7BUSS001W: Economics for Management

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University of WestminsterLondon House Market:Multiple Regression Analysis of London House PricesEconomics for ManagementModule Code: 7BUSS001W.21
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
University of Westminster1. AbstractThe London house market is a key feature of the UK economy development due to it being anindicator of progress. Construction and building prices usually depend on multiple factors and assuch, 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 Londonhouse prices and some variables that affect the same with the help of multiple regression analysisand Microsoft Excel. The model researched in this report is concluded to be a good one due to thevalue of the Significance F which is minuscule. The squared correlation coefficient (R2) also indicatesthat the model used is liable for 99% of the dependant variable’s variation. Scatter charts areexplored and presented as well in order to perceive the correlation between London house pricesand each of the different variables selected. The model can be useful for further research in theLondon house market.Keywords:Regression analysis, London house prices, Significance F, Scatter charts, Squaredcorrelation coefficient, London house market.2. IntroductionIn this report, we will look at regression analysis which is the relation between a dependent variableand multiple independent variables. A standard multiple regression was performed. It was done sofor the purpose of assessing the ability of gross domestic product, population, average income ofborrowers and average mortgage advance to predict London house prices. Below, a step-by-stepapproach is shown on how to carry out a regression analysis and how to construe the same. Thisreport 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 domesticproduct, population, average income of borrowers, average mortgage advance and London houseprices.3. Methodology and ResultsFirstly, the data gathered from the Office of National Statistics (UK) for the years 2000 to 2018 isplaced in separate columns in Excel starting from the dependant variable, London house prices, andthen followed by the four dependant variables which were carefully selected in order to stem fromthe same source.3
University of WestminsterTable 13.1. Regression AnalysisSecondly, a regression analysis is carried out using the Data Analysis ToolPak. When inserting thedata for this, the dependant variable, which in this case is London house prices, is selected for Yrange. The value of Y is reliable on X which represents the independent variable(s). The latter, whichin this instance is gross domestic product, population, average income of borrowers and averagemortgage advance, is chosen when inputting the X range. The independent variables are used todefine and/or forecast any changes that could affect the dependant variable. The outcome of theanalysis is as seen below:Table 24
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