BABS Foundation: Numeracy and Data Analysis Assignment on Forecasting

This assignment demonstrates the application of descriptive analysis methods, including mean, median, mode, standard deviation, and range, on a humidity dataset from Cardiff. The solution includes data arrangement, presentation through charts, and the calculation of key statistical measures. Furthermore, it incorporates forecasting techniques using a regression equation to predict future humidity levels. The analysis culminates in a conclusion summarizing the importance of descriptive analysis for data understanding and forecasting future trends. The assignment also includes references to support the analysis.

Numercay

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
(1) Arranging data into table............................................................................................................1
(2) Presentation of data....................................................................................................................2
(3) Calculation of mean, median, mode and standard deviation......................................................2
(4) Forecasting and calculation of M and C.....................................................................................5
(1)................................................................................................................................................5
(2) Calculation of C.....................................................................................................................6
(3) Forecasting of humidity for 15th and 20th day........................................................................6
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................................8
Table 1Calculation of mean.............................................................................................................3
Table 2Median.................................................................................................................................3
Table 3Calculation of standard deviation........................................................................................4
Table 4Calculation of M..................................................................................................................5
Figure 1Column chart on humidity in Cardiff.................................................................................2
Figure 2Line chart on humidity in Cardiff......................................................................................2

INTRODUCTION
In the present research study demonstration of the use of descriptive analysis method is
shown. In this regard mean, median, mode, standard deviation and range method are applied on
the humidity related data set that is on Cardiff. Prediction is also made on basis of results
obtained through application of regression equation. At end of the research study conclusion
section is prepared.
(1) Arranging data into table
Year Humidity
25-12-19 96%
26-12-19 96%
27-12-19 99%
28-12-19 99%
29-12-19 91%
30-12-19 94%
31-12-19 90%
01-01-20 90%
02-01-20 98%
03-01-20 96%
1

(2) Presentation of data
Figure 1Column chart on humidity in Cardiff
Figure 2Line chart on humidity in Cardiff
(3) Calculation of mean, median, mode and standard deviation
Descriptive statistics is the one of the most important tool that is used by the firms and
analysts for doing analysis of data at basic level (Ho and Yu, 2015). This tool provides basic
level of information about variable to the analysts or it can be said that descriptive analysis tools
assist one in identifying direction in which variable is moving.
2

Mean
Table 1Calculation of mean
Year Humidity
25-12-19 96%
26-12-19 96%
27-12-19 99%
28-12-19 99%
29-12-19 91%
30-12-19 94%
31-12-19 90%
01-01-20 90%
02-01-20 98%
03-01-20 96%
Number of observations 10.00
Sum 949%
Mean 95%
=949/10 = 95%
Mean is the one of the most important tool of the descriptive statistics that is used for data
analysis purpose (Bonner, 2018). It indicates the average performance of the variable. In the
above table mean value of humidity is 95% which reflect that on an average basis humidity of
95% remain in the Cardiff. If in upcoming days humidity increase above given value then it can
be said that humidity increased above average level.
Median
Table 2Median
Year Humidity
31-12-19 90%
01-01-20 90%
29-12-19 91%
30-12-19 94%
3

25-12-19 96%
26-12-19 96%
03-01-20 96%
02-01-20 98%
27-12-19 99%
28-12-19 99%
Mid-point 5%
Mid-point = Number of observations/2
= 10/2
Median = (96%+96%)/2 = 96%
Median is the tool that classify data in to two parts (Pyrczak, 2016). It makes easy for one
to analyse large data set median value is 96% which is in between in above given data set. It can
be seen that below median value humidity level decline slightly but then it again gets increased.
In this way, median help one in analysing data set.
Mode
Mode value in present case is 99% because it is maximum value and repeating again.
Mode always indicate value that is repeating again and again in the dataset (Bolwell, Rogers, Ge
and Rosanowski, 2016). Thus, it can be said that mode help one in locating trends in the data set
that is repeating frequently.
Standard deviation
Table 3Calculation of standard deviation
Year Humidity Average
X-
Average (X-Average) ^2
31-12-19 90% 95% -5% 0.24%
01-01-20 90% 95% -5% 0.24%
29-12-19 91% 95% -4% 0.15%
30-12-19 94% 95% -1% 0.01%
25-12-19 96% 95% 1% 0.01%
4

