Analysis of School Energy Efficiency, Expenditure, and Building Age

This report presents a detailed analysis of school energy data, focusing on energy efficiency, expenditure on energy-saving capital, and the age of school buildings and boilers. The study employs descriptive statistics, regression analysis, and correlation analysis to examine the relationships between these variables. The report begins with descriptive statistics, summarizing data on school size, building age, energy efficiency, and expenditure across different school types. Graphical representations are used to illustrate key findings, such as the distribution of school types and building ages. Regression analysis is then used to investigate the extent to which energy efficiency, expenditure, and boiler age differ by school type, testing several hypotheses. Finally, the report explores the relationship between energy efficiency and total expenditure on energy-saving costs using correlation analysis. The analysis reveals the relationships between different factors affecting energy consumption and expenditure in schools, offering insights for school managers and policymakers. The report provides detailed statistical tables and graphs to support the findings, offering a comprehensive overview of the school energy landscape.

Table of Contents
INTRODUCTION...........................................................................................................................3
a. Descriptive statistics of school size, age of buildings, energy efficiency and expenditure on
energy saving capital across all schools......................................................................................3
b. Analyzing the extent to which energy efficiency, expenditure on energy saving capital and
the age of the main boiler differs by type of school....................................................................7
c. Analyzing the relationship between energy efficiency and to total expenditure on saving
cost...............................................................................................................................................9
d. Analyzing the extent to which two variables are related with each through the means of chi-
square test..................................................................................................................................10
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12
APPENDIX....................................................................................................................................14
APPENDIX 1.............................................................................................................................14
b.................................................................................................................................................14
Appendix 2.................................................................................................................................14
b. 2nd Hypothesis........................................................................................................................14
Appendix 3: 3rd Hypothesis........................................................................................................15
Appendix table 4: Computation of chi-square test....................................................................16

INTRODUCTION
Data analysis may be defined as a process which in turn provideshigh level of assistance
in determining suitable solution from the given data set. Statistical and non-statistical are the
main tools that are undertaken by researcher for assessing the research issue more effectively and
efficiently. In the present study, data in relation to theenergy aspect ofinfant to nursery, primary
and secondary school has been evaluated by the scholar through the means of excel. Hence, the
present report will shed light on the manner through which large amount of data can be analyzed
in a highly structured way. Further, it will also provide deeper insight about themanner through
which excel tools facilitate effective decision making. Report will highlight the extent to which
excel options help in simplifying the large data set in a highly structured way. It also depicts how
statistical tool such as correlation assists in testing hypothesis.
a. Descriptive statistics of school size, age of buildings, energy efficiency and expenditure on
energy saving capital across all schools
Descriptive statistics: It is one of the most effectual tools which provide deeper insight
about the mean, mode, median, minimum and maximum value. Hence, descriptive statistics tool
helps in summarizing the data set in the best possible way (Shirakawa, Abe and Ito, 2016). By
taking into consideration such tool researcher can also assess the extent to which data set will
deviate in the future. In this way, descriptive statistics tool renders information about the
variabilityas well as mean and middlevalue.
In order to provide school manager with highly effective solution for decision making
sample of 84 has been undertaken by the scholar.Hence, data in relation to the various aspects
have been gathered such as age of building, boiler, average building efficiency, energy saving
expense etc. Thus, by evaluating allquantitative data set researcher can provide personnel with
effectual framework for decision making. In this, by making assessment of data through the
means of excel researcher can present fair outcome of issue which is going is to be investigated.
The rationale behind the selection and use of excel is to arrange and determine suitable solution
from the given data set (Holcomb, 2016).

