Present the estimated regression equation

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Reference no: EM131966277

Empirical Research Methods for Business Assignment

In this assignment, we will revisit the "Melbourne Property Prices" dataset considered in Assignment 1 and described at the end of this assignment (p. 3). Before beginning the assignment, read through the description of this dataset and its variables carefully. The Melbourne Property Prices" dataset can be found in the file "PropertyPrices.sav."

1. First, we will consider the task of predicting property price (PRICE) from the distance between the property suburb and the Melbourne central business district (DISTANCE) using simple linear regression.

(a) State the assumptions of

linearity,
normality, and
homoscedasticity/equal variances

that underlie linear regression.

(b) Using appropriate graphical displays, thoroughly assess these assumptions for a linear regression of property price on distance between the property suburb and the Melbourne central business district.

(c) Assuming that the assumptions of linear regression hold (regardless of your conclusion from part (b)), is there a significant relationship between property price and the distance between the property suburb and the Melbourne central business district? If so, interpret the slope term (i.e., β₁). Additionally, interpret the value of R².

(d) Present the estimated regression equation relating property price to the distance between the property suburb and the Melbourne central business district, and use it to predict the property price for a suburb that is 15 kilometers from the central business district.

2. Now, we will consider the task of predicting property price (PRICE) from the distance between the property suburb and the Melbourne central business district (DISTANCE), number of rooms (ROOMS), and building size (BUILDING AREA).

(a) Using appropriate graphical displays, thoroughly assess the assumptions of linear regression for a linear regression of property price on distance between the property suburb and the Melbourne central business district, number of rooms, and building size. Additionally, be sure to assess multicollinearity for the independent variables.

(b) Assuming that the assumptions of linear regression hold (regardless of your conclusion from part (a)), which of these independent variables are significantly associated with property price? For each variable that is significantly associated with property price, interpret the slope term. Additionally, interpret the value of R².

(c) Is property price significantly associated with number of rooms and building size after adjusting for the distance between the property suburb and the Melbourne central business district? Use an appropriate analysis to address this. (You are not expected to check assumptions.)

3. Presentation marks. These marks are allocated based on:

structure, clarity, and tidiness of presented solutions/answers; and
correctness in spelling and grammar.

Attachment:- Assignment Files.rar

Reference no: EM131966277

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len1966277

5/2/2018 2:20:54 AM

Lecture Slides and data set is attached. This assignment covers material from Sessions 1-7 and is worth 30 marks. Your solutions should be properly presented, and it is important that you double-check your spelling and grammar and thoroughly proofread your assignment before submitting. Instructions for assignment submission are presented in the \Assignment 2" link and must be strictly adhered to. No marks will be awarded to assignments that are submitted after the due date and time. All analyses must be carried out using SPSS, and no marks will be awarded for assignment questions where SPSS output supporting your answer is not provided. Presentation marks. These marks are allocated based on: structure, clarity, and tidiness of presented solutions/answers; and correctness in spelling and grammar.

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