26-12-19 96% 95% 1% 0.01%
03-01-20 96% 95% 1% 0.01%
02-01-20 98% 95% 3% 0.10%
27-12-19 99% 95% 4% 0.17%
28-12-19 99% 95% 4% 0.17%
Average 95% Total 1.11%
STDEV 0.00111
 
N
X 

2
2


1.11/10 = 0.00111
Standard deviation is the tool that indicate the variation that is happening in the values of
the variable (Shang, 2015). In present case value of standard deviation is 0.001 which is low and,
on this basis, it can be said that variable is deviating slightly from its mean value.
Range
It is the tool which indicate the difference that is between maximum and minimum value
of the data set (Range statistics., 2019). Maximum value in present case is 99 and minimum
value is 91. Thus, range is 8 which is small and is indicating that in small range value of the
variable is moving.
(4) Forecasting and calculation of M and C
(1)
Table 4Calculation of M
Year Humidity X X*Y X^2
25-12-19 0.96 1 0.96 1
26-12-19 0.96 2 1.92 4
27-12-19 0.99 3 2.97 9
28-12-19 0.99 4 3.96 16
29-12-19 0.91 5 4.55 25
30-12-19 0.94 6 5.64 36
5

31-12-19 0.90 7 6.3 49
01-01-20 0.90 8 7.2 64
02-01-20 0.98 9 8.82 81
03-01-20 0.96 10 9.6 100
Total 9 55 52 385
m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
Y = mX + c
M = 10(52) – (55*9) / (10*385) - 55^2
M = (520 – 495) / (3850 – 3025)
M = 25/ 825
M = 0.030 or 3%
(2) Calculation of C
c = Σy – m Σx / N
c = 9 – (0.03 * 55)/ 10
c = (9 – 1.65)/10
c = 7.35/10
c = 0.735
(3) Forecasting of humidity for 15th and 20th day
Y = Mx + c
= 0.03*15+0.735
= 1.185
Y = Mx + c
= 0.03*20+0.735
= 1.335
It is forecasted that on 15th day humidity will be 112% and same on 20th day will be 134%. Thus,
it can be said that in the upcoming time period it is expected that humidity percentage will be
increased at rapid pace. In order to make prediction equation mX +c is used value of M is
already calculated which is 0.03 and value of C is 1.335. In the formula value of X is changed
both times. First value of 15 is taken and obtained value is 112% and when value of 20 is placed
on X obtained value is 1.335.
6

CONCLUSION
On basis of above discussion, it is concluded that there is huge importance of descriptive
analysis tool for the analysts. This is because by using these methods basic understanding of the
variable can be developed in proper manner. Thus, before doing data analysis on large level
business firms usually prefer to make use of descriptive analysis tools so that in systematic way
data analysis process can be initiated. It is also concluded that in the upcoming time period
humidity level will increase.
7

REFERENCES
Books and Journals
Bolwell, C.F., Rogers, C.W., Gee, E.K. and Rosanowski, S.M., 2016. Descriptive statistics and
the pattern of horse racing in New Zealand. 1. Thoroughbred racing. Animal Production
Science. 56(1). pp.77-81.
Bonner, M.D., 2018. Descriptive statistics. Police Abuse in Contemporary Democracies. p.257.
Ho, A.D. and Yu, C.C., 2015. Descriptive statistics for modern test score distributions:
Skewness, kurtosis, discreteness, and ceiling effects. Educational and Psychological
Measurement. 75(3). pp.365-388.
Pyrczak, F., 2016. Making sense of statistics: A conceptual overview. Routledge.
Shang, H.L., 2015. Resampling techniques for estimating the distribution of descriptive statistics
of functional data. Communications in Statistics-Simulation and Computation, 44(3),
pp.614-635.
Online
Range statistics., 2019. [Online]. Available through:< https://explorable.com/range-in-statistics>.
8