MBA FPX 5008 Assessment 3 Applying Analytic Techniques to Business
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Assessment Overview:
MBA FPX 5008 Assessment 3: applies introductory descriptive and visual analytics to real- world business data( Starbucks). You epitomize company background, produce graphs( histograms, scatterplots), cipher descriptive statistics( mean, standard, SD), interpret distributions and connections, and draw business- inclined conclusions about trends and pitfalls. The thing is to show you can transfigure raw time- series request data into clear perceptivity that supports decision- timber.
How to Pass MBA FPX 5008 Assessment 3 Applying Analytic Techniques to Business
To excel in this third assessment, which focuses on applying analytic techniques to a specific business case (Starbucks), follow these 10 points:
- Correct the Terminology: Your source text has several “scan errors” likely from OCR. Ensure you change “vended” to “sold,” “disposed-right” to “skewed-right,” and “standard” to “median” when discussing the middle value of the data.
- Explain the Skewness: When you notice the Mean ($73.72) is higher than the Median ($70.88), explicitly state that the data is “positively skewed.” This tells the professor you understand how high-value outliers pull the average up.
- Contextualize Volatility: Use the Standard Deviation (12.5) to explain risk. A higher standard deviation in trading volume compared to price suggests that while the price is steadily rising, the interest or activity in the stock varies wildly day-to-day.
- Identify the Peak: The data mentions a peak in July 2019. In your analysis, try to link this to a business event (like an earnings report or an expansion announcement) to show “business-inclined” thinking.
- Label Your Figures Correctly: Ensure every histogram and scatterplot has a clear Title, X-axis label (Time or Price Range), and Y-axis label (Frequency or Price).
- Analyze the “60/40” Shift: The text mentions a shift toward gourmet coffee. Use this as a qualitative backdrop to explain why the stock price shows a positive upward trend in the scatterplots.
- Connect Volume to Price: In your interpretation, discuss if high-volume days (Figure 1) correspond with the highest stock prices (Figure 3). This shows you are looking for correlations between different data sets.
- Address International Risk: Don’t just look at the numbers. Mention how the U.S.-China trade war mentioned in the text might be reflected in the “small dip at the end of the data set.”
- Differentiate Your Charts: Be clear that the Histogram shows the shape of the data (how many times a price occurred), while the Scatterplot shows the direction of the data (is it going up or down over time?).
Sample Assessment:
Starbucks Corporation
Starbucks Corporation is honored encyclopedically as the leading specialty coffee company, with nearly 30,000 locales worldwide. Its success is apparent through strong fiscal earnings and harmonious growth, reaching a record net profit of$ 24.7 billion in 2018. The U.S. coffee request has witnessed a significant shift towards epicure coffee, which for the first time achieved a 60/40 advantage over traditional non-gourmet coffee( Brown, 2019). With the U.S. coffee request valued at$ 48 billion, the specialty sector contributes to 55 of the value. This sector is projected to grow by 5.5 annually over the coming five times, potentially reaching$ 75 billion in profit( Menke, 2018).
Starbucks’ success extends beyond coffee, with strategic accessions similar as Teavana, Bay Breads, and Evolution Fresh Authorities, furnishing new growth openings. Starbucks operates company- possessed stores and common gambles internationally, rather than following traditional ballot models. In 2017, 79% of its profit came from company- operated stores, while certified stores, however lesser in number, only accounted for 11% of total profit( Cuofano, 2019).
Expansion into developed and arising requests has been necessary in Starbucks’ growth, now operating in 78 countries. The United States and China are the largest requests, with growth rates of 7 and 6 in 2018, independently( Wiener- Bronner, 2019). Starbucks plans to continue its transnational expansion by opening a fresh 500 stores in China by the end of 2019.
Applying Analytic Techniques
Graphical Representation of Data
Interpreting the Histograms
The histograms presented below give insight into the volume and stock prices of Starbucks.
| Range of Trading Volume | Frequency of Occurrences |
| 3,500,000 – 7,100,000 | 10 |
| 7,100,000 – 10,700,000 | 20 |
| 10,700,000 – 14,300,000 | 30 |
| 14,300,000 – 17,900,000 | 50 |
Figure 1 illustrates Starbucks’ stock volume being bought or vended daily from September 17, 2018, to September 13, 2019. The x-axis shows the stock volume, while the y- axis represents the number of circumstances within each volume range. utmost data points fall between and, with a disposed-right distribution due to high- volume outliers, complicating prognostications of diurnal trading volumes.
