Data Analysis and Forecasting

Data Analysis and
Forecasting

Contents
INTRODUCTION...........................................................................................................................................3
MAIN BODY.................................................................................................................................................3
1. Arranging data into table format:.........................................................................................................3
2. Present above data using charts:..........................................................................................................4
3. Computation and highlight final value.................................................................................................5
4. Use of linear forecasting model i.e. y = mx + c to compute and make discussion on followings:.......7
CONCLUSION...............................................................................................................................................9
REFERENCES..............................................................................................................................................10

INTRODUCTION
Numeracy is understanding, skills, attitudes and criteria that are needed in a broad range of
possible conditions and situations in arithmetic. Whereas the concept personal information-
analysis is characterized as structured analysis, configuration, adjustment that provide accurate
and meaningful information (Falloon, 2016). Data collection is the process of data collection,
processing, recycling and modeling with the purpose of finding the necessary info. It expresses
the findings thus achieved, recommends observations and guides decision-making. In this report
consist of ten consecutive calls that taken by a person in 10 days. For this prepare bar chart and
line chart and calculation of mean, mode, median, range and standard deviation. Moreover,
calculate value of M, C and forecasting of number of cells.
MAIN BODY
1. Arranging data into table format:
The table below includes telephone number details for 10 consecutive days, as described:
Day Number of phone calls (per day)
1st day 18
2nd day 22
3rd day 26
4th day 30
5th day 25
6th day 35
7th day 15
8th day 30

9th day 15
10th day 30
2. Present above data using charts:
Bar chart: A bar chart or bar graph data acquired at levels or distances with rectangular
blocks corresponding to the quantities that they reflect. The bars may be horizontal or vertical
but they are normally vertical. The uniqueness of bar charts is that economic or business
statistics – such as effect on share prices, company earnings or sales numbers – can be results are
achieved at the graph with a single look. It makes an excellent platform for additional
fundamental indicators (Grotlüschen and et.al, 2019).
18 22
26
30
2535
15
30
15 30
Number of phone calls (per day)
1st day
2nd day
3rd day
4th day
5th day
6th day
7th day
8th day
9th day
10th day
Line chart: A line graph is a type of graph that used access data continually changing.
We plot line utilizing sharp corners connecting multiple points. Often, it's called a line map. The
line graph consists of two axes, defined as either axis 'x' and the axis 'y.' There are two axes in a
paragraph-graph. The x-axis of a line graph displays the events and the groups being measured
over time, and the y-axis represents the scale, that is a sequence of numbers based upon the data
and then being divided into regular spacing (Mata and et.al, 2015) .

1st
day 2nd
day 3rd
day 4th
day 5th
day 6th
day 7th
day 8th
day 9th
day 10th
day
0
5
10
15
20
25
30
35
40
18
22
26
30
25
35
15
30
15
30
Number of phone calls (per day)
Number of phone calls
(per day)
3. Computation and highlight final value
Mean: Data suggests quantification of the core-tendency of data points is mean of predictions
factor. This is the sum total of all data principles / statistics separated by data number.
For selected data sample of the size 10, its mean is calculated as follows:
Day Number of phone calls (per day)
1st day 18
2nd day 22
3rd day 26
4th day 30
5th day 25
6th day 35
7th day 15
8th day 30

9th day 15
10th day 30
∑x = Sum of all the phone calls 246
Mean = ∑x / n = 246 / 10 = 24.6
Mode: The mode is the number which most usually occurs in a collection of data. A collection of
figures may either have one mode, upwards of one mode, or no mode. Other common regression
coefficients metrics provide a format's mean, or mean, and a format's median, mean number.
This method reflects on the biggest value in data range which is 30.
Median: The median is a mere regression coefficient metric. They organize the measurements to
find the median in sequence from the lowest to the highest benefit. Whether there is an arbitrary
integer of results, the mid value is the norm. If there are even numbers of findings, the median of
the two mid value systems is the median (Mavilidi and et.al, 2018).
As here, selected data is of even number i.e. 10 days. Thus Median would be = [(10/2) + (10/2
+1) value] / 2 = (5th value + 6th value )/2 = (25 + 35)/2 = 30
Range: A database's spread is the distance between the greatest and minimum values in that set
of data. Range also has a simple and accurate-to-understand objective: to notify us rapidly and
effortlessly about how large the results are.
Minimum Range = Smallest Value i.e. 15
Maximum Range = Highest Value i.e. 35
Standard deviation: The standard deviation is being used to describe discrete performance in
line with both the average, and not reference variables. Moreover, the standard deviation, such as
the mean, is typically only acceptable whenever the maintained constant is not substantially

