Applying Analytic Techniques to Business Josh

BUSINESS ANALYTICS TECHNIQUES 10

Business Analytics Techniques: McDonald

Joshua Clark

FPX5008

Instructor: John Gaze

April 18, 2022

Introduction

Business analytics techniques have attracted the attention of most business organizations due to their influence on organizations' decision-making processes. These skills transform large amounts of raw data into useful and meaningful information. Business leaders use the information to make strategic and operational decisions for organizations based on the analysis of historical data. Techniques such as graphical representation and descriptive statistics translate raw data into valuable information for organizations. In this report, McDonald's is the publicly traded business selected for evaluation and analysis.

Background of McDonald's

McDonald's (MCD) is among the top fast-food restaurants globally. MCD currently operates in over 100 countries and has more than 36000 restaurants. The company has employed over 2 million employees across the globe (Rajawat et al., 2020). It recorded more than 7% in revenue share in the global information eating out from 2018. The company offers a uniform menu that includes products such as fries, Big Mac, hamburgers, chicken sandwiches, shakes, and many more. It also provides locally relevant foods to ensure it has a global market appeal. MCD started as a hamburger stand in California by its founders, Dick and Mac MacDonalds. The two founders signed a franchise agreement with Ray Krok in 1954, enabling the company to go national. This led to the opening of other branches across the United States and continued to grow into a successful global restaurant.

Business Context of McDonald's

MCD has divided its business operations into segments. The main segments are the franchise stores and the company-operated stores operating in the fast foods restaurants industry, NAICS 72221. The industry comprises focused and established restaurants that emphasize the sale of prepared foods and drinks for remediate consumption (Rajawat et al., 2020). The industry is characterized by intense competition, the sale of cheap fast foods, and different regional growth patterns. The fast foods industry has low barriers to entry, making it easier for companies to join and increase the levels of competition. The fast foods industry holds approximately $500 billion in revenues annually and accounts for more than $120 billion in sales.

MCD's mission is to be customers' favorite place and way to eat and drink. The company's focus is to align with the global strategies in operations centering on exceptional customer service and customer experience (Han, 2021). The main competitors that provide increased pressure for MCD include KFC, the second-largest fast-food restaurant globally. Other competitors are Burger King, Subway, Starbucks, and Pizza Hut. However, MCD prides itself on its effective strategy and competitive advantage, dominating the industry and the global market. The robust strategies that the company has utilized to stand out in the market are robust advertising campaigns. It employs rigorous systems of advertising that amount to billions of dollars and reflects positively on the company's sales and revenues. The other effective strategy that has kept MCD ahead of its competitors is the franchise business model. The franchise model helped the company grow into global standards and thrive well in the industry. MCD is among the best-named companies that utilize effective problem-solving approaches globally. The ability to innovate and create solutions to market trends and social responsibilities has increased the company's positive reputation and market demand (Han, 2021). Consistency to quality is key to the company, and this is maintained across all branches globally and has helped the company maintain a great competitive advantage over its peers. These qualities have kept the company among the top-performing in the stocks market.

Graphical Representation

Graph 1: Scatter Plot of Highest Stock Price against Time

Creating the scatter plot for the highest stock price against time involved selecting the date and the High columns from the data created. This step ensured that only the relevant data was selected for the scatter plot. The next step was selecting insert in the excel tools bar, which provides several options for different charts. I selected the scatter plot, and the chart is effectively created on the excel sheet. Several modifications were required to ensure the scatter plot effectively represented the data into meaningful information. These included labeling the axes and choosing an appropriate scale to ensure the graph was visually effective.

Graph 2: Scatter Plot of Lowest Stock Price against Time

I created the scatter plot for the highest stock price against time by selecting the date and the low columns from the data created. This step ensured that only the relevant data was selected for the scatter plot. The next step was selecting insert in the excel tools bar, which provides several options for different charts. I selected the scatter plot, and the chart is effectively created on the excel sheet. Several modifications were required to ensure the scatter plot effectively represented the data into meaningful information. I labeled the axes and chose an appropriate scale to ensure the graph was visually effective.

Graph 3: Histogram of the Adjusted Daily Closing Stock Price

I selected the data from the descriptive table to create the histogram for the adjusted daily closing stock price. I selected the Adjusted stock price labeled Adj Close from the data and then selected the insert option. The insert option provided several options for different graphs. However, since the data was to be represented through a histogram, I selected the histogram option and created the graph on the excel sheet. I adjusted the size of the graph to make it appropriate. I also added color to make it visually appealing and easy to distinguish the data variations. I also adjusted the axes by labeling them to create a clear data presentation.

