Name
Capella University
MBA-FPX 5008 Applied Business Analytics
Prof. Name
Date
Starbucks Corporation’s historical stock performance can be analyzed using descriptive statistics, histograms, scatterplots, and market research to identify price trends, trading activity, and potential business risks. Examining historical data from September 17, 2018, through September 13, 2019, provides insight into the company’s adjusted closing prices, daily trading volume, and stock price fluctuations. The reported results indicate a generally upward stock price trend during this period, although changes in trading activity and broader economic and industry conditions introduced uncertainty. These analytical techniques demonstrate how businesses can use data to evaluate performance, understand market behavior, and make more informed strategic decisions.
Starbucks is a global specialty coffee company recognized for its beverages, customer experience, and established brand identity. Its business model combines company-operated stores, licensed locations, and international partnerships. This structure allows Starbucks to maintain control over many aspects of its customer experience while expanding into additional markets through different operating arrangements.
During the historical period examined, Starbucks pursued international expansion and product diversification. Its offerings included specialty beverages, packaged coffee, tea, and other products designed to reach different customer segments. These initiatives created opportunities to increase customer engagement, strengthen brand loyalty, and diversify revenue sources.
Industry research has highlighted the importance of specialty coffee and changing consumer preferences in the beverage market. Brown (2019) discussed coffee and beverage consumption trends, while Menke (2018) examined developments in the specialty coffee industry. These trends provided opportunities for Starbucks to differentiate its products, introduce new offerings, and strengthen its competitive position.
International expansion also played an important role in Starbucks’ business strategy. The United States remained a major market, while China offered opportunities for further growth. However, operating internationally exposed the company to additional risks, including local competition, changing consumer preferences, economic uncertainty, and trade tensions.
Analyzing Starbucks’ historical stock performance alongside these business factors provides a more comprehensive understanding of its market position. Stock prices reflect investor expectations, but they should be evaluated alongside operational performance, revenue growth, profitability, and broader industry conditions.
Graphical analysis makes large datasets easier to interpret by presenting distributions, trends, and relationships between variables. Histograms show how frequently observations occur within specific ranges, while scatterplots help identify relationships between time and stock prices.
This analysis examines historical Starbucks stock data from September 17, 2018, through September 13, 2019. Because the dataset covers a specific historical period, the findings describe past market behavior rather than Starbucks’ current stock performance or future investment potential.
Figure 1. Histogram of Starbucks Daily Trading Volume (Yahoo Finance, 2019)
The histogram of daily trading volume illustrates the distribution of Starbucks shares traded during the selected period. The horizontal axis represents trading-volume ranges, while the vertical axis shows the number of observations within each range.
The reported distribution indicates that most observations fell within lower-to-middle trading-volume ranges, approximately 7.1 million to 14.3 million shares. Fewer observations occurred at higher trading volumes, suggesting a distribution with a longer right tail.
The mean daily trading volume was approximately 10,030,024 shares, compared with a median of 9,223,100 shares. Because the mean exceeded the median, unusually high-volume observations may have increased the average.
These higher-volume days could have coincided with earnings announcements, company news, changes in investor expectations, or broader market developments. However, identifying the actual causes requires comparing trading dates with relevant news and market information.
Trading volume measures the number of shares exchanged, not whether investors are collectively optimistic or pessimistic. High volume may accompany either rising or falling prices. Therefore, trading volume should be analyzed alongside price movements and other relevant information.
The histogram provides a useful overview of trading activity and highlights observations that may warrant additional investigation. It does not independently establish the direction of investor sentiment or predict future stock performance.
Figure 2. Histogram of Starbucks Adjusted Closing Stock Prices (Yahoo Finance, 2019)
The histogram of adjusted closing prices illustrates the distribution of Starbucks’ daily adjusted stock prices during the selected period. The horizontal axis represents price ranges, while the vertical axis indicates the frequency of observations within those ranges.
The reported mean adjusted closing price was $73.72, while the median was $70.88. The difference suggests that relatively high price observations may have pulled the mean upward. This pattern is consistent with a potentially right-skewed distribution, although the actual histogram and complete dataset are needed to confirm its precise shape.
The mean represents the arithmetic average of the observed prices, whereas the median identifies the middle value when observations are arranged in ascending order. Comparing these measures helps analysts understand how the distribution of prices may affect the average.
Adjusted closing prices are useful in historical stock analysis because they account for certain corporate actions, such as dividends and stock splits, according to the data provider’s methodology. This adjustment can make comparisons across time more meaningful than comparisons based solely on unadjusted closing prices.
