Numeracy and Data Analysis

This document provides an overview of numeracy and data analysis. It covers topics such as reviewing data in tabular format, plotting data on graphs, calculating descriptive statistics, and using linear forecasting to predict future values. The document also includes examples and interpretations of the data. Study material and solved assignments on numeracy and data analysis are available on Desklib.

Numeracy and Data Analysis

Table of Contents
1. Reviewing the data in the tabular format ................................................................................3
2. plotting the data on the graph ..................................................................................................3
3. Calculating descriptive statistics .............................................................................................4
4. By making use of the linear forecasting mode in order to predict the value for 15th and the
20th day........................................................................................................................................7
REFERENCES................................................................................................................................1

1. Reviewing the data in the tabular format
S. No. Date Data related to humidity
1 29th December 2019 93%
2 30th December 2019 94%
3 31st December 2019 97%
4 1st January 2020 93%
5 2nd January 2020 86%
6 3rd January 2020 95%
7 4th January 2020 82%
8 5th January 2020 90%
9 6th January 2020 79%
10 7th January 2020 90%
2. plotting the data on the graph
Line chart

Column chart
0
0.2
0.4
0.6
0.8
1
1.2
Data related to humidity

3. Calculating descriptive statistics
a. Mean
S. No. Date Data related to humidity
1 29th December 2019 93%
2 30th December 2019 94%
3 31st December 2019 97%
4 1st January 2020 93%
5 2nd January 2020 86%
6 3rd January 2020 95%
7 4th January 2020 82%
8 5th January 2020 90%
9 6th January 2020 79%
10 7th January 2020 90%
Sum total of Humidity 899.00%
Total number of the
observation 10
29th December 2019
30th December 2019
31st December 2019
1st January 2020
2nd January 2020
3rd January 2020
4th January 2020
5th January 2020
6th January 2020
7th January 2020
0
0.2
0.4
0.6
0.8
1
1.2
93% 94% 97% 93% 86%
95%
82%
90%
79%
90%
Data related to humidity

Mean 89.90%
Interpretation- The above table reflects that the average value of the humidity data of
Nottingham relating to last 10 consecutive days resulted as 89.90%. It is been computed by
dividing the total of the humidity data that accounted as 899.00% to Total number of an
observation that 10 (Kim, Sefcik and Bradway, 2017). This means that averagely the humidity
in the Nottingham tended to be 89.90 that is 90% in the last 10 days.
b. Median
Step-1 : Arranging the data in ascending form
S. No. Date Data related to humidity
1 6th January 2020 79.00%
2 4th January 2020 82.00%
3 2nd January 2020 86.00%
4 5th January 2020 90.00%
5 7th January 2020 90.00%
6 29th December 2019 93.00%
7 1st January 2020 93.00%
8 30th December 2019 94.00%
9 3rd January 2020 95.00%
10 31st December 2019 97.00%
Step-2 : Applying formula (n+1)/2
Number of observation
Median= (10+1)/2
= 5.5
M = (.90 + .93) / 2
= 1.83 / 2
= .91 or 91%

Interpretation- From above analysis it has been interpreted that median value attained as
91% which is depicted as the mid value of an observation (Ma’arif, Motahar and Mohd Satar,
2018). The median value is computed by arranging the data of 10 consecutive days in ascending
order then the formula of median is employed that is (n+1)/2 which resulted 5.5 observation as
median value. As for computing the accurate value average of 5th and the 6th observation is made.
c. Mode
0.90 or 90%
Interpretation- The value of mode evaluated as 0.90 or in percentage value as 90% , that
indicates a value that is repeated higher number of times in the dataset (Colorafi and Evans,
2016). This means 90% of humidity has been seen as repeated in the last 10 days of the
Nottingham's humidity data.
d. Range
Max: 97%
Min: 79%
Range: 97% – 79%
= .18 or 18%
Interpretation- The range of the humidity data equated to 18% that lies between the
maximum and the minimum value of the data. This means that overall dispersion of the values in
the dataset is accounted as difference between largest value that is 97% and smallest value that is
79%.
e. Standard Deviation
Date Data related to humidity (x) X^2
29th December 2019 0.93 0.86
30th December 2019 0.94 0.88
31st December 2019 0.97 0.94
1st January 2020 0.93 0.86

