Numeracy and Data Analysis Report: Forecasting Phone Calls Data

This report presents a comprehensive analysis of phone call data collected over ten consecutive days. It begins by representing the dataset in a tabular format and visualizing the data through column and line graphs. The core of the analysis involves the application of descriptive statistics, including the calculation and interpretation of mean, median, mode, range, and standard deviation. These statistical measures provide insights into the central tendencies, spread, and distribution of the phone call 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 findings, highlighting the utility of descriptive values and forecasting in analyzing and predicting phone call patterns. The report references relevant literature to support the analysis.

Numeracy and Data
Analysis

Table of Content
INTRODUCTION...........................................................................................................................3
1. Representing the dataset in form of table................................................................................3
2. Plotting the data on graph........................................................................................................3
3. Presenting descriptive statistics table......................................................................................4
4. Predicting value for 12 & 14th day by making use of linear forecasting model......................8
CONCLUSION..............................................................................................................................10
REFERENCES..............................................................................................................................11

INTRODUCTION
Numeracy and data analysis means as applying the statistical tool for analyzing the data
in an effective manner which is been expressed in terms of numbers. The present report
highlights the data relating to the number of the phone calls made in the last 10 consecutive days.
Moreover, it presents the computation of descriptive values through an application of the
statistical techniques.
1. Representing the dataset in form of table
Sr. No. Date
phone
calls
per day
1
1st August
2020 5
2
2nd August
2020 4
3
3rd August
2020 6
4
4th August
2020 8
5
5th August
2020 4
6
6th August
2020 9
7
7th August
2020 10
8
8th August
2020 7
9
9th August
2020 6
10
10th August
2020 4
2. Plotting the data on graph
Column chart

1st August 2020
2nd August 2020
3rd August 2020
4th August 2020
5th August 2020
6th August 2020
7th August 2020
8th August 2020
9th August 2020
10th August 2020
0
2
4
6
8
10
12
5 4
6
8
4
9 10
7 6
4
phone calls per day
phone calls per day
Line graph
1st August 2020
2nd August 2020
3rd August 2020
4th August 2020
5th August 2020
6th August 2020
7th August 2020
8th August 2020
9th August 2020
10th August 2020
0
2
4
6
8
10
12
phone calls per day
phone calls per day
3. Presenting descriptive statistics table
a. Mean value
Sr. No. Date
phone
calls
per day
1 1st August 2020 5
2 2nd August 2020 4
3 3rd August 2020 6
4 4th August 2020 8
5 5th August 2020 4

6 6th August 2020 9
7 7th August 2020 10
8 8th August 2020 7
9 9th August 2020 6
10 10th August 2020 4
Sum phone calls 63
No. of observation 10
Mean 6.3
Interpretation- The above table shows evaluation of mea value that accounted as 6.3
which mean that an average value of the phone calls made are reflected as 6.3. It is computed by
dividing the total number of the observation with that of the total of phone calls.
b. Median value
Step 1- Arranging the data in form of ascending order
Sr. No. Date
phone
calls
per day
1
2nd August
2020 4
2
5th August
2020 4
3
10th August
2020 4
4
1st August
2020 5
5
3rd August
2020 6
6
9th August
2020 6
7
8th August
2020 7
8
4th August
2020 8
9
6th August
2020 9
10
7th August
2020 10

Step 2- Calculating value by applying (n+1)/2
No. of
observati
on 10
M= (10+1)/2 5.5
M= (6+6)/2 6
Interpretation- The assessment shows that value of median attained as 6 which is
calculated by arranging the dataset into ascending order and thereafter applying the formula that
is (n+1)/2 (Fisher and Marshall, 2019). As the value of n resulted as 5.5, an average of 5th and 6h
observation is taken that equates to 6. This how the median value calculated and is counted as the
mid-value of dataset.
c. Modal value
Date
phone
calls
per day
1st August
2020 5
2nd August
2020 4
3rd August
2020 6
4th August
2020 8
5th August
2020 4
6th August
2020 9
7th August
2020 10
8th August
2020 7
9th August
2020 6
10th August
2020 4
Mode = 4