However, a histogram summarizes the distribution of prices rather than their chronological movement. To determine whether Starbucks’ stock price increased or decreased over the observation period, analysts must also examine time-based graphs and other performance measures.
Scatterplots help identify relationships between two variables. In stock market analysis, time can be represented on the horizontal axis and stock price on the vertical axis. This arrangement allows analysts to observe general trends, fluctuations, and potentially unusual observations.
Figure 3. Scatterplot of Starbucks’ Highest Daily Stock Prices (Yahoo Finance, 2019)
The scatterplot of Starbucks’ highest daily stock prices shows the relationship between time and the highest price recorded during each trading day from September 17, 2018, through September 13, 2019.
The reported pattern indicates a generally upward trend, suggesting that Starbucks’ daily high prices increased over the observation period. Historical market commentary also described a rally in Starbucks shares during 2019 (Whitfield, 2019).
Although the scatterplot illustrates this upward pattern, it does not establish why the increase occurred. Potential influences include revenue growth, consumer demand, product innovation, store expansion, earnings announcements, and changes in investor expectations.
A positive overall trend does not mean the stock price increased every day. Short-term declines and fluctuations can occur within a longer-term upward movement. Similarly, a decline near the end of the observation period would not necessarily indicate a sustained reversal.
Analysts should therefore interpret the scatterplot as evidence of a historical pattern rather than proof of a specific cause or a guarantee of future growth.
Figure 4. Scatterplot of Starbucks’ Lowest Daily Stock Prices (Yahoo Finance, 2019)
The scatterplot of Starbucks’ lowest daily stock prices presents the relationship between time and the lowest price recorded during each trading day over the same period.
The reported observations also show a generally upward pattern. This suggests that the increase in Starbucks’ historical price range was not limited to its daily highs; the lower end of its daily price range also generally moved upward.
Examining daily highs and lows together can help analysts understand how a stock’s trading range changes over time. However, these measures represent intraday price extremes rather than the final trading price. Adjusted closing prices and daily returns are more appropriate for many historical performance comparisons.
The two scatterplots demonstrate the value of examining multiple measures rather than relying on a single price indicator. When interpreted alongside trading volume, descriptive statistics, and business developments, graphical analysis provides a stronger foundation for evaluating historical stock behavior.
Descriptive statistics summarize datasets using measures such as the mean, median, and standard deviation. These measures help analysts understand typical values, the distribution of observations, and the degree of variation within a dataset.
For Starbucks, descriptive statistics provide a numerical summary of historical stock prices and trading activity during the selected period. However, these measures should be interpreted alongside graphical analysis and relevant business information.
The reported mean adjusted closing price for Starbucks was $73.72 from September 17, 2018, through September 13, 2019. The mean is calculated by adding all adjusted closing prices and dividing the total by the number of observations.
The median adjusted closing price was $70.88. Because the median is less sensitive to unusually high or low observations, it provides a useful comparison with the mean.
The difference between the two measures suggests that higher price observations may have influenced the average. However, the mean and median alone cannot establish the precise shape of the distribution.
The reported standard deviation was 12.5. Standard deviation measures how widely observations are dispersed around their mean. A larger standard deviation indicates greater dispersion, while a smaller value indicates that observations tend to cluster more closely around the average.
The units and calculation method for this reported standard deviation should be verified against the original dataset. If it represents the standard deviation of adjusted price levels, it measures variation in prices rather than the volatility of investment returns.
Stock price variation is important because investors and business analysts need to understand how market values change over time. However, standard deviation of price levels should not be confused with standard deviation of daily returns.
Daily returns measure the percentage change in price from one trading session to the next. Analysts commonly use return volatility to evaluate how widely investment returns fluctuate.
A more comprehensive analysis of Starbucks would examine daily returns, compare performance with an appropriate market benchmark, and consider company fundamentals such as revenue growth, operating margins, cash flow, and valuation.
The reported descriptive statistics provide a useful historical summary, but they are not sufficient to determine whether Starbucks was an attractive investment or whether its stock would continue to appreciate.
Starbucks’ reported mean daily trading volume was 10,030,024 shares, while its median was 9,223,100 shares. The higher mean suggests that some unusually active trading days increased the average volume.
The reported standard deviation of daily trading volume was 4,876,760 shares. This indicates variation in the number of shares traded across the observation period, although the exact calculation should be verified against the underlying data.