Graph 4: Histogram of the Stock Trading Volume

I selected the data from the descriptive table to create the histogram for the stock trading volume. I selected the stock trading volume labeled volume from the data and selected the insert option. The insert option provided several options for different graphs. However, since the data was to be represented through a histogram, I selected the histogram option and created the graph on the excel sheet. I adjusted the size of the graph to make it appropriate. I also added color to make it visually appealing and easy to distinguish the data variations. I also adjusted the axes by labeling them to create a clear data presentation.

Descriptive Statistics

Table 1: Descriptive Statistics for McDonald's Stocks

	Open	High	Low	Close	Adj Close	Volume
Mean	243.8705394	245.4819	242.1179	243.7987	241.0742	2738496
Standard Error	0.716714505	0.721423	0.714395	0.717933	0.769054	63503.36
Median	240.710007	242.35	239.35	240.98	238.5531	2529600
Mode	233.300003	235.35	231.94	233.86	228.7596	1989700
Standard Deviation	11.12640118	11.1995	11.09039	11.14533	11.93892	985837.3
Sample Variance	123.7968031	125.4289	122.9967	124.2183	142.5379	9.72E+11
Kurtosis	-0.522769276	-0.65131	-0.41064	-0.56996	-0.71887	1.174973
Skewness	0.640558692	0.61776	0.647743	0.593562	0.578769	1.19857
Range	49.300003	49.09999	50.59	47.69	46.19803	4822300
Minimum	220.199997	222.05	217.68	222	222	1304000
Maximum	269.5	271.15	268.27	269.69	268.198	6126300
Sum	58772.79999	59161.13	58350.41	58755.48	58098.88	6.6E+08
Count	241	241	241	241	241	241

In creating the descriptive statistical data in table 1, I downloaded McDonald's data from Yahoo Finance and created a raw data set in excel. The data selected was huge and difficult to integrate and could not be used for effective decision-making as it did not illustrate specific statistical patterns. To make the raw data useful and meaningful for the report, I selected the data analysis toolpack. I instructed the data analysis tool pack to create a summary statistic fr the raw data I had provided. This resulted in creating a tabled descriptive statistical set, as shown in table 1. All the calculations for mean, mode, median, and other essential statistical parameters were computed through the data analysis tool pack, making it easy to create useful information with minimum strain. It also reduced possible errors involved in extracting data manually from different sources.

Summary of the Data

According to table 1, the mean for the adjusted daily closing stock price is 241.0742, explaining that the average closing stock price for MCD is 241.0742 daily. Investors and business executive leaders can use this information to make decisions on approaches to develop the company's marketing (Mishra et al., 2019). Investors can also use the mean daily stock price to compare viable investment options. The mean for stock volume was 2738496, expalining the average total stocks the MCD makes daily. Computing the average stock and the average closing price provided provides the amount of revenue MCD makes daily from the stock market. The information is vital for investors as they can obtain an image of viability in the investment approaches they intend to make with MCD. The median for the adjusted daily closing stock price is 238.5531 and gives the middle position of the stock price for the data analyzed. The median gives a better description of the stock price value and can be used to determine the most appropriate value. On the other hand, the median for stock volume is 2529600 showing the middle position of the stocks sold by MCD daily. The mode for the adjusted stock price is 228.7596, and that of the stock volume is 1989700. Additionally, the standard deviation for the adjusted stock price is 11.93892, and for stock, volume is 985837.3. This gives the dispersion in the data presented and provides insights into the reliability of the statistical data (Sporns, 2022). Therefore, MCD makes approximately 2738496 daily at an average price of $241.0742. The information can be used to compute numerous decisions in the organization and investors.

References

Han, M. (2021). The Impact of Competitive Strategy on Profitability in the Context of COVID-19: A Case Study of McDonald's. In E3S Web of Conferences (Vol. 235). EDP Sciences. https://search.proquest.com/openview/85e66f04d583d426bdc44c3227242695/1?pq-origsite=gscholar&cbl=2040555

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350423/

Rajawat, A., Kee, D. M. H., Malik, M. Z. B. A., Yassin, M. A. Q. B. M., Shaffie, M. S. I. B. A., Fuaat, M. H. B., ... & Santoso, M. E. J. (2020). Factors: responsible for McDonald's performance. Journal of the Community Development in Asia (JCDA), 3(2), 11-17. http://www.ejournal.aibpm.org/index.php/JCDA/article/view/806

Sporns, O. (2022). Graph theory methods: applications in brain networks. Dialogues in clinical neuroscience. https://www.tandfonline.com/doi/full/10.31887/DCNS.2018.20.2/osporns

