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Statistics for Analytical Decisions

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Added on  2020-05-08

Statistics for Analytical Decisions

   Added on 2020-05-08

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STATISTICS FORANALYTICAL DECISIONSSTUDENT ID:[Pick the date]
Statistics for Analytical Decisions_1
Executive SummaryThe objective of the given report is to provide recommendations to housing constructioncompany which wants to undertake a project in the coastal region of NSW. The samplehousing data is provided which considers the prices across dwelling type (house, unit), region(Sydney, Wollongong, Newcastle) and ocean view. The technique deployed to undertake theanalysis involves both descriptive and inferential statistical techniques. Based on theinferential statistics, it is highlighted that houses have higher prices as compared to units,prices of houses vary across the regions and houses with ocean view tend to result in a pricepremium. As a result, it makes sense for the construction company to consider the abovefactors and ensure that higher focus should be on houses which are located in Sydney andpreferably with ocean view. However, corresponding cost considerations are also pivotalwhich company must consider.1
Statistics for Analytical Decisions_2
Table of ContentsBusiness Problem.......................................................................................................................2Statistical Problem.....................................................................................................................3Analysis......................................................................................................................................3Descriptive Statistics..............................................................................................................3Inferential Statistics................................................................................................................5General Conclusion....................................................................................................................9Implications................................................................................................................................9References................................................................................................................................112
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Business ProblemA firm which is involved in housing construction wants to undertake a major housingdevelopment and needs to do analysis of the same for which information about the ongoingpricing and preferences of the customers need to be considered. A sample data has beenperformed in order to enable the analysis of the prices of houses and units that are located inWollongong, Sydney and New Castle. Through the analysis, the company can obtain vitalinformation in relation to the various combinations in terms of region, view and type whichcan fetch higher prices and essentially can also narrow down on the existing prices. Statistical ProblemThe given sample lists down the price of houses and units. It takes into consideration theregion and also whether ocean view is available or not. For this data, descriptive statisticstools need to be applied so as to highlight the key characteristics of the provided sample data.Further, based on the sample data inferences need to be derived based on the population datathrough the use of inferential statistics techniques such as hypothesis testing. Through thisstatistical analysis a comparison needs to be facilitated between various parameters whichcould highlight the prevailing price trends which could be then used by the company (Hair et.al., 2015).AnalysisDescriptive StatisticsThe descriptive statistics aims to describe the characteristics of the sample data availablethrough the use of various measures of central tendency and dispersion as they providevaluable information about the underlying shape and also helps in highlighting key trendswhich further can be validated for the population using the inferential statistical tools. Type of DwellingThe descriptive statistics as per the dwelling type are as highlighted below.3
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