2nd January 2020 0.86 0.74
3rd January 2020 0.95 0.90
4th January 2020 0.82 0.67
5th January 2020 0.9 0.81
6th January 2020 0.79 0.62
7th January 2020 0.9 0.81
Sum Total 8.99 8.1
Standard deviation= Square root of ∑x^2 / N – (∑x / n) ^ 2
= SQRT of (8.1 / 10) – (8.99 / 10) ^ 2
= SQRT of (.81 – .899)^2
= SQRT of 0.089^2
= SQRT of 0.007921
= 0.08
Interpretation- The evaluation states that value of standard deviation computed as 0.08
which reflected as the number that about the way in which measurement for the group is been
spread out from mean value or an expected value (Chambers IV, 2018). It is calculated by
making square root of the value resulted by application of an equation.
4. By making use of the linear forecasting mode in order to predict the value for 15th and the 20th
day
Date X
Data related to
humidity (Y) X*Y X^2
29th December
2019 1 0.93 0.93 1
30th December
2019 2 0.94 1.88 4
31st December
2019 3 0.97 2.91 9

1st January 2020 4 0.93 3.72 16
2nd January 2020 5 0.86 4.3 25
3rd January 2020 6 0.95 5.7 36
4th January 2020 7 0.82 5.74 49
5th January 2020 8 0.9 7.2 64
6th January 2020 9 0.79 7.11 81
7th January 2020 10 0.9 9 100
Sum Total 55 8.99 48.49 385
m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
Y = mX + c
M = 10 (48.49) - (55 * 8.99) / (10 * 385) – (55)^2
m = (484.9 – 494.4) / (3850 – 3025)
m = -9.5 / 825
m = -0.01 or -1%
c = Σy – m Σx / N
c = 8.99 – (-0.01 * 55) / 10
c = (8.86 – (-.55) / 10
c = 9.41 / 10
c = 0.94
computing value of Y by making use of m and c value
For 15 days-
Y = mX + c
= -0.01(15)+0.94
= -0.15+0.94
= 0.79
For 20 days -
Y = mX + c
= -0.01(20)+0.94
= -0.2+0.94
= 0.74

Interpretation- The above computation shows that the forecasted value of humidity for
15th day is seen as 0.79 or 79% and for 20th day it accounted as 0.74 or 74% (Humidity data of
Nottingham, 2018). It is been expressed as Y = mX + c where value of m and c is calculated by
following an equation and X is counted as the number of days for which the forecast has to be
made. The figures shows in the coming days the humidity in Nottingham tends to be decrease.

REFERENCES
Books and journals
Chambers IV, E., 2018. Consensus methods for descriptive analysis. Descriptive analysis in
sensory evaluation. pp.213-236.
Colorafi, K. J. and Evans, B., 2016. Qualitative descriptive methods in health science
research. HERD: Health Environments Research & Design Journal. 9(4). pp.16-25.
Kim, H., Sefcik, J. S. and Bradway, C., 2017. Characteristics of qualitative descriptive studies: a
systematic review. Research in nursing & health. 40(1). pp.23-42.
Ma’arif, M. Y., Motahar, S. M. and Mohd Satar, N. S., 2018. A descriptive statistical based
analysis on perceptual of ERP training needs. In Proceedings of 2018 International
Conference on Engineering, Science, and Application (ICESA 2018) (pp. 46-62).
Online
Humidity data of Nottingham. 2018. [Online]. Available through:
<https://www.timeanddate.com/weather/uk/nottingham/historic>
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