Trading-volume changes can be associated with company announcements, earnings releases, shifts in investor expectations, economic developments, and broader market volatility. Nevertheless, high trading volume does not automatically indicate strong buying pressure or a positive market outlook. Every completed trade involves both a buyer and a seller.
For Starbucks, the difference between mean and median trading volume suggests that a smaller number of higher-volume observations may have influenced the overall average. Examining the dates associated with these observations could help analysts identify whether company-specific announcements or broader market events coincided with unusual trading activity.
These findings illustrate how descriptive statistics can highlight patterns that deserve further investigation. They also demonstrate why numerical summaries should be interpreted in context rather than treated as complete explanations of market behavior.
The combination of histograms, scatterplots, and descriptive statistics provides several insights into Starbucks’ historical market performance. The scatterplots indicate a generally upward pattern in daily high and low prices, while the histograms summarize the distributions of adjusted closing prices and trading volume.
The reported means and medians suggest that relatively high observations may have influenced average prices and trading activity. These findings can help analysts identify areas for additional investigation, but historical stock data alone cannot establish whether a particular business strategy caused the observed price movements.
A comprehensive evaluation should consider company fundamentals, competitive conditions, consumer demand, operating costs, and international expansion. This broader approach helps distinguish between statistical patterns and evidence-supported explanations.
International expansion has been an important component of Starbucks’ business strategy. Established markets provide opportunities to strengthen customer loyalty and improve store productivity, while developing markets may offer additional opportunities to expand the customer base.
China was particularly important to Starbucks’ international growth strategy during the historical period examined. However, entering and expanding in international markets also introduces challenges related to local competition, consumer preferences, regulations, economic conditions, and cultural differences.
A successful expansion strategy requires Starbucks to maintain a recognizable global brand while adapting its products and customer experience to local markets.
Analysts evaluating international expansion should consider indicators such as store growth, comparable-store sales, operating margins, customer demand, and the costs associated with opening and maintaining new locations. These measures provide more direct evidence of business performance than stock prices alone.
Trade tensions between the United States and China were identified in contemporary reporting as a potential concern for Starbucks’ operations in China (Moon, 2018). Political and economic tensions can create uncertainty for international businesses, influence consumer sentiment, and affect expectations about future growth.
However, a change in stock price cannot be attributed to a single event without additional evidence. Analysts would need to compare the timing of relevant announcements with stock returns while accounting for other company-specific and market-wide developments.
Stock prices reflect a combination of factors, including financial results, investor expectations, economic conditions, interest rates, and competitive developments. Consequently, a sound business analysis should distinguish between a possible explanation and a demonstrated causal relationship.
Starbucks depends on a reliable supply of coffee beans and other ingredients to maintain product quality and meet customer demand. Weather disruptions, climate change, crop diseases, transportation problems, and commodity price fluctuations can affect supply availability and operating costs.
These risks may influence profit margins and create uncertainty about future performance. Starbucks can manage some of these challenges through supplier relationships, responsible sourcing practices, supply-chain monitoring, and appropriate purchasing arrangements. Nevertheless, these measures cannot eliminate all environmental and market risks.
For business analysts, the key question is how supply-chain developments affect expenses, product availability, operating margins, and customer satisfaction. Combining operational indicators with financial and market data can provide a more complete understanding of the company’s performance.
MBA FPX 5008 Assessment 3: Applying Analytic Techniques to Business
Analytical techniques help organizations transform raw data into useful information for planning and decision-making. In Starbucks’ case, historical stock prices and trading volumes provide insight into market behavior and investor activity. Similar methods can be applied to sales performance, customer preferences, product demand, marketing effectiveness, and store operations.
Adjusted closing prices are useful for historical analysis because they account for certain corporate actions according to the data provider’s methodology. However, analysts should verify the dataset, its adjustment method, and the calculation procedures before drawing conclusions.
Starbucks has historically paid dividends to shareholders. Dividends represent distributions of company value to shareholders and should not automatically be described as evidence that a company is losing value. Stock prices may adjust around ex-dividend dates, while adjusted-price series incorporate dividends according to the provider’s methodology.
Starbucks can apply data analytics to evaluate customer preferences, forecast demand, identify suitable store locations, optimize marketing campaigns, and assess product performance.
For example, combining sales data with customer demographics and location information may help management identify markets where a new store could perform well. Product-level data can reveal seasonal demand patterns and help decision-makers determine which offerings deserve additional investment.
Historical reporting from 2019 described revenue growth at Starbucks during that period (Shama, 2019). Nevertheless, revenue growth and stock price appreciation represent different measures. Revenue measures the sales generated by the business, while stock prices reflect investor expectations and other factors affecting demand for the company’s shares.