distorted or has outliers. The standard deviation is a calculation of the points distributed across a
sample group. Typically we’re investing in a sample standard deviation (Weil and et.al, 2015).
Day
Number of phone
calls (per day) X- mean (x-mean)2
1st day 18 -6.6 43.56
2nd day 22 -2.6 6.76
3rd day 26 1.4 1.96
4th day 30 5.4 29.16
5th day 25 0.4 0.16
6th day 35 10.4 108.16
7th day 15 -9.6 92.16
8th day 30 5.4 29.16
9th day 15 -9.6 92.16
10th
day 30 5.4 29.16
Mean = 24.6
Variance =
∑(x-mean)2 / n =
432.4 / 10 = 43.24
Standard
Deviation =
√∑(x-mean)2 / n
= √432.4 = 20.79
4. Use of linear forecasting model i.e. y = mx + c to compute and make discussion on followings:
Calculation of M
Day
Number
of phone
calls (per
day) X2 XY
1st day 18 1 18
2nd day 22 4 88
3rd day 26 9 234
4th day 30 16 480
5th day 25 25 625
6th day 35 36 1260

7th day 15 49 735
8th day 30 64 1920
9th day 15 81 1215
10th day 30 100 3000
X = 55 246
X2 =
385 9575
m = N∑xy- ∑x∑y / N∑ X2 - (∑x)2
= 10*9575 – 55*246 / 10*385 – (55)2
= 95750 – 13530/ 3850 – 3025
= 82220 / 825
= 99.66
Calculation of C
c = [(∑y / n)-m (∑x/n)]
= [(246 /10) - (99.66) (55/10)]
= 24.6 + 548.13
= 572.73
Forecasting of number of calls
 At 12th day:
y= m x+ c
= 99.66 *12 + 24.6
= 1195.92 + 24.6

= 1220.52 or around 1221 calls on 12th day
 At 14th day:
y= m x+ c
= 99.66 *14 + 24.6
= 1395.24 + 24.6
= 1419.84 or around 1420 calls on 14th day.
CONCLUSION
As per the above report it has been concluded that data analysis is important for business
to collect all the information and set a particular picture. To collect the entire amount requires
applying the different methods of the mean, mode, median and standard deviation. Along with,
make right decision to select the right assumptions.

REFERENCES
Books and Journals
Falloon, G., 2016. An analysis of young students' thinking when completing basic coding tasks
using Scratch Jnr. On the iPad. Journal of Computer Assisted Learning. 32(6). pp.576-
593.
Grotlüschen, A. and et.al, 2019. Vulnerable subgroups and numeracy practices: How poverty,
debt, and unemployment relate to everyday numeracy practices. Adult Education
Quarterly. 69(4). pp.251-270.
Mata, A. and et.al, 2015. Strategic numeracy: Self-serving reasoning about health
statistics. Basic and Applied Social Psychology. 37(3). pp.165-173.
Mavilidi, M. F. and et.al, 2018. Immediate and delayed effects of integrating physical activity
into preschool children’s learning of numeracy skills. Journal of experimental child
psychology. 166. pp.502-519.
Weil, A. M. and et.al, 2015. Proficiency of FPPI and objective numeracy in assessing breast
cancer risk estimation. Learning and Individual Differences. 43. pp.149-155.