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BEE1034 - Economics for Management

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Added on  2021-04-05

BEE1034 - Economics for Management

   Added on 2021-04-05

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University of WestminsterLondon House Market: Multiple Regression Analysis of London House PricesEconomics for ManagementModule Code: 7BUSS001W.21
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University of WestminsterTable of Contents1.Abstract.....................................................................................................................32.Introduction..............................................................................................................33.Methodology and Results.......................................................................................33.1. Regression Analysis............................................................................................43.2. Scatter Charts......................................................................................................64.Conclusion................................................................................................................85.Appendix...................................................................................................................86.Bibliography...........................................................................................................112
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University of Westminster1. AbstractThe London house market is a key feature of the UK economy development due to it being an indicator of progress. Construction and building prices usually depend on multiple factors and as such, this report investigates some of those components that could be considered remarkable to it. The reader will be able to familiarize themselves with the existing relationships between London house prices and some variables that affect the same with the help of multiple regression analysis and Microsoft Excel. The model researched in this report is concluded to be a good one due to the value of the Significance F which is minuscule. The squared correlation coefficient (R2) also indicates that the model used is liable for 99% of the dependant variable’s variation. Scatter charts are explored and presented as well in order to perceive the correlation between London house prices and each of the different variables selected. The model can be useful for further research in the London house market.Keywords: Regression analysis, London house prices, Significance F, Scatter charts, Squared correlation coefficient, London house market.2. IntroductionIn this report, we will look at regression analysis which is the relation between a dependent variable and multiple independent variables. A standard multiple regression was performed. It was done so for the purpose of assessing the ability of gross domestic product, population, average income of borrowers and average mortgage advance to predict London house prices. Below, a step-by-step approach is shown on how to carry out a regression analysis and how to construe the same. This report looks at the specific model of this analysis, assesses it and provides explanation of the results. The method is helpful for evaluating the strength of the dependencies between the gross domestic product, population, average income of borrowers, average mortgage advance and London house prices.3. Methodology and ResultsFirstly, the data gathered from the Office of National Statistics (UK) for the years 2000 to 2018 is placed in separate columns in Excel starting from the dependant variable, London house prices, and then followed by the four dependant variables which were carefully selected in order to stem from the same source. 3
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University of WestminsterTable 13.1. Regression AnalysisSecondly, a regression analysis is carried out using the Data Analysis ToolPak. When inserting the data for this, the dependant variable, which in this case is London house prices, is selected for Y range. The value of Y is reliable on X which represents the independent variable(s). The latter, which in this instance is gross domestic product, population, average income of borrowers and average mortgage advance, is chosen when inputting the X range. The independent variables are used to define and/or forecast any changes that could affect the dependant variable. The outcome of the analysis is as seen below:Table 24
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