Numeracy and Data Analysis

This document provides information on numeracy and data analysis. It covers topics such as presenting data in table format, plotting data on line and column chart, computing descriptive statistics, and using linear forecasting model for predicting values.

Numeracy and Data Analysis

1. Presenting the data in the table format...............................................................................3
2. Potting the data on the line and column chart....................................................................3
3. Computing descriptive statistics........................................................................................4
4. Using the linear forecasting model for predicting the value for 15 and 20 day.................7

1. Presenting the data in the table format
S. No. Date
Data related to
humidity
1 24th December 2019 90%
2 25th December 2019 87%
3 26th December 2019 80%
4 27th December 2019 85%
5 28th December 2019 87%
6 29th December 2019 86%
7 30th December 2019 87%
8 31st December 2019 74%
9 1st January 2019 77%
10 2nd January 2019 81%
2. Potting the data on the line and column chart
Line chart
Column graph

3. Computing descriptive statistics
i. Mean
S. No. Date
Data related to
humidity
1 24th December 2019 90%
2 25th December 2019 87%
3 26th December 2019 80%
4 27th December 2019 85%
5 28th December 2019 87%
6 29th December 2019 86%
7 30th December 2019 87%
8 31st December 2019 74%
9 1st January 2019 77%
10 2nd January 2019 81%
Sum of humidity (x) 834%
Number of observation 10.00
Mean 83%
Interpretation- The above table shows that mean is the average value of an entire data
that is computed by dividing total of the humidity that resulted as 834% to that of total
number of an observation within the data that is 10 (Kahan and et.al., 2017). By following
this step a mean value equating to 83% that means the average humidity in last ten
consecutive days of Brasov city in Romania is seen as .83.

ii. Median
Step 1:
Date
Data related to
humidity
24th December 2019 90%
25th December 2019 87%
26th December 2019 80%
27th December 2019 85%
28th December 2019 87%
29th December 2019 86%
30th December 2019 87%
31st December 2019 74%
1st January 2019 77%
2nd January 2019 81%
Number of observation = 10
M = (10 + 1) / 2
= 5.5
M = (.87 + .86) / 2
= 1.73 / 2
= .86 or 86%
Interpretation- The above evaluation depicts that mid value of the humidity data for
the last 10 days accounted as 86% that in called as the median of the data set. It is calculated
by applying the formula that is (n+1)/2 where n is reflected as the number of observation
(Dolan and et.al., 2016). The resulted value attained as 5.5 so the average is been taken of the
5th and the 6th observation that equates to .87&.86.
iii. Mode
.87 or 87%
Interpretation- The above assessment shows that the value that is repeated for highest
number of times in the data accounted as 87% or .87. It is been computed by determining the
value that assessing the value that is occurred frequent number of times at different days. It is

known as the modal value or mode of the humidity data in last 10 days of the Brasov city in
the Romania.
iv. Range
Max: 90%
Min: 74%
Range: 90% – 74%
= .16 or 16%
Interpretation- The computation reflects that the difference between largest and the
smallest value ascertained as .16 or 16%. It is known as the range that is calculated by
subtracting minimum humidity value from the maximum humidity value that is 74% from the
90%. This means the range value lies between .90 and .74, it also indicates that the range of
humidity in Brasov within the previous 10 days.
v. Standard deviation
Date
Data related to
humidity (x) X^2
24th December 2019 0.90 0.81
25th December 2019 0.87 0.76
26th December 2019 0.80 0.64
27th December 2019 0.85 0.72
28th December 2019 0.87 0.76
29th December 2019 0.86 0.74
30th December 2019 0.87 0.76
31st December 2019 0.74 0.55
1st January 2019 0.77 0.59
2nd January 2019 0.81 0.66
Total 8.34 6.98
Standard deviation= Square root of ∑x^2 / N – (∑x / n) ^ 2
= SQRT of (6.98 / 10) – (8.34 / 10) ^ 2
= SQRT of .69 – .68
= SQRT of 0.01

= 0.1
Interpretation- It is represented from the above calculation that value of standard
deviation resulted as 0.1 which is depicted as the value that is spread over from the average or
the mean value (Watson, Handal and Maher, 2016). It is calculated by application of the
formula that putting up the value of x and its sum accordingly and thereafter computing
square root of the resulted value.
4. Using the linear forecasting model for predicting the value for 15 and 20 day

iii. Forecast for day 15 and 20
Date X
Data
related to
humidity (y) x*y X^2
24th December 2019 1 0.90 0.9 1
25th December 2019 2 0.87 1.74 4
26th December 2019 3 0.80 2.4 9
27th December 2019 4 0.85 3.4 16
28th December 2019 5 0.87 4.35 25
29th December 2019 6 0.86 5.16 36
30th December 2019 7 0.87 6.09 49
31st December 2019 8 0.74 5.92 64
1st January 2019 9 0.77 6.93 81
2nd January 2019 10 0.81 8.1 100
Total 55 8.34 44.99 385
m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
Y = mX + c
m = 10 (44.99) - (55 * 8.34) / (10 * 385) – (55)^2
m = (449.9 – 458.7) / (3850 – 3025)
m = -8.8 / 825
m = -0.010 or -1%
c = Σy – m Σx / N
c = 8.34 – (0.01 * 55) / 10

c = (8.34 – .55) / 10
c = 7.79 / 10
c = .779
computing value of Y by making use of m and c value
For 15 days-
Y = mX + c
= -0.01(15) + 0.77
= -0.15+0.77
= 0.62
For 20 days -
Y = mX + c
= -0.01(20) + 0.77
= -0.2 + 0.77
= 0.57
Interpretation- With an application of the linear forecasting model, the 15 and 20
days forecast is been made by following an equation that is Y= mX + C (Data analysis of
Brasov, 2018). This value of m and c resulted as – 0.01 & 0.77 so the 15th day .62 or 62% of
humidity is been estimated and for 20th day .57 or 57% of the humidity is anticipated.

REFERENCES
Books and journals
Dolan, J. G. and et.al., 2016. Should health numeracy be assessed objectively or
subjectively?. Medical Decision Making. 36(7). pp.868-875.
Kahan, D.M. and et.al., 2017. Motivated numeracy and enlightened self-
government. Behavioural Public Policy. 11. pp.54-86.
Watson, K., Handal, B. and Maher, M., 2016. The influence of class size upon numeracy and
literacy performance. Quality Assurance in Education. 24(4). pp.507-527.
Online
Data analysis of Brasov. 2018. [Online]. Available through:<
https://www.timeanddate.com/weather/romania/brasov/historic>