A complete business assessment should therefore consider revenue, profitability, operating expenses, cash flow, valuation, and long-term investment requirements alongside stock performance.
Effective business analytics also requires reliable data, appropriate statistical methods, and careful interpretation. Analysts should validate source data, investigate unusual observations, consider alternative explanations, and communicate limitations clearly. These practices reduce the risk of misleading conclusions and support more informed strategic decisions.
The historical analysis of Starbucks Corporation demonstrates how histograms, scatterplots, and descriptive statistics can be used to examine stock prices and trading volume. From September 17, 2018, through September 13, 2019, the reported data showed a generally upward stock price trend, while trading-volume statistics indicated variation and the presence of higher-volume observations.
The reported mean adjusted closing price of $73.72 exceeded the median of $70.88, suggesting that relatively high observations may have influenced the average. Similarly, the mean daily trading volume exceeded the median, indicating that unusually active trading days may have contributed to the distribution. These results provide a historical summary but do not independently predict future returns or establish the causes of stock price movements.
For MBA FPX 5008 Assessment 3, the main takeaway is that analytical techniques are most useful when combined with business context. Starbucks’ international expansion, customer demand, competitive position, supply-chain exposure, and operating performance should be considered alongside historical stock market data.
By validating data, selecting appropriate statistical measures, and avoiding unsupported causal claims, business leaders can use analytical techniques to support decisions involving strategy, marketing, investment, and long-term growth.
Histograms, scatterplots, mean, median, and standard deviation can help summarize historical stock prices and trading volume. Analysts can also examine daily returns, moving averages, and market benchmarks to develop a more comprehensive understanding of stock performance.
The mean adjusted closing price represents the average adjusted price across the observations in the dataset. In this analysis, the reported mean was $73.72. It summarizes the historical price level but does not independently indicate whether the stock is undervalued or likely to increase.
The reported mean daily trading volume was approximately 10.03 million shares, compared with a median of 9.22 million shares. This difference suggests that some higher-volume trading days increased the average. Additional research would be necessary to determine which events contributed to those observations.
A right-skewed histogram has a longer tail toward higher values. When the mean exceeds the median, relatively high observations may be pulling the average upward. However, the complete dataset and histogram should be examined to confirm the distribution’s shape.
No. An upward historical trend describes past performance and does not guarantee future returns. Future stock prices may be influenced by financial results, investor expectations, competition, economic conditions, and unexpected developments.
Starbucks can use data analytics to understand customer preferences, forecast demand, assess product performance, optimize marketing campaigns, evaluate store locations, and monitor operational efficiency. Combining these insights with financial and market data can improve planning and resource allocation.
Adjusted closing prices account for certain corporate actions, such as dividends and stock splits, according to the data provider’s methodology. This can make historical comparisons more meaningful than relying on unadjusted closing prices alone.
Stock market data reflects investor expectations and market activity, but it does not explain every factor affecting a company’s performance. Historical prices cannot independently establish the causes of stock movements or predict future returns. Analysts should combine market data with financial statements, operational measures, industry research, and relevant economic information.
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Cuofano, G. (2019). Starbucks business model in a nutshell. FourWeekMBA. https://fourweekmba.com/starbucks-business-model/
Eckstein, J. (2019, September 24). How Starbucks makes money. Investopedia. https://www.investopedia.com/articles/markets/021316/how-starbucks-makes-moneysbux.asp
Ganti, A. (2019, April 23). Stock dividend. Investopedia. https://www.investopedia.com/terms/s/stockdividend.asp
Menke, J. (2018). The state of the specialty coffee industry. Specialty Coffee Association. https://sca.coffee/sca-news/an-insider-look-at-the-state-of-the-specialty-coffee-industry-in-2018
Moon, L. (2018, July 26). Trade war threatens Starbucks’ success in China. Business Insider. https://www.businessinsider.com/trade-war-threatens-starbucks-success-in-china-2018-7
Shama, G. (2019, April 25). Starbucks’ Q2 revenue growth tops expectations. MarketWatch. https://www.marketwatch.com/story/starbucks-q2-revenue-growth-tops-expectations-2019-04-25
Yahoo Finance. (2019). Starbucks Corporation (SBUX) historical stock data. https://finance.yahoo.com/quote/SBUX/history/
Whitfield, G. (2019, January 10). Starbucks stock rallies for 2019. Zacks. https://www.zacks.com/stock/news/352051/starbucks-stock-rallies-for-2019
