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Real World Analytics - Assignment Sample

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Added on  2021-06-14

Real World Analytics - Assignment Sample

   Added on 2021-06-14

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Running head: REAL WORLD ANALYTICSREAL WORLD ANALYTICSName of StudentName of University
Real World Analytics -  Assignment Sample_1
REAL WORLD ANALYTICS1Table of ContentsPart A:..............................................................................................................................................2Description...................................................................................................................................2Task 1...........................................................................................................................................2Task 2...........................................................................................................................................8Task 3...........................................................................................................................................8Task 4...........................................................................................................................................9Part B.............................................................................................................................................101.................................................................................................................................................10
Real World Analytics -  Assignment Sample_2
REAL WORLD ANALYTICS2Part A: DescriptionHeating load and cooling load serve to guide the specifications of equipment for heating and cooling that are installed buildings. Therefore they are key variables to consider while designing energy efficient buildings when considering how to optimize energy consumption.The variables Heating load (Y1 or HL) and cooling load (Y2 or CL) are two variables being considered to be of interest in this paper. The independent variables, viz., relative compactness(X1) in percentage(in decimals),the surface area(X2) ,expressed in squared meter, the wall area(X3) in square meter, the roof area(X4) in squared meter and finally the overall height(X5) in meters are being considered as potential influences on the chosen response. The analysis was done in R using a sample of size of 300.Task 1Following instructions, the data file ENB18data.txt was downloaded from CloudDeakin into the R. Cooling Load or Y2 was selected as the variable of interest. The influence of the variables X1, X2, X3, X4 and X5 on Y2 and their individual natures were analyzed on the basis of the chosen sample and hence discussed. The graphical descriptive summary of the variables and the relationships between the cooling load and the variables X1, X2, X3, X4 and X5 are given as follows. The histogram of the variable cooling load measured in the unit KWh per square meter per annum is seen to have distribution which is skewed right with most values are seen to be towards the left tail or the lower side of the X-axis making its left tail more steep and right tail flatter and elongated than its left. The values lie between 10 to 50 KWh per square meter with mode being between 15 - 20KWh per square meter.
Real World Analytics -  Assignment Sample_3
REAL WORLD ANALYTICS3Figure 1The relative compactness values are observed to be between 0.6 and 1. The distribution ispositively or right skewed.Figure 2
Real World Analytics -  Assignment Sample_4

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