Calculation of descriptive statistics
Elements of
descriptive
statistics
School
type
Age of
building
Total
expenditure
on energy
saving
capital since
2011
Mean 1.90 52.95 7485.81
Standard Error 0.07 3.26 806.20
Median 2.00 49.00 6207.00
Mode 2.00 39.00 0.00
Standard
Deviation 0.64 29.67 7344.86
Skewness 0.08 0.78 0.91
Range 2.00 132.00 28202.00
Minimum 1.00 5.00 0.00
Maximum 3.00 137.00 28202.00
Sum 158.00 4395.00 621322.00
Count 83.00 83.00 83.00
Table 1: Calculation of descriptive statistics
School type

Infant & Nursery Primary Secondary
0
10
20
30
40
50
60
Frequency on the basis of Type of
school
Frequency
School Type
Graph 1: School type
The above depicted graphical presentation shows that frequency of primary school was
higher such as 49. Out of the number of 84, frequency of infant and secondary school accounts
for 21 and 13. Thus, it can be stated that in survey, most of the participants belonged from
primary school rather than others.
Age of building
5-
14 15-
24 25-
34 35-
44 45-
54 55-
64 65-
74 75-
84 85-
94 95-
104 105
-
114
115
-
124
125
-
134
135
-
144
0
2
4
6
8
10
12
14
16
18
Frequency
Frequency
Age
Frequency
Graph 2: Age of building

Graph clearly shows that age of 14 building was approximately within the range of 35-44
years. On the other side, 17 buildings were in the age of 55-64 years at the time of 2015. Thus,
buildings which were undertaken for the purpose of study considered as too old approximately
31.
Total expenditure on energy saving
0
417
897
2,730
3,992
4,551
6,207
7,327
7,903
8,802
10,210
12,156
13,503
16,506
18,640
23,300
28,202
0
2
4
6
8
10
12
14
16
18
frequency
frequency
In £
Frequency
Graph 3: Total expenditure on energy saving
By doing assessment of data set, it has been found that mean expenses incurred on saving
total energy accounts for £7485.81 respectively. On the other side, middle level expenditure
implies for £6207 significantly. Further, amount of standard deviation accounts for £7344.86
which entails that in the near future expenses related to total energy saving will deviate from
such aspect.
Building energy efficiency
Particulars 2014 2013 2012 2011
Mean 195.095
213.75
8
194.24
3
201.37
5

Standard
Error 7.42965
7.8017
3
16.417
4
9.4248
9
Median 179.197
197.57
3
162.02
1
185.82
1
Standard
Deviation 67.6873
71.077
2 149.57
85.864
8
Sample
Variance 4581.57
5051.9
6
22371.
2
7372.7
6
Kurtosis 3.14741
0.6440
3
49.486
9
2.3190
2
Skewness 1.23581
0.6213
1
6.3394
7
1.0874
3
Range 435.994
372.45
6
1339.8
6
511.80
4
Minimum 42.9058
43.202
9
43.278
3 14.628
Maximum 478.9
415.65
9
1383.1
4
526.43
2
Sum 16192.9
17741.
9
16122.
2
16714.
1
Count 83 83 83 83
Table 2: Building energy efficiency
Average building energy efficiency
33.70307971
93.13313892
152.5631981
211.9932573
271.4233166
330.8533758
390.283435
449.7134942
509.1435534
More
0
20
40
Histogram
Frequency
Bin
Frequency
Graph 4: Average building energy efficiency
2014-15

2014-15
Mean
194.68
8
Standard Error
7.3519
6
Median
177.71
4
Mode #N/A
Standard Deviation
67.381
8
Sample Variance
4540.3
1
Kurtosis
3.2132
2
Skewness 1.2542
Range
435.99
4
Minimum
42.905
8
Maximum 478.9
Sum
16353.
8
Count 84
42.9-52.9
102.9-112.9
122.9-132.9
142.9-152.9
162.9-172.9
182.9-192.9
202.9-212.9
222.9-232.9
242.9-252.9
262.9-272.9
282.9-292.9
352.9-362.9
472.9-482.9
0
2
4
6
8
10
12
Building energy efficiency 2014-15
F
KWH / M2
Frequency
2013-14

2013-14
Mean 213.212
Standard Error 7.72758
Median 197.163
Mode #N/A
Standard
Deviation 70.8244
Sample Variance 5016.1
Kurtosis 0.67271
Skewness 0.64057
Range 372.456
Minimum 43.2029
Maximum 415.659
Sum 17909.8
Count 84
43.2-53.2
93.2-103.2
123.2-133.2
143.2-153.2
163.2-173.2
183.2-193.2
203.2-213.2
223.2-233.2
243.2-253.2
263.2-273.2
283.2-293.2
313.2-323.2
353.2-363.2
403.2-413.2
0
2
4
6
8
10
Building energy efficiency 2013-14
Frequency
KWH/ m2
Frequency
2012-13
2012-13
Mean 193.515
Standard Error 16.2372
Median 161.923
Mode #N/A
Standard
Deviation 148.816