Figure 2 shows Starbucks’ acclimated daily closing stock prices over the same period. The x-axis represents the range of acclimated ending prices, while the y- axis shows the frequency of prices within these ranges. Analogous to Figure 1, the distribution is slightly disposed to the right due to high- value ending prices, pushing the mean above the standard.
Interpreting the Scatterplots
Two scatterplots present trends in Starbucks’ stock prices.
| Date | Highest Stock Price |
| 9/17/2018 | 50 |
| 10/2/2018 | 60 |
| 11/21/2018 | 70 |
Figure 3 shows a positive relationship between time and Starbucks’ loftiest stock prices from September 17, 2018, to September 13, 2019. A steady upward trend can be observed, with a peak in July 2019.
| Date | Lowest Stock Price |
| 9/17/2018 | 40 |
| 10/2/2018 | 50 |
Figure 4 glasses the former graph, showing the smallest stock prices for Starbucks over the same period. Analogous trends are observed, with both high and low prices steadily adding until a small dip at the end of the data set.
Descriptive Statistics
| Statistic | Adjusted Closing Stock Price |
| Mean | 73.72 |
| Median | 70.88 |
| Standard Deviation | 12.5 |
The mean of Starbucks’ acclimated closing stock prices from September 17, 2018, to September 13, 2019, was$ 73.72, with a standard of$ 70.88, indicating the presence of high- value outliers. The standard divagation of 12.5 suggests some position of volatility in the stock price but also shows that utmost prices remain close to the normal.
MBA FPX 5008 Assessment 3 Applying Analytic Techniques to Business
| Statistic | Daily Traded Stock Volume |
| Mean | 10,030,024 |
| Median | 9,223,100 |
| Standard Deviation | 4,876,760 |
For diurnal traded stock volume, Starbucks had a normal of shares traded, with a standard of. The large standard divagation indicates significant oscillations in trading volume, contributing to the stock’s volatility.
Conclusion
The graphical and statistical analyses indicate that Starbucks has a steady upward trend in stock prices, supported by its strong request presence and strategic growth enterprise. Expanding operations into established and arising requests, particularly in China, will probably sustain its fiscal success. Still, external factors similar to the U.S.- China trade war and climate- related challenges to coffee products may increase stock volatility. Starbucks’ capability to navigate these pitfalls while continuing to introduce and expand will be critical to maintaining investor confidence and icing long- term growth( Ekstein, 2019; Ganti, 2019).
References (APA 7 Format)
- George, M. L. (2002). Lean Six Sigma: Combining Six Sigma quality with lean production speed. McGraw-Hill.
- Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.
- Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media.
- Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data mining for business analytics: Concepts, techniques, and applications. Wiley.
- Tableau. (n.d.). Tableau software overview. https://www.tableau.com
- Power BI. (n.d.). Business analytics solutions. https://powerbi.microsoft.com
- Harvard Business Review. (2021). A refresher on regression analysis. https://hbr.org
Rubric Breakdown
| Criteria | Proficient (Pass) | Distinguished (High Pass) |
| Advanced Data Interpretation | Explains the relationship between variables (e.g., time vs. stock price) using scatterplots. | Analyzes the impact of outliers on the mean and explains the “right-skewed” nature of the data. |
| Statistical Analysis | Accurately reports Mean, Median, and Standard Deviation for price and volume. | Compares the variability (Standard Deviation) of trading volume against price to assess relative volatility. |
| Visual Communication | Uses histograms and scatterplots to illustrate data frequency and trends over a one-year period. | Evaluates the effectiveness of visual tools in predicting future stock behavior or identifying peaks/dips. |
| Business Synthesis | Connects statistical findings to Starbucks’ global expansion and market risks (e.g., China growth). | Provides strategic recommendations based on the intