Numeracy Analysis Report: Data Compilation and Statistical Analysis

This report presents a comprehensive numeracy analysis of sleeping hours data. It begins with an introduction to data analysis and its significance, followed by the compilation of data in a table format. The report then visualizes the data using column and bar charts. The core of the report involves calculating statistical measures such as mean, mode, median, range, and standard deviation. Furthermore, a linear forecasting model (y = mx + c) is applied to predict sleeping hours for future days. The report concludes with a summary of the findings and references relevant sources, demonstrating the practical application of numeracy techniques in data interpretation and prediction. The report aims to demonstrate data analysis, statistical calculations, and forecasting for business entities.

Numeracy and data
Analysis

Contents
INTRODUCTION.......................................................................................................................................3
MAIN BODY..............................................................................................................................................3
CONCLUSION.........................................................................................................................................10
REFERENCES..........................................................................................................................................11

INTRODUCTION
Data analysis is a tool allowing us to access and compile data allowing valuable
knowledge. In other terms, data research primarily attempts to examine the data on which beliefs
are centered, whatever other data they aim to notify (Ballarini and Sloman, 2017). The report
deals primarily with the compilation of statistics, in which data refers to period of sleeping hours
on different days. Significant areas of calculation, such as mean, mode, medium and prediction
are also implemented according to the project study.
MAIN BODY
1. Arrangement of the data in a table format.
Day Sleeping hours
1 8
2 9
3 7
4 6
5 8
6 6
7 8
8 10
9 14
10 12
2. Presentation of the data using any two types of charts.
Column chart- A column diagram depicts a graphic representation in which the height of
each segment illustrates the values represented. Under this chart, tables with vertical bars
are commonly regarded as column panels.

1 2 3 4 5 6 7 8 9
0
2
4
6
8
10
12
14
16
8 9
7 6
8
6
8
10
14
Sleeping hours
Sleeping hours
Bar chart- Bar charts or bar graph are a schematic of categorical details explicitly commensurate
with their width and height or horizontal axes (Estrada-Mejia, De Vries and Zeelenberg, 2016).
Below a bar chart is presented of sleeping hour data in such manner:
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10 12 14 16
8
9
7
6
8
6
8
10
14
12
Sleeping hours
Sleeping hours
3. Calculation of followings:
Mean- Average numbers of all variables are more generally referred to as the statistical
analysis of a single random variable and it is called mean. Herein, below value of mean is

computed on the basis of sleeping hour data for consecutive ten days by using specific
formula that is as:
Mean= Sum of total variables/number of variables
Day Sleeping hours
1 8
2 9
3 7
4 6
5 8
6 6
7 8
8 10
9 14
10 12
Total of variables 88
Mean= 88/10
= 8.8
Mode- The most common variable in a set of data is regarded as a mode. In other words, it is
defined as a variable whose frequency is higher in a data series (Vignoles, 2016). Below
calculation of mode is done in such manner that is as:
Day Sleeping hours
1 8
2 9
3 7
4 6
5 8
6 6

7 8
8 10
9 14
10 12
On the basis of above table, this can be found out that number 8 has higher frequency of 3 times,
thus mode will be 8.
Median- The median is the middle value of the number set where no number is reproduced and
no integer variable happens. In other words, the median is the total amount of the top half of the
bottom half of the census, the collective or the statistical distribution of the possibility and
numerical hypothesis (Nogueira, Thai, Nelson and Oh, 2016). It may be recognized to be the
"upper" element for collection of data. Herein, below calculation of median is done in such
manner of given data set:
When data set is odd= (N+1)/2th item.
When data set is even= {N/2th item+ N/2th item + 1}2
The above mentioned data set is considered as even data set so formula will be accordingly.
Step one- arrangement of data in ascending order:
Day Sleeping hours
1 6
2 6
3 7
4 8
5 8
6 8
7 9
8 10

9 12
10 14
N= 10
M= (10/2th item + 10/2th item + 1)/2
= (5th item+ 6th item)/2
= (8+8)/2
= 8
Range- The difference between the higher and lower values is defined as the range. In other
words, the choice of data between the largest and the lowest statistical values is a difference
between the two. The differentiation is exceptional, because the length of the data gathering is
retrieved from the lowest meaning. However, the concept of dispersion has a more complex
meaning in descriptive analysis. Herein, below value of range is calculated in such manner:
Higher value= 14
Lower value= 6
Range= (14-6)
= 8
Standard deviation- This is a statistical tool of the allocation to the population of moderate or
expected value is considered as standard deviation. Many predictions are incorrectly predicted to
be below usual (Watson, Handa and Maher, 2016). The statistics are more generally followed by
a wide disparity of norms. The standard deviation is calculated in such manner of above data set
of sleeping hours:
Day Sleeping
hours
x-m (x-m) 2
m= 8.8
1 8 -0.8 0.64

2 9 0.2 0.04
3 7 -1.8 3.24
4 6 -2.8 7.84
5 8 -0.8 0.64
6 6 -2.8 7.84
7 8 -0.8 0.64
8 10 1.2 1.44
9 14 5.2 27.04
10 12 3.2 10.24
Total 59.6
Variance = [∑(x – m) 2 / N]
= (59.6/10)
= 5.96
Standard deviation= √ (variance)
= √5.96
= 2.44
4. 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
Day (x) Sleeping
hours (y)
x2 xy
1 8 1 8

2 9 4 18
3 7 9 21
4 6 16 24
5 8 25 40
6 6 36 36
7 8 49 56
8 10 64 80
9 14 81 126
10 12 100 120
55 88 385 529
10*(529)-(55)*(88)/10 (385)-(55) 2
= 5290-4840/3850-3025
= 450/825
= 0.54
Calculation of c:
c= [(∑y) / n]-m (∑x/n)
= [88/10]- 0.54 (55/10)
= 8.8-2.97
= 5.83
Forecasting for 11 and 15 days:
Forecasting for day 11:
y= mx+c

= 0.54*11+5.83
= 11.77 OR 12 hour
Forecasting for day 15:
= 0.54*15+5.83
= 13.93 OR 14 hour
CONCLUSION
On the basis of above project report this can be concluded that for business entities, data
analysis and its techniques are useful. The report concludes about different kinds of calculations
such as mean, mode and median as well as standard deviation. In the further part of report,
forecasting of 11 and 15 days is done in accordance of linear forecasting method.
REFERENCES
Books and journal:

Ballarini, C. and Sloman, S.A., 2017. Reasons and the “Motivated Numeracy Effect.”.
In Proceedings of the 39th annual meeting of the Cognitive Science Society (pp. 1580-
1585).
Estrada-Mejia, C., De Vries, M. and Zeelenberg, M., 2016. Numeracy and wealth. Journal of
Economic Psychology, 54, pp.53-63.
Vignoles, A., 2016. What is the economic value of literacy and numeracy?. IZA World of Labor.
Nogueira, L.M., Thai, C.L., Nelson, W. and Oh, A., 2016. Nutrition label numeracy: Disparities
and association with health behaviors. American journal of health behavior, 40(4),
pp.427-436.
Watson, K., Handal, B. and Maher, M., 2016. The influence of class size upon numeracy and
literacy performance. Quality Assurance in Education.