Scatter Plot of Highest Stock Price vs Time

High 44305 44306 44307 44308 44309 44312 44313 44314 44315 44316 44319 44320 44321 44322 44323 44326 44327 44328 44329 44330 44333 44334 44335 44336 44337 44340 44341 44342 44343 44344 44348 44349 44350 44351 44354 44355 44356 44357 44358 44361 44362 44363 44364 44365 44368 44369 44370 44371 44372 44375 44376 44377 44378 44379 44383 44384 44385 44386 44389 44390 44391 44392 44393 44396 44397 44398 44399 44400 44403 44404 44405 44406 44407 44410 44411 44412 44413 44414 44417 44418 44419 44420 44421 44424 44425 44426 44427 44428 44431 44432 44433 44434 44435 44438 44439 44440 44441 44442 44446 44447 44448 44449 44452 44453 44454 44455 44456 44459 44460 44461 44462 44463 44466 44467 44468 44469 44470 44473 44474 44475 44476 44477 44480 44481 44482 44483 44484 44487 44488 44489 44490 44491 44494 44495 44496 44497 44498 44501 44502 44503 44504 44505 44508 44509 44510 44511 44512 44515 44516 445 17 44518 44519 44522 44523 44524 44526 44529 44530 44531 44532 44533 44536 44537 44538 44539 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234.320007 234.89999399999999 237.5 237.770004 237.80999800000001 237.279999 236.270004 232.88999899999999 233.229996 234.86000100000001 234.449997 235.16000399999999 233.41000399999999 232.75 231.71000699999999 232.25 232.39999399999999 234.11000100000001 233.89999399999999 234.990005 233.83000200000001 236.229996 236.240005 237.470001 239.050003 237.38999899999999 237.88000500000001 233.320007 235.520004 238.11000100000001 239.69000199999999 245.050003 244.570007 247.050003 243.800003 245.199997 245.41000399999999 244.679993 239.949997 236.60000600000001 236.80999800000001 237.13999899999999 236.070007 235.10000600000001 235.94000199999999 236.759995 239 241.050003 240.83999600000001 239.759995 238.11999499999999 239.38999899999999 240.070007 240.38999899999999 239.60000600000001 238.91999799999999 238.36999499999999 237.759995 237.729996 239.03999300000001 239.949997 239.64999399999999 238.61000100000001 239.46000699999999 241.429993 241.320007 241.800003 242.25 241.270004 243.58999600000001 244.63000500000001 241.96000699999999 242.35000600000001 245.86000100000001 245.949997 247.38000500000001 248.89999399999999 247.71000699999999 245.71000699999999 245.41999799999999 244.69000199999999 244.80999800000001 246.88000500000001 247.16000399999999 249.949997 249.63000500000001 249.13999899999999 248.60000600000001 243.86999499999999 245.41999799999999 246.38000500000001 243.449997 243.929993 242.64999399999999 241.91000399999999 242.240005 239.25 238.39999399999999 244.61999499999999 246.08000200000001 246.08999600000001 251.69000199999999 252.220001 251.13000500000001 254.179993 257.52999899999998 255.64999399999999 253.69000199999999 253.720001 253.33000200000001 251.009995 253.759995 254.19000199999999 253.33999600000001 253.490005 253.320007 255.449997 255.78999300000001 257.790009 253.16000399999999 252.320007 247.89999399999999 250.89999399999999 250.33999600000001 250.679993 256.92999300000002 260.38000499999998 262.80999800000001 263.10000600000001 265.26998900000001 265.85998499999999 263.58999599999999 265.67001299999998 266.89001500000001 264.85000600000001 261.959991 266.48001099999999 265.44000199999999 267.36999500000002 268.26001000000002 269.22000100000002 269.72000100000002 269.2600 1000000002 269.11999500000002 270.73001099999999 271.14999399999999 270.27999899999998 270.76001000000002 270.17001299999998 266.72000100000002 263.95001200000002 263.95001200000002 263.85000600000001 261.23998999999998 257.10000600000001 257.95001200000002 258.10998499999999 258.459991 253.91999799999999 252.529999 254.21000699999999 252.46000699999999 256.35000600000001 259.5 259.89999399999999 262.60998499999999 262.79998799999998 261.73001099999999 261.57998700000002 262.38000499999998 262.88000499999998 260.44000199999999 258.76001000000002 256.25 256.92001299999998 254.60000600000001 252.88000500000001 252.28999300000001 254.699997 253.85000600000001 245.58000200000001 249.529999 245.720001 243.60000600000001 242.740005 242.33999600000001 236.38999899999999 234.30999800000001 229.740005 227.25 222.050003 229.11000100000001 228.44000199999999 234.570007 238.21000699999999 237.470001 239.19000199999999 240.08999600000001 238.44000199999999 238.009995 241.03999300000001 241.85000600000001 242.949997 248.64999399999999 250.5

