Numeracy and Data Analysis

This document discusses the analysis of statistics and data using numerical equations for estimating future phenomenon. It includes the arrangement of data in a table format and the presentation of data using charts. The document also covers the calculation of mean, mode, median, range, and standard deviation. The conclusion highlights the importance of assessing recent trends and making predictions based on the data analysis.

Numeracy and Data
Analysis

Contents
INTRODUCTION.......................................................................................................................................3
MAIN BODY..............................................................................................................................................3
1. Arrangement of the data in a table format...........................................................................................3
2. Presentation of the data using any two types of charts.........................................................................3
CONCLUSION.........................................................................................................................................11
REFERENCES..........................................................................................................................................12

INTRODUCTION
Analysis of statistics and data requires the systematic use numerical or quantitative
equations for estimated details of the future phenomenon. This approach is primarily used to
approximate weather forecast data while retaining the reasonableness as reliable as possible
(Bennison, 2015). The new assignment will forecast data collection which is related with the bill
payments information in days ahead, based on past new month results. Statistical measures such
as mean, median and mode comprising scope and standard deviations can be used for more
numerical data analysis to this end.
MAIN BODY
1. Arrangement of the data in a table format
Month Type of bill Amount (in ‘00 pounds)
January Bank transfer bill 15
February Mobile bill 20
March Water bill 25
April News paper bill 15
May Internet bill 20
June Food bill 10
July Heating bill 10
August Television bill 15
September Grocery bill 20
October Transportation bill 25
2. Presentation of the data using any two types of charts
Column Chart: A column chart is a description of information in a chart. Column
diagrams show longitudinal lines uniformly around the graph, with both the properties axis
appearing on the left side of the line. A column diagram is a chart overview of the details.
Column graphs view consistently longitudinal lines across the chart, of both directions of
resources occurring on the left side of row.

Bank transfer bill
Mobile bill
Water bill
News paper bill
Internet bill
Food bill
Heating bill
Television bill
Grocery bill
Transportation bill
Janu
ary Febr
uary Mar
ch April May June July Aug
ust Sept
emb
er
Octo
ber
0
10
20
30
Amount (in ‘00 pounds)
Amount (in ‘00 pounds)
Bar chart: A diagram or bar chart is a graphic displaying normative data with circular
columns with measurements or intervals equal to the meanings they reflect. The bars can be
horizontal or vertical potable. Bar charts are really a form of diagram used during showing and
comparing the amount, intensity or other indicator (e.g. mean) of various distinct statistical
groups. The graph is built in this kind of way that the distances of the various bars are dependent
on the size of the group represented.
Bank transfer bill
Mobile bill
Water bill
News paper bill
Internet bill
Food bill
Heating bill
Television bill
Grocery bill
Transportation bill
JuneMarch September
0 5 10 15 20 25 30
Amount (in ‘00 pounds)
Amount (in ‘00 pounds)
Calculation of followings:

Mean: A mean is the basic mathematical estimate of two and sometimes more figures in
a set. The mean could be calculated in far more than one manner for a specified set of results,
such as the calculation imply technique that utilizes the amount of the statistics in the sequence,
and the linear mean process which is the avg of a variety of commodity. Moreover, almost all of
the way, all the main approaches of estimating a weighted average yield the same estimated
outcome.
Month Type of bill Amount (in ‘00 pounds)
January Bank transfer bill 15
February Mobile bill 20
March Water bill 25
April News paper bill 15
May Internet bill 20
June Food bill 10
July Heating bill 10
August Television bill 15
September Grocery bill 20
October Transportation bill 25
Total 175
Mean = Sum of all values/number of values
= 175/10
= 17.5
Mode: The mode is the amount that most later appeared within a package. A collection of
figures could have one style, one method, or no method at all. Many common descriptive
statistics indicators provide a set's standard, or entail, and a format's median, center price.
Month Type of bill Amount (in ‘00 pounds)
January Bank transfer bill 15
February Mobile bill 20
March Water bill 25

