task 2

You have been hired by Joy, the wealthy owner of a house on Elm Street in Junction (not included in the data set) to predict the price at which her house will sell. Her house has two stories, is in Claygate, is 178 square metres large, is not near a shopping precinct and is 10 km from the town centre. She estimates that the house is about 10 years old and in a low crime area according to her experiences. Joy inherited the house from her uncle and is therefore unsure when it was last sold. Some other features of the property can be seen below:

You are expected to build a regression model of house prices. In doing so, make sure that you use an appropriate number of predictors to develop your estimates. Once you have constructed an appropriate model, use it to obtain and provide for Joy’s house:

1. A point prediction of the sales price which it can be expected to fetch

2. A 95% interval prediction for this sale price

3. An estimate of the marginal effect of house size on this sale price

4. Financial advice on whether Joy should use “W&M” or “A&B” to sell her house. “W&M” charges a commission of 2.5% whereas “A&B” charges a commission of 3.5% of the final sale price.

Joy, 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 88%.

Note: Task 1 directed you to take note of any peculiarities in the data set. There are other additional errors in the data set that you may not have picked up on in Task 1. These will only become clear to you once you start working on Task 2. Several problems can result if you fail to handle these issues correctly, so be mindful to address them, both in your regression application as well as your final report. If resolving any of the errors in the dataset requires you to make assumptions, make sure to clearly state your reasoning and approach in your report. 

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