Statistics for management

Statistics for management

Executive summary

This report is based on the selection of two of the data sources from the given list of sources for analysing the market share with respect to mobile phones. This also includes the analysis of market share of various companies and the position held by them along with rise or fall from the prior performance. This report also discusses the units which are being shipped by the various companies and their comparison. In the later of part 1 the comparison of data of two chosen sources have also been made. The source chosen ate the Canalys and counterpoint.

Introduction-

This report is prepared for conducting the analysis on the market survey which relates to the share in the market with respect to various mobile phones in the industry of telecom. This analysis includes the segregation with respect to the market share of android and the market share for iPhone from various available and reliable sources. This report will provide the understanding of various tools of statistics and the graphical representation of data. This data being calculated using the statistical tools is been critically analysed so that an informed decision can be made on various issues relating to the Widjitz.

Part A-

Sources used for data collection-

Canalys newsroom- this is one of the source which is well known for the data analysis and provide reliable data based on various industries and various different issues. This website provides the data at global level for global issues and makes its interpretation and analysis at global level only (S.N 2017). I this case the data has been taken for the mobile companies and their share in the market across the world. This website is well known and popular among the companies and various other users for making analysis as per their needs.

CounterPoint- this source is also very much popular for its data been provided. This site provides the data for the various topics, companies, issues and their related data. This source has been used for below analysis in respect to mobile companies. This website has provided with the reliable data for various companies and providers of mobile phones in different brand name (Eom, et al 2020).

Evaluating and comparing the various given sources of data-

The jump in the supply of the mobile phones can been in mid of 2020 and Apple was the only company which was growing in this critical condition of market. The mobiles (iPhone) shipped by Apple were 45.1 million across the globe. The shipped phones had an increases of 25 % for Apple than previous year. The fall in the market share of smartphones can be seen by approx. units of 286 million which is a free fall for consecutive two quarters (Miller 2019). Due to covid-19 the fall in the growth of industry can be seen. As per the ranking for various companies in the second quarter Huawei was the one for the first time fallen above Samsung in terms of share in market. The shipped phones of Huawei are 55.8 million with Samsung having 53.7 million. The Xiaomi held the fourth place with units being shipped to be 28.8 million which is decrease of 10% from previous year. The next position was held by OPPO with fall of 16 % and the units being shipped to be 25.8 million (Magnusson, Westjohn 2019).

As per the data of counterpoint fall in mobile market of 24% on YoY basis in the mid of 2020. The decline of 271.4 million units was seen. Huawei has surpassed Samsung and is holding the top position in the smartphones market worldwide (S.N 2017). The growth of Huawei of 11 % in share of market but its shipment fall by 3% globally. Decline in the market share of Samsung was 29% in the tough situation of market. Realme has done well from its prior year with a growth of YoY 11 %. This brand has increased unexpectedly in the rise in sales holding 7^Th place. The rise in market share is 3 % and in its revenue being 2% due to its innovative launches and is expected to grow more. Fall in share of Xiaomi has been 18 % for quarter and is doing well in its major markets. OPPO has a low share of 20% but is substantially goes in the markets of European countries (Eom, et al 2020).

Hence as per the share of market held by various companies according to their operating system the prominence is held by Android with maximum share of 74.6 %. The combine share of google Android and the iOS contains the share in the market total of 99% worldwide. Because of upgradation of software and technology advancement their demand has been very differing (Miller 2019).

Note: *Ranking is according to the latest quarter.

Inferences that have been drawn from chosen sources of data-

Canalys newsroom

counterpoint

Huawei has shipped the total units for 2020 for around 55.8 million (S.N 2017) The units being shipped by Samsung have been 53.7 million. The units shipped by Apple company for their iPhone were 45.1 million across the world. This indicated the growth by around 25 % from that of previous year (Miller 2019). The fourth position holder Xiaomi had shipped around 28.8 million units which indicates the decreases of 10 % from prior year. The subsequent position holder is OPPO with fall of 16 % having the units shipped of 25.8 million (Aldashova, et al 2020).

Huawei has indicate a satisfactory growth in its share of market with YoY 11% and fall in shipments globally of 3% The decline of YoY 29% can be seen for Samsung in across the global during the hit of pandemic The growth of YoY 3 % for apple and its revenue with 2 % high can be seen with further expectation to rise due to its new launches (Eom, et al 2020). The share of market for Xiaomi have fallen YoY 18% in last quarter but holds significant position in their major markets (Magnusson, Westjohn 2019). OPPO have decline in its market share of 20% bit having good position in markets of Europe.

Conclusion-

This report is based on the statistical tools which are used by the managers of business for making decision for the future plans. This data being calculated using the statistical tools is been critically analysed so that an informed decision can be made on various issues relating to the Widjitz. This data being calculated using the statistical tools is been critically analysed so that an informed decision can be made on various issues relating to the Widjitz.

Part B-

Question 1-

Calculation of the average sales per Salesperson and standard deviation for August 2020 and September 2020-

b) Graph for comparison of sales graph for each sales person for the month of August and September 2020-

c) Reasons for supporting the choice of graph chosen-

This graph has been chosen for as it makes in more clear to understand and interpret the sales that is made by the each sales person in Widjitz. This graph indicates the sales of all 15 sales person on an individual basis for the month of August and September for financial year 2020 (Eom, et al 2020). This graph indicates the maximum sales made by Sales person with ID 8 for both months followed by sales person with IOD 4. Similarly lowest sales have been made by 12 in August and sales person with ID 11 in September. This evaluation and understanding of graph will also help in making a sensible decision with respect to sales of Widjitz for the future to come and also in evaluation of performance of each sales person (Beckerman, 2014). The graph in form of bar graph can be easily understood by people at every level with an organisation for making evaluation (Miller 2019).

