Numeracy and Data Analysis Report: Phone Call Descriptive Analysis

This report provides a comprehensive analysis of phone call data using various statistical methods. It begins with an introduction to numeracy and data analysis, emphasizing the application of descriptive statistics. The report presents data in both table and graphical formats, including line and column graphs. Descriptive statistics such as mean, median, mode, range, and standard deviation are calculated and interpreted to understand the central tendencies and dispersion of the data. Furthermore, the report utilizes a linear forecasting model to predict the number of phone calls for the 12th and 14th days, demonstrating the application of statistical techniques for future predictions. The conclusion summarizes the key findings and the references used in the report are listed at the end.

Numeracy and Data Analysis

Table of Content
INTRODUCTION...........................................................................................................................3
1. Presenting data in table form...................................................................................................3
2. graphical presentation of data..................................................................................................3
3. Determining descriptive statistics............................................................................................4
4. Using the linear forecasting model for predicting the value for 15 and 20 day.......................8
CONCLUSION..............................................................................................................................10
REFERENCES..............................................................................................................................11

INTRODUCTION
Numeracy and analysis refers to an application of statistical tool that includes descriptive
statistics for analyzing the average, middle and mode of the phone calls. The study highlights
computation of range and prediction for 12 and 14th day regarding the number of phone calls that
would be made.
1. Presenting data in table form
Sr. No. Date
phone
calls
per day
1
1st July
2020 7
2
2nd July
2020 6
3
3rd July
2020 9
4
4th July
2020 8
5
5th July
2020 4
6
6th July
2020 12
7
7th July
2020 10
8
8th July
2020 7
9
9th July
2020 4
10
10th July
2020 6
2. graphical presentation of data
Line graph

1st July 2020
2nd July 2020
3rd July 2020
4th July 2020
5th July 2020
6th July 2020
7th July 2020
8th July 2020
9th July 2020
10th July 2020
0
2
4
6
8
10
12
14
phone calls per day
phone calls per day
Column graph
1st
July
2020
2nd
July
2020
3rd
July
2020
4th
July
2020
5th
July
2020
6th
July
2020
7th
July
2020
8th
July
2020
9th
July
2020
10th
July
2020
0
2
4
6
8
10
12
14
phone calls per day
phone calls per day
3. Determining descriptive statistics
i. Mean
Sr. No. Date
phone
calls
per day
1 1st July 2020 7
2 2nd July 2020 6
3 3rd July 2020 9

4 4th July 2020 8
5 5th July 2020 4
6 6th July 2020 12
7 7th July 2020 10
8 8th July 2020 7
9 9th July 2020 4
10 10th July 2020 6
Sum total of phone
calls per day 73
No. of observation 10
Mean 7.3
Interpretation-The above analysis reflect that the mean value of the phone calls made in
the last 10 consecutive days accounted as 7.3. This value indicated as an average of calls for last
10 days that is been calculated through dividing the sum of phone calls with that of the total
number of the observation (Grebitus and Davis, 2019). It depicts that in the 10 days average no
of calls is made as 7.3 times which helps in assessing the calls in an adequate manner through the
use of appropriate formula.
ii. Median
Step 1: Presenting data in ascending order
Sr. No. Date
Sleepin
g Hours
per day
1 5th July 2020 4
2 9th July 2020 4
3 2nd July 2020 6
4
10th July
2020 6
5 1st July 2020 7
6 8th July 2020 7
7 4th July 2020 8
8 3rd July 2020 9
9 7th July 2020 10
10 6th July 2020 12
No. of
observation 10
M= (10+1)/2 5.5
M= (7+7)/2 7

Interpretation- From the above table it has been represented that the value median
resulted as 7 which seems as the mid value of the dataset (Laracy, Hojnoski and Dever, 2016). It
is been calculated by adding the 5th and 6th observations and then dividing it by 2 for getting
average value that attained as 7. It is been done because the observation resulted in decimal value
that is 5.5 by applying the formula as (n+1)/2.
iii. Mode
Date
phone
calls
per day
1st July
2020 7
2nd July
2020 6
3rd July
2020 9
4th July
2020 8
5th July
2020 4
6th July
2020 12
7th July
2020 10
8th July
2020 7
9th July
2020 4
10th July
2020 6
Mode = 7
Interpretation- The modal value resulted as 7 which is depicted as highest number of
time an individual value is been repeated within the dataset (Kaur, Stoltzfus and Yellapu, 2018).
This shows that 7 no. of times the call had been made frequently in the 10 consecutive days.
iv. Range