Sample Variance 22146.2
Kurtosis 50.0001
Skewness 6.37108
Range 1339.86
Minimum 43.2783
Maximum 1383.14
Sum 16255.3
Count 84
0
4
8
12
16
Building energy efficiency 2012-13
Frequency
KWH / M2
Frequency
2011-12
2011-12
Mean 200.815
Standard Error 9.32882
Median 185.533
Mode #N/A
Standard
Deviation 85.5001
Sample Variance 7310.27
Kurtosis 2.37362
Skewness 1.10588
Range 511.804
Minimum 14.628
Maximum 526.432
Sum 16868.4
Count 84

0
4
8
12
Building energy efficiency 2011-12
Frequency
KWH / M2
Frequency
Interpretation of descriptive statistics mentioned above
From descriptive statistics or analysis, it has been assessed that from the sample size of84
most of the schools are primary. Further, median and mode of the data set is 2 respectively. This
aspect also shows that 50% of data set represent thatresearcher has included primary school in
the investigation to a great extent as compared to others such as nursery and secondary (Bolwell
and et.al., 2016).Standard deviation and variance is 0.63 &.40 which clearly shows that in the
near future figure will deviate or scattered from such amount. Range of school type is 2 which
statethe middle value of minimum and maximum level. Along with this, descriptive statistics
result show that average age of building is 53 years since 2015. On the other side, age of 50%
building is 49, whereas most of buildings were built before 39 years. Standard deviation and
sample variance of such variables are 29.67 & 880.14 respectively.
Further, by applying the tools of descriptive statistics it has been identified that mean
expenses which are made energy saving aspect is related to £7485.81. However,median level of
expense is related to the amount of £6207. Standard deviation is £7344.86 which shows that in
the near future changes will take place in level of expense with such figure. Along with this,
minimum and maximum value of expense is 0 & £28202 respectively. The above depicted table
shows that average building energy efficiency is 201.37, 194.24, 213.76 & 195.09 KW from
2011 to 2014. Along with this, median value of average taken from building efficiency is 185.82,
162.02, 197.57 &179.20 in2011, 2012, 2013 and2014. Standard deviation of given data setfrom

2011 to 2014 is 85.86, 149.57, 71.08 &67.69. By taking into account all such aspects, it can be
stated that manger of school needs to frame strategy by taking into consideration the aspects of
mean, median, mode and standard deviation (Faraway, 2016). Hence, tool of descriptive
statistics is highly significant which in turn provides clear and precise information to the
concerned authority regarding data set.
b. Analyzing the extent to which energy efficiency, expenditure on energy saving capital and the
age of the main boiler differs by type of school
Regression analysis tool of statistics help in determining the relationship which takes
place amongthe variables. Such statistical tool helps in analyzing the dependency of one
variable on others. By this, researchercan assess the extent to which changes takeplace in the
value of dependent variable in line with theindependent variable (Schroeder, Sjoquist and
Stephan, 2016). Linear and multiple are the main two types of regression whichin turn helps in
predicting the change that independent variables have on dependent factors.
Hypothesis 1
Ho: There is no significant difference in the mean value of average building energy efficiency
and school type.
H1: There is a significant mean difference in the mean value of average building energy
efficiency and school type.
By referring the appendix 1, regression table, it has been assessed that no or highly lower
relationship exists between the two factors such as energy efficiency and school type. R and R
square is 0.02 and0.00 respectively. Such aspect shows that both the variables are not highly
associated with each other. Further, R square entails that if changes take place in the school
typefrom primary to secondary then there will be no great impact on the aspects regarding
building of energy efficiency. Further, level of significanceis 0.84 which entails that alternative
hypothesis rejected. Hence, there is no mean difference in the value of school type and building
energy efficiency (Fagerland and Hosmer, 2016).
Hypothesis 2