Management Data Analysis in Construction Sector- UK
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Added on 2020-06-04
Management Data Analysis in Construction Sector- UK
Added on 2020-06-04
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MANAGEMENT DATA ANALYSIS
TABLE OF CONTENTSINTRODUCTION...........................................................................................................................1Sample.............................................................................................................................................1Findings...........................................................................................................................................2Descriptive statistics....................................................................................................................2CONCLUSION................................................................................................................................8Further research...............................................................................................................................9Recommendation.............................................................................................................................9REFERENCES..............................................................................................................................10APPENDIX....................................................................................................................................11
INTRODUCTION Construction sector is one of the growing field in the UK however, due to impact ofrecession slowdown is observed in this sector. Sign of recovery are observed in the sector and itis identified that in mentioned sector construction of building for people residential purposeincreased at fast pace. However, commercial purpose building construction does not get thatmomentum which is seen in respect to residential buildings construction growth rate. It can besaid that revival comes in the UK construction sector but it does not happened at fast rate andthere are lot more things that need to be seen in the mentioned industry. In the present researchstudy data related to offers, square feet of premises, number of bedrooms and number ofbathrooms are analyzed (Gurran and Bramley, 2017). Price of premises and whether bricks areused in construction of building or not are also explained in detail. Data analytics is the one ofthe important key tool that can be used by the firms for data analysis purpose and it help them toidentify number of factors that need to be consider while making busines decisions. In presentresearch study varied statistical tests are applied on data and their results are interpreted in propermanner. In this regard, Gretl and excel software is used for data analysis purpose. By usingmentioned softwares t test aere applied and it is identified whether variable sample mean isdifferent from standard. This is done for multiple variables in the dataset. It can be said thatsystmatic approach is followed to carry out research work and to analyze data in proper mannerin order to develop broad understanding of the variable. At end of the report conclusion isformed and in this way entire research work is carried out (Shuyun and Yin, 2015). It must benoted that analysis of current data set will help firm in making strong business decisions. It canbe seen from table that different price levels are given in the dataset and due to this reason it isvery important to identify that what trends are going on in terms of square feet and bedrooms aswell as bathrooms. After doing thorough analysis finally conclusion section is prepared in thereport. SampleIn present research study sample of 129 people is taken and different questions wereasked from them in relation to neighbiurhood, number of offers that are made in the house,number of bedrooms and bathrooms there are in the house. Apart from this, price relatedinformation and bricks whether used or not are given in dataset. While analyzing dataset it isidentified that there are no missing values in dataset and due to this reason its cleaning is not1 | P a g e
required. Hence, it can be said that data is already prepared. There is no need to do any sort ofmodifications in dataset as it is already perfect and can be used for analysis purpose directly. FindingsDescriptive statisticsNBHD: Mean value of variable neighbourhood is 1.96 and its standard deviation is 0.80which means that neighbourhood is in traditional area. Mode value is 2 and this alsoreflect that similar results are revealed by mode. Hence, it can be said that most ofrespondents homes are in tradtional area.Offers: In case of offers it can be observed that mean value is 2.57 and standard deviationvalue is 1.06 which is reflecting that number of offers made on home purchase is 3 whichmeans that most of home owners receive offers for home sale three times in specificduration.Square foot: Square foot mean value is 2000 and standard deviation is 211.57. On otherhand, mode value is 1920 and it is reflecting that on average basis people homes are ofsquare foot 2000 and apart from this there are many other homes where square foot sizeis 1920. Bedroom: Mean value of bedroom is 3.092 and its standard deviation is 0.72 whichreflect that on average basis people have 3 bedrooms in their homes. Mode value is also 3and this is also indicating that on average basis and most of times respondents givesimilar reponse. Bathroom: It can be seen from table that mean value of variable is 2.44 and its standarddeviation is 0.51. On other hand, mode value is 2 which means that most of homes havetwo bathrooms and this number is deviating at very low rate. Price: Mean value of price is 260854 and standard deviation value is 53737 which makeit clear that most of times price of house remain 260854 and this value is deviating at atvery low pace. It can be said that price of product is flucutating at low or moderate rate. Brick: Table that is given in appendix is clearly reflecting that mean value of brick is1.32 and mode value is 1 which means that majority of homes are not made of bricks.Instead stones are used to prepare homes. 2 | P a g e
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