Question 2-

1. Graph for sales of all months-

2. Forecast for months of October to december2020-

3. Evaluation of forecast and the reasons for selection of graph-

The above data indicates that the monthly sales for the accessory in relation to mobile phones. These sales are from the month of January to the month of September. In the above graph, bar graph is chosen so that the sales or the given data. This graph indicates the variation in the sales for all the given months with ups and down in the sales (Martínez-Camblor, et al 2014). The trend line has also been drawn of linear function for forecasting the probable sales for the months of October, November and December (Aldashova, et al 2020). The above data indicates the maximum sales in the month of September followed by august. The bar graph used for graphical representation of data is easily comparable with that of the other months and will enable the concerned person using this statistical tool for making decision suitable for the future actions (Magnusson, Westjohn 2019).

Question 3-

1. Graph for the Correlation of Widjitz Sales vs Android Sales is as below-

2. Computation of correlation of Widjitz and android sales-

3. Calculation of how closely are the correlated as per the data in excel-

Hence correlation = 0.76 (calculated in above table)

4. Critical evaluation of the graphical data in respect to correlation between them-

As per the above given data the information is provided with respect to the table indicating the sales for the Android mobile phones in UK in terms of pound in million and the sales for Widjitz Accessories in UK in terms of thousand figures in pounds. These sales have been given from the month of July 2019 to that of September 2020 on a monthly basis. Scatter diagram has been opted based on sales for graphical representation and the linear trend line has been drawn afterwards based on the scatter points for estimating the movement in the sales of the goods (Miller 2019).

Correlation coefficient is the statistical tool for measuring the relationship between the two data or the variables. The relation determined through this tool defines the strength in the relationship. When the coefficient correlation is the value which lies between the numerical value of -1 and the value of +1 (Aldashova, et al 2020). The -1 is the indicator for the strong relationship of negative nature whereas the +1 is the indicator for the strong relationship of positive nature. Here the value calculated considering the two variables of Android and the accessories of Widjitz is been 0.76 which indicates the value closer to that of +1. This means that the relation is positively correlated but is not significant (Magnusson, Westjohn 2019). The value above the value of 0.8 is considered to be significant and a strong relationship among the variables chosen. Hence in spite of being positively correlated the relation between both the variables is not strong (Ali, Bhaskar 2016).

Question 4-

Given that –

1. Break-Even analysis-

2. Chart for cost and revenue at 1000 units-

total cost	total revenue
£12,000	15000

Question 5-

1. Calculation of t-shirts-

2. The arrangement for the sizes of T-shirts as per the mean and the standard deviation has been given where graph indicates the distribution for same sixe t-shirts to be 500 medium size to be 333 and large size to be 167 in number (Luo, et al 2018). The distribution shall be done as per the given mean and the standard deviation (Magnusson, Westjohn 2019). The manager shall look for values after seeing them for the total t-shirts to be made.

References-

• Aldashova, G., Zhakupova, B., Spankulova, L., Orazgaliyeva, A., Balginova, K., Mussirov, G., Kydyrova, Z. & Onlasynov, E. 2020, "THE ROLE OF MANAGEMENT STRATEGIES FOR START-UP GROWTH: A CASE STUDY OF XIAOMI TECHNOLOGY COMPANY", Academy of Entrepreneurship Journal, vol. 26, no. 1, pp. 1-10.

• Ali, Z. & Bhaskar, S. 2016, "Basic statistical tools in research and data analysis", Indian Journal of Anesthesia, vol. 60, no. 9.

• Beckerman, A.P. 2014, "What can modern statistical tools do for limnology?", Journal of Limnology, vol. 73.

• Eom, J.K., Lee, K., Ji, Y.S. & Lee, J. 2020, "Analysis of Mobile Phone Data to Compare Mobility Flows and Hotspots Before and After the Opening of High-Speed Railway: Case Study of Honam KTX in Korea", Applied Sciences, vol. 10, no. 14, pp. 5009.

• Luo, B., ? Alanna, K.E., Tolg, C., Turley, E.A., Dean, C.B., Hill, K.A. & Kulperger, R.J. 2018, "Spatial statistical tools for genome-wide mutation cluster detection under a microarray probe sampling system", PLoS One, vol. 13, no. 9.

• Magnusson, P. & Westjohn, S.A. 2019, "Advancing global consumer culture research", International Marketing Review, vol. 36, no. 4, pp. 593-597.

• Martínez-Camblor, P., Carleos, C., Baro, J.Á. & Cañón, J. 2014, "Standard statistical tools for the breed allocation problem", Journal of Applied Statistics, vol. 41, no. 8, pp. 1848.

• Miller, D. 2019, "Anthropological Studies of Mobile Phones", Technology and Culture, vol. 60, no. 4, pp. 1093-1097.

• S.N. 2017, "The Role Of Country Of Origin In Mobile Phone Choice Of Generation Y And Z", Journal of Management and Training for Industries, vol. 4, no. 2, pp. 16-29.