Report: Data Analysis of Eccleston Park Station Usage (2009-2018)

This report presents a data analysis of station usage data from Eccleston Park, UK, spanning the years 2009 to 2018. The analysis begins with the arrangement of data in a tabular format, followed by the representation of data through column and line charts, providing visual insights into usage trends. Key statistical calculations, including mean, median, mode, range, and standard deviation, are performed to understand the central tendencies and data dispersion. Furthermore, the report applies a linear forecasting model to predict future station usage, offering valuable insights for planning and resource allocation. The report concludes by summarizing the findings and highlighting the importance of data analysis in informed decision-making.

Numeracy and Data
Analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
1. Arrangement of data in tabular form.......................................................................................1
2. Representation of data in two different charts.........................................................................1
3. Calculation of mean, median, mode, range and standard deviation........................................3
4. Use of linear forecasting model...............................................................................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Data analysis is the process of cleansing, transforming and modelling data which is
prepare for the effective analyses (Chen and Yang, 2015). Main purpose of the data analysis is to
develop useful information which is beneficial for the evidence, conclusion or decision making
process. This report prepared on the station usage data of Eccleston Part of YK. This report
include the various topics such as, arrangement of data, representation of data in chart form,
various calculation of mean, median, mode etc. In addition, application of liner forecasting
method which help the organization to forecast future usage.
MAIN BODY
1. Arrangement of data in tabular form
Here is the 10 years of data regarding station usage of Eccleston Park from the period
2009 to 2018. All the the data information represented in the tabular form and it will be
mentioned below:
Years Station usage
1 43
2 141
3 119
4 144
5 34
6 39
7 45
8 48
9 88
10 249
2. Representation of data in two different charts
Column Chart:
1

Above chart represent the station usage of the Eccleston Park of UK which is different
from the another year of usage. In the initial year which is 2009, station usage is 43 which is
changed according to the time and it include the period of 10 years. It is clearly shows that,
station usage will fluctuate and at the end of 2018 it was 249. Usage in 2018 was rapidly grow
from 2017 which is clearly mention in the graph.
Line Chart:
2

As per above mention line chart, usage of 10 years of data of Eccleston Park. With the
help of line chart, external as well as internal parties can easily understand the growth of station
usage.
3. Calculation of mean, median, mode, range and standard deviation
Years Station usage
1 43
2 141
3 119
4 144
5 34
6 39
7 45
8 48
9 88
10 249
Total/ ∑X 950
Mean 95
Mode 24
Median 68
Range 215
Maximum 249
Minimum 34
Standard
deviation 66
Mean: It is arithmetic mean, which provide the average of given data or sample and it
will be calculated by total observation divided by number of observation (Figueres-Esteban,
Hughes and Van Gulijk, 2015). With the help of average functions mean will be calculated and it
will be represented below:
Formula: ∑X/ N
= 950/10
3

= 95
Median: It is the middle value of the available data series which is called median and it
will be calculated through dividing two parts of the arranged data in the ascending order.
Formula: If observations are even = N +1 /2
If observations are odd= N /2
As the data range is even so the formula which will be applied will be N +1 /2
= 10 +1 /2
= 5.5 observation
So mean will be 68
Mode: It is that value which is appear in the series for the maximum time called mode of
the series. Repeated number in the data series called mode and in the above table there is no
mode (Gatobu, Arocha and Hoffman-Goetz, 2016).
Range: It is the value which is come after the deduction of lower value in the higher
value and the resulted amount called range of the series. Basically it is analyse the value of lower
or maximum limit.
Formula: Maximum Value – Minimum value
= 249 – 34
= 215
Standard deviation: It is used for the valuation of data in the given series and it will
help the dispersion of data range which can be measured and calculated as square root of the
variance (Marks, 2015).
Year
Station
usage (x) x- mean (x-m)2
1 43 6 36
2 141 104 10816
3 119 82 6724
4 144 107 11449
5 34 -3 9
6 39 2 4
7 45 8 64
8 48 11 121
4

9 88 51 2601
10 249 212 44944
Total 76768
Formula: √ (variance)
Variance = [∑(x – mean)2 / N]
= 76768/ 10
= 76768.8 or 76769
So standard deviation will be
= √76769
= 277.07 or 277
4. Use of linear forecasting model
Below mention table represent the difference between the x and y. Here, x will be
consider as year and y is station usage.
Year (x)
Station
Usage (y)
1 43
2 141
3 119
4 144
5 34
6 39
7 45
8 48
9 88
10 249
Value of m is estimated value with the help of estimated model where formula of y =
mx+c
5

Value of M = M is the value of slope of line which shows the relationship between the x
and y variable. It include the following steps which is mentioned below:
M= Change in y/ change in x
For usage of station data y1 is15th year and y0 is 12th year.
Changes in x is 1 year
value of m = 15-12 /1
= 3
Value of c = C is the liner value which is always constant and it will not affected due to
change in value of x.
With the help of Forecast.linear function (x,yknownvalues,xknownvalues) station usage
indicator for 12th and 15th year (Nguyen and Lugo-Ocando, 2016). In the excel formula is used to
calculate the same is FORECAST(Value;Series_Y;Series_X). Calculation are as follows:
year Station usage
12 128
15 143
Station usage in 12th year: The calculations shows that station usage for 12th year will be
128.
Station usage in 15th year: For 15th year station usage will be 143.
CONCLUSION
From the above discussion, it has been concluded that data analysis provide information
which help the organization to use it in effective way. Different tools such as mean, median,
mode etc. used to identify the all aspect of data analysis in appropriate way.
6

REFERENCES
Books & journals
Chen, Y. and Yang, Z. J., 2015. Message formats, numeracy, risk perceptions of alcohol-
attributable cancer, and intentions for binge pp.37-55.
Figueres-Esteban, M., Hughes, P. and Van Gulijk, C., 2015, September. The role of data
visualization in railway big data risk analysis.
Gatobu, S. K., Arocha, J. F. and Hoffman-Goetz, L., 2016. Numeracy, health
Marks, G. N., 2015. School sector differences in student achievement in of School Choice. 9(2).
pp.219-238.
7