Interpretation- The value of mode accounted as 4 which reflects the highest number of
time the phone calls are been repeated with the data (Bertrand and Goupil, 2020). Therefore, the
researcher has observed that 4 times the phone calls are made repeatedly.
d. Range
Particular
s Formula
Amoun
t
Maximum 10
Minimum 4
Range
Largest value-
Smallest value 6
Interpretation- The table depicts that the range evaluated as 6 which is determined by
subtracting smallest number that is 4 from the largest number as 10. This shows the number of
phone calls lies between minimum and maximum value.
e. Standard deviation
Date
phone
calls
per day X^2
1st August
2020 5 25
2nd August
2020 4 16
3rd August
2020 6 36
4th August
2020 8 64
5th August
2020 4 16
6th August
2020 9 81
7th August
2020 10 100
8th August
2020 7 49
9th August
2020 6 36
10th August
2020 4 16
Total 63 439

Standard deviation= Square root of ∑x^2 / N – (∑x / n) ^ 2
= SQRT of (439 / 10) – (63 / 10) ^ 2
= SQRT of 43.9 – 39.69
= SQRT of 4.21
= 2.05
Interpretation- The analysis reflects the standard deviation accounted as 2.05 by applying
the formula and computing square root of the value that is 4.21 (Stamler and et.al., 2015). This
shows the value that is dispersed from the mean.
4. Predicting value for 12 & 14th day by making use of linear forecasting model
Date X
phone
calls
per day X*Y X^2
1st August
2020 1 5 5 1
2nd August
2020 2 4 8 4
3rd August
2020 3 6 18 9
4th August
2020 4 8 32 16
5th August
2020 5 4 20 25
6th August
2020 6 9 54 36
7th August
2020 7 10 70 49
8th August
2020 8 7 56 64
9th August
2020 9 6 54 81
10th August
2020 10 4 40 100
Total 55 63 357 3025
1. Computing value of m
m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2

Y = mX + c
m = 10 (357) - (55 * 63) / (10 * 3025) – (55)^2
m = (3570 – 3465) / (30250 – 3025)
m = 105 / 27225
m = 0.0038
2. Calculating value of c
c = Σy – m Σx / N
c = 63 – (0.003 * 55) / 10
c = (63 – 0.165) / 10
c = 62.835 / 10
c = 6.28
3. Forecast for 12th and 14th day
Computing value of Y by making use of m and c value
For 12th day-
Y = mX + c
= 0.003(12) + (6.28)
= 0.036 + 6.28
= 6.316
For 14th day -
Y = mX + c
= 0.003(14) + (6.28)
= 0.042 + 6.28
= 6.322
Interpretation- The above results indicates that the value of m resulted as 0.003 by using
the equation through which the c value equated as 6.28. By using the value of c and m, forecast

for the coming days is been made through an equation that is y= mX + c (Bickel and Lehmann,
2016). Therefore, it has been observed that for 12th day around 6.316 phone calls are estimated
and for 14th day approx 6.322 phone calls are anticipated.
CONCLUSION
From the above report it has been summarized that descriptive values helps in analyzing
the average, mid and repeated value for which the phone calls would be made. Moreover, it also
helps in predicting the number of time the phone calls will be made for the 12th and 14th day.

REFERENCES
Books and journal
Bertrand, P. and Goupil, F., 2020. Descriptive statistics for symbolic data. In Analysis of
symbolic data (pp. 106-124). Springer, Berlin, Heidelberg.
Bickel, P. J. and Lehmann, E. L., 2016. Descriptive statistics for nonparametric models II.
Location. In Selected Works of EL Lehmann (pp. 473-497). Springer, Boston, MA.
Fisher, M. J. and Marshall, A. P., 2019. Understanding descriptive statistics. Australian Critical
Care. 22(2). pp.93-97.
Stamler, J. and et.al., 2015. INTERMAP: background, aims, design, methods, and descriptive
statistics (nondietary). Journal of human hypertension. 17(9). pp.591-608.