Particular
s Formula
No. of
time
Maximum 12
Minimum 4
Range
Largest value-
Smallest value 8
Interpretation- The assessment shows that range account as 8 by computing the
difference value in between smallest and largest times the phone call made that is 4 and 12
(Mishra and et.al., 2019). Range means as the value lies between highest & lowest phone call
and depicted as the difference value of those numbers.
v. Standard deviation
Date
phone
calls
per day X^2
1st July
2020 7 49
2nd July
2020 6 36
3rd July
2020 9 81
4th July
2020 8 64
5th July
2020 4 16
6th July
2020 12 144
7th July
2020 10 100
8th July
2020 7 49
9th July
2020 4 16
10th July
2020 6 36
Total 73 591
Standard deviation= Square root of ∑x^2 / N – (∑x / n) ^ 2
= SQRT of (591 / 10) – (73 / 10) ^ 2

= SQRT of 59.1 – 53.29
= SQRT of 5.81
= 2.41
Interpretation- The table reflects that standard deviation presented as 2.41 through an
application of statistical formula. It is computed by getting a square root of the value and
depicted as dispersion from the mean value.
4. Using the linear forecasting model for predicting the value for 15 and 20 day
iii. Forecast for day 12 and 14
Date X
phone
calls
per day X*Y X^2
1st July
2020 1 7 7 1
2nd July
2020 2 6 12 4
3rd July
2020 3 9 27 9
4th July
2020 4 8 32 16
5th July
2020 5 4 20 25
6th July
2020 6 12 72 36
7th July
2020 7 10 70 49
8th July
2020 8 7 56 64
9th July
2020 9 4 36 81
10th July
2020 10 6 60 100
Total 55 73 392 3025
m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2
Y = mX + c
m = 10 (392) - (55 * 73) / (10 * 3025) – (55)^2

m = (3920 – 4015) / (30250 – 3025)
m = -95 / 27225
m = -0.003
c = Σy – m Σx / N
c = 73 – (-0.003 * 55) / 10
c = (73 + 0.165) / 10
c = 73.165 / 10
c = 7.316
Computing value of Y by making use of m and c value
For 12 days-
Y = mX + c
= -0.003(12) + (7.316)
= 0.036 + 7.316
= 7.352
For 14 days -
Y = mX + c
= 0.003(14) + (7.316)
= 0.042 + 7.316
= 7.358
Interpretation- From the above table it has been represented that the value of m & c
calculated as -0.03 and 7.316 by applying the appropriate formula. Following this the forecast
has been made for 12th and 14th day regarding the number of phone calls that would be made
which resulted as 7.352 and 7.358 (Andrews, Gentzkow and Shapiro, 2018). Moreover, it is been
computed by applying an equation that is Y= mX + c where, m and c are putted as 0.003 & 7.316
with X as 12 & 14. His shows that at 12th day and 14th day same phone calls will be made that is
7 times.

CONCLUSION
The above report showed that the average value of the phone calls resulted as 7.3 and
median & mode as 7 times. It has also been analyzed that for 12 and 14th day around 7.3 number
of time phone calls will be made.

REFERENCES
Books and journal
Andrews, I., Gentzkow, M. and Shapiro, J. M., 2018. On the informativeness of descriptive
statistics for structural estimates (No. w25217). National Bureau of Economic Research.
Grebitus, C. and Davis, G. C., 2019. Does the new nutrition facts panel help compensate for low
numeracy skills? An eye‐tracking analysis. Agricultural Economics. 50(3). pp.249-258.
Kaur, P., Stoltzfus, J. and Yellapu, V., 2018. Descriptive statistics. International Journal of
Academic Medicine. 4(1). p.60.
Laracy, S. D., Hojnoski, R. L. and Dever, B. V., 2016. Assessing the classification accuracy of
early numeracy curriculum-based measures using receiver operating characteristic curve
analysis. Assessment for Effective Intervention. 41(3). pp.172-183.
Mishra, P. and et.al., 2019. Descriptive statistics and normality tests for statistical data. Annals of
cardiac anaesthesia. 22(1). p.67.