Time

High Stock Price

Scatter Plot of Lowest Stock Price vs Time

Low 44305 44306 44307 44308 44309 44312 44313 44314 44315 44316 44319 44320 44321 44322 44323 44326 44327 44328 44329 44330 44333 44334 44335 44336 44337 44340 44341 44342 44343 44344 44348 44349 44350 44351 44354 44355 44356 44357 44358 44361 44362 44363 44364 44365 44368 44369 44 370 44371 44372 44375 44376 44377 44378 44379 44383 44384 44385 44386 44389 44390 44391 44392 44393 44396 44397 44398 44399 44400 44403 44404 44405 44406 44407 44410 44411 44412 44413 44414 44417 44418 44419 44420 44421 44424 44425 44426 44427 44428 44431 44432 44433 44434 44435 44438 44439 44440 44441 44442 44446 44447 44448 44449 44452 44453 44454 44455 44456 44459 44460 44461 44462 44463 44466 44467 44468 44469 44470 44473 44474 44475 44476 44477 44480 44481 44482 44483 44484 44487 44488 44489 44490 44491 44494 44495 44496 44497 44498 44501 44502 44503 44504 44505 44508 44509 44510 44511 44512 44515 44516 44517 44518 44519 44522 44523 44524 44526 44529 44530 44531 44532 44533 44536 44537 44538 44539 44540 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231.16000399999999 231.33999600000001 231.449997 231.929993 234.71000699999999 234.80999800000001 235.66000399999999 233.779999 233.279999 228.820007 229.470001 232.429993 232.699997 232.740005 232.33999600000001 230.009995 230.050003 230.60000600000001 230.75 232.44000199999999 231 231.720001 231.94000199999999 233.05999800000001 234.5 235.199997 236.63999899999999 234.91999799999999 234.38000500000001 226.41999799999999 230.21000699999999 235.220001 235.16000399999999 239.60000600000001 241.60000600000001 244.05999800000001 239.35000600000001 242.979996 242.21000699999999 239.69000199999999 235.25 233.050003 234.33999600000001 235.66999799999999 234.029999 233.240005 233.75 235.050003 236.270004 238.55999800000001 238.21000699999999 237.779999 236.070007 236.320007 237.990005 238.320007 237.83999600000001 235.91999799999999 236.36999499999999 235.86000100000001 234.66999799999999 237.21000699999999 238.38000500000001 237.509995 235.949997 236 237.770004 239.03999300000001 239.83999600000001 239.5 238.179993 239.949997 241.88000500000001 236.63999899999999 239.39999399999999 241.470001 243.529999 245.240005 245.279999 242.91000399999999 243.14999399999999 240.820007 240.25 241.86000100000001 243.009995 243.020004 247.679993 247.11000100000001 246.449997 244.179993 239.89999399999999 242.03999300000001 241.990005 241.13000500000001 241.529999 239.28999300000001 239.30999800000001 238 236.5 236.13999899999999 238.85000600000001 242.529999 242.470001 245.33999600000001 248.16000399999999 247.91000399999999 250.199997 254.6 1999499999999 251.929993 250.449997 250.80999800000001 249.259995 248.88999899999999 250.66999799999999 252.38000500000001 250.520004 251.08000200000001 250.229996 252.21000699999999 253.050003 254.050003 248.270004 249.83000200000001 243.949997 244.11000100000001 245 247.570007 252.36999499999999 256.45001200000002 259.08999599999999 258.35998499999999 262.42999300000002 262.39999399999999 261.75 261.41000400000001 264.08999599999999 260.88000499999998 258.42999300000002 263.42001299999998 264.07000699999998 265.45001200000002 266 267.25 267.95001200000002 266.70001200000002 267.209991 265.55999800000001 268.26998900000001 266.89999399999999 267.7999879 9999998 267 262.01001000000002 260.32000699999998 260.27999899999998 260.58999599999999 257.16000400000001 254.30999800000001 255.08000200000001 252.89999399999999 253.69000199999999 247.779999 247.070007 247.86000100000001 245.25 247.16000399999999 254.44000199999999 255.58000200000001 258.48998999999998 260.07000699999998 257.790009 259.17001299999998 259.10000600000001 259.41000400000001 255.970001 254.729996 251.679993 253.55999800000001 251.199997 250 250.009995 249.60000600000001 247.08000200000001 239.61000100000001 243.63000500000001 242.36000100000001 238.300003 239.61000100000001 235.679993 231.63999899999999 224.19000199999999 222.199997 222.0 09995 217.679993 224.199997 223.16000399999999 227.979996 234.36000100000001 234.53999300000001 235.990005 234.070007 236.470001 235.949997 236.470001 239.429993 239.86000100000001 244.770004 247.509995

Time

High Stock Price