April News paper bill 15
May Internet bill 20
June Food bill 10
July Heating bill 10
August Television bill 15
September Grocery bill 20
October Transportation bill 25
Total 175
The term 15 has higher frequency thus, mode is 15.
Median: In the string of numbers the "median" is the "middle" value. To determine the median it
is important to list statistics in sequential order from the lowest to the highest, and it might be
important to update the calculation until the median could be found. In an ordered, increasing or
declining series of values, the median is the center figure and may be more representative of that
data point than the norm.
When data set is odd= (N+1)/2th item.
When data set is even= {N/2th item+ N/2th item + 1}2
As per the above mentioned data set, this can be found out that data set is even so median will be
calculated as follows:
First of all, the data set needs to be arranged in ascending order:
Month Type of bill Amount (in ‘00 pounds)
January Bank transfer bill 10
February Mobile bill 10
March Water bill 15
April News paper bill 15
May Internet bill 15
June Food bill 20

July Heating bill 20
August Television bill 20
September Grocery bill 25
October Transportation bill 25
Total 175
N= 10
M= (10/2th item + 10/2th item + 1)/2
= (5th item+ 6th item)/2
= (15+20)/2
= 17.5
Range: A data type range is the collection of values the feature reaches if extended to all of its
sources. In the analogy of the feature device, the range is the collection of artifacts which
probably come out of another system if all the parameters are the inputs (Scott, 2016).
Higher value = 25
Lower value = 10
Range = 25-10
= 15
Standard deviation: Standard deviation is calculation of the distribution in its average of a data
point. The total variance of a variable is measured; the greater the diffusion or variation, the
stronger the confidence interval and the stronger the extent of the variance of the variable from
its standard.
Month Type of bill Amou
nt (in
x-m (x-
m) 2

‘00
pound
s)
January Bank
transfer
15
-2.5 6.25
February Telephone
bill
20
2.5 6.25
March Water bill 25
7.5
56.2
5
April News paper
bill
15
-2.5 6.25
May Internet bill 20 2.5 6.25
June Food bill 10
-7.5
56.2
5
July Heating bill 10
-7.5
56.2
5
August Television
bill
15
-2.5 6.25
Septemb
er
Grocery bill 20
2.5 6.25
October Transportati
on bill
25
7.5
56.2
5
262.
5
Variance = [∑(x – mean) 2 / N]
= (262.5/10)
= 26.25

Standard deviation: √ (variance)
= √26.25
= 5.12
1. linear forecasting model which is y = mx + c in order to do below mentioned
calculations:
 Calculation of value m:
Y= mx+c
m= n (∑xy) - (∑x) (∑y)/ n(∑x2)-( ∑x)2
Number
of
month
(x)
Amount
(y)
x2 xy
1 15 1 15
2 20 4 40
3 25 9 75
4 15 16 60
5 20 25 100
6 10 36 60
7 10 49 70
8 15 64 120
9 20 81 180
10 25 100 250

55 175 385 970
= 10(970)-(55)*(175)/10(385) -(55) 2
= 9700- 9625/3850-3025
= 75/825
0.09
Calculation of c:
c= [(∑y) / n]-m(∑x/n)
= (175/10)-0.09(55/10)
= 17.5-0.49
= 17.01
Forecast the expenses for month 12 and 14:
Forecasting for month 12:
y= mx+c
= 0.09*12+17.01
= 1.08+17.01
= 18.09
Forecasting for month 14:
= 0.09*14+17.01

= 1.26+17.01
= 18.27
CONCLUSION
In this overall analysis, it was determined that in order to estimate the future analysis
from a variety of quantitative approaches, the main tendency applies to being the best strategy
would continue to assess the recent trend as well as make predictions information too.

REFERENCES
Books and Journal
Bennison, A., 2015. Developing an analytic lens for investigating identity as an embedder-of-
numeracy. Mathematics Education Research Journal. 27(1). pp.1-19.
Hopp, T., 2015. The effect of numeracy on the relationship between state reactance and message
evaluations. Communication Research Reports 32(4). pp.314-321.
Hassinger-Das, B., Jordan, N. C. and Dyson, N., 2015. Reading stories to learn math:
Mathematics vocabulary instruction for children with early numeracy difficulties. The
Elementary school journal. 116(2). pp.242-264.
Scott, F. J., 2016. An investigation into students’ difficulties in numerical problem solving
questions in high school biology using a numeracy framework. European Journal of
Science and Mathematics Education 4(2). pp.115-128.