This document provides a comprehensive guide on descriptive data analysis for phone calls. It covers topics such as creating tables, graphical presentation, mean, median, range, and standard deviation. It also explains the concept of linear forecasting.
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TABLE OF CONTENTS TABLE OF CONTENTS................................................................................................................2 INTRODUTION..............................................................................................................................1 TASK...............................................................................................................................................1 1. Creating a table for the data related to phone calls..................................................................1 2. Presentation of data for phone calls in graphical format.........................................................1 3. Mean, Median, range and standard deviation..........................................................................3 4 Linear Forecasting....................................................................................................................6 CONCLUSION................................................................................................................................7 REFERENCES................................................................................................................................8
INTRODUTION Descriptive analysis is important and first step in conducting the statistical analyses. It helps in appropriate distribution of the data which helps in detecting the typos and outliers and enable them to identify the association among the variables, enabling them to make further research. TASK 1.Creating a table for the data related to phone calls Sr. No.Date Phone calls per day 11st August 20204 2 2nd August 20206 3 3rd August 20208 4 4th August 20204 5 5th August 20208 6 6th August 20203 7 7th August 20207 8 8th August 20209 9 9th August 20204 10 10th August 20202 2. Presentation of data for phone calls in graphical format 1
2.1 Column Chart 01-Jul- 2002-Jul- 2003-Jul- 2004-Jul- 2005-Jul- 2006-Jul- 2007-Jul- 2008-Jul- 2009-Jul- 2010-Jul- 20 12345678910 0 1 2 3 4 5 6 7 8 9 Phone calls per day Phone calls per day 2.2 Line Chart 30/Jun/2002/Jul/2004/Jul/2006/Jul/2008/Jul/2010/Jul/2012/Jul/20 0 1 2 3 4 5 6 7 8 9 10 Phone calls per dayPhone calls per day days no of phone calls 2
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3. Mean, Median, range and standard deviation 3.1 The mean It is the metric that is used for finding the average value of the given data in statistics. It is the first step in statistics which is used for analysing data (Jiang and Yu, 2017). Sr. No.Date Phone calls per day 11st August 20204 22nd August 20206 33rd August 20208 44th August 20204 55th August 20208 66th August 20203 77th August 20207 88th August 20209 99th August 20204 1010th August 20202 Sum total of phone calls55 No. of observation10 Mean5.5 Inthegivendatameaniscomputedbysumofobservationsdividedbynoof observations. It is essential for the experts to identify the average occurrence of value in given data set. Mean in the data for phone calls is 5.5. 3.2 The Median It could be described as the measure of the central tendency in the descriptive analysis. it is used by the experts and researchers to identify the mid values. Sr. No.Date Data in relation to phone calls per day 11st August 20204 22nd August 20206 33rd August 20208 44th August 20204 55th August 20208 66th August 20203 77th August 20207 3
88th August 20209 99th August 20204 1010th August 20202 No. of observation55 M=(10+1)/25.5 M=(8+3)/25.5 Median for the phone calls in a day is calculated by averaging the mid values which are values of 5thand 6thday that are 8 and 3. On averaging the two values mid value is computed as 5.5 3.3 The Mode Mode in the descriptive analysis is used for identifying the values which is occurring most commonly. Mode is also known as the mean value (Olm and et.al., 2018). Date Phone calls per day 1st August 20204 2nd August 20206 3rd August 20208 4th August 20204 5th August 20208 6th August 20203 7th August 20207 8th August 20209 9th August 20204 10th August 20202 Mode =4 Mode of the data set for call is found as 4 which shows that in 10days user has received 4 calls for 3 times. 3.4 The Range 4
Range in statistics could be described as the method which is used for identifying difference of minimum and maximum values. ParticularsFormulaAmount Maximum9 Minimum2 Range Largest value-Smallest value7 Range of the given call is 7 as highest value is 9 and lowest value is 2 and difference between them is 7. 3.5 The standard deviation It could be defined as the metric used for identifying dispersion of the outcomes from the mean values (Krieg, 2019). DatePhone calls (X)X^2 1st August 2020416 2nd August 2020636 3rd August 2020864 4th August 2020416 5th August 2020864 6th August 202039 7th August 2020749 8th August 2020981 9th August 2020416 10th August 202024 Total55355 Standard deviation= Square root of ∑x^2 / N – (∑x / n) ^ 2 SQRT of (355 / 55) – (55 / 10) ^ 2 SQRT of 6.45 – 30.25 SQRT of -23.79 = 4.88 5
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Standard deviation of the given data set is 4.88 which is not very high therefore the dispersion from the mean values is less. 4 Linear Forecasting DateX Phone calls (Y)X*YX^2 1st August 20201441 2nd August 202026124 3rd August 202038249 4th August 2020441616 5th August 2020584025 6th August 2020631836 7th August 2020774949 8th August 2020897264 9th August 2020943681 10th August 202010220100 Total5555291385 4.1 “m” value m = NΣxy – Σx Σy / NΣ x^2 – (Σx)^2 m = 10 (291) - (55 * 55) / (10 * 385) – (55)^2 m = (2910 – 3025) / (3850 – 3025) m = -115 / 825 m = -0.14 4.2 “c” value c = Σy – m Σx / N c = 55 – (-0.14 * 55) / 10 c = (55 - 7.7) / 10 c = 47.3 / 10 c = 4.73 6
4.3 Day 12 Forecasting Y = mX + c = -0.14 * (12) + (4.73) = -1.68 + 4.73 =3.05 = 3 calls approx 4.4 Day 14 Forecasting Y = mX + c = -0.14 * (14) + (4.73) = -1.96 + 4.73 =2.77 = 3 calls approx It could be analysed that using the linear forecasting, forecast for calls on 12thand 14th day is 3. CONCLUSION It could be concluded from the above table report that descriptive data analysis plays an important role in analysing the data. Report has increased the practical understanding about the use of different method in statistics. 7
REFERENCES Books and Journals Jiang, W. and Yu, W., 2017. Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome- wide association studies.Bioinformatics.33(4). pp.500-507. Olm, M., and et.al.,2018. Operativetreatmentof diabeticswith vascularcomplications: Secondary data analysis of diagnosis-related groups statistics from 2005 to 2014 in Germany.Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen.89(7). p.545. Krieg, E.J., 2019.Statistics and data analysis for social science. SAGE Publications, 8