Real World Analytics Assignment

Added on - 30 May 2021

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Running head: REAL WORLD ANALYTICSREAL WORLD ANALYTICSName of StudentName of University
REAL WORLD ANALYTICS1Table of ContentsPart A:..............................................................................................................................................2Description of Data......................................................................................................................2Task 1...........................................................................................................................................2Task 2.........................................................................................................................................10Task 3.........................................................................................................................................10Task 4.........................................................................................................................................11
REAL WORLD ANALYTICS2Part A:Description of DataHeating and cooling load are quite significant consideration while determining thespecifics of the equipment used for heating and cooling purposes in eco-friendly , energy savingdesigns of buildings. It is why tools used for simulation of energy for predicting the tentativeenergy consumption behavior in a building are necessary.The paper considers the two parameters, Heating load (Y1) and Cooling load (Y2) asvariables of interest to be studied and analyzed. The study takes into account the independentvariables such as relative compactness (X1), Surface area (X2),wall area(X3),roof area (X4)and additionally theoverall height (X5) of the building as indicators or influencers of the twovariables heating load and cooling load. The data consists of data from 768 buildings which wassimulated via building simulator tool. The subsequent analysis as per the requirements of theassignment was done in R.Task 1The fileENB18data.txt, downloaded from CloudDeakin consists of the data as describedin the previous section which was used for the study. The data was saved in the workingdirectory of R software and thus loaded into R environment. It was assigned to a data matrix,named “the.data” as directed by the requirement file of the assignment. The matrix had 7columns storing data on the seven variables and 786 rows of observations on each of the sevenvariables.The assignment required only one variable of interest to be chosen between the heatingLoad (Y1) and the cooling load (Y2). Y1 was chosen as the study variable for this paper. Thusthis the paper deals with the analysis of the Y1 variable and its relationship with the otherindependent variables X1, X2, X3, X4, and X5. A sample of size 300 was chosen randomly fromamong the 768 rows of data, as directed by the assignment requirements and the ensuing analysiswas done on this sample.The following diagram shows the histogram of the variable Y1 or annual heating load(KWh / meter square). It is seen to follow a distribution which is positively skewed , whichmeans that most of the values of Y1 seem to be located along the left hand side of thedistributional curve or the left tail which makes its right tail more elongated than its left makingit positively or right skewed. The mode or the maximum frequency is found to be in the intervalbetween 15KWh per square meter and 20KWh per square meter.
REAL WORLD ANALYTICS3Figure 1The value of the variable X1 or relative compactness values more or less lie around thecenter of the distribution in the interval between 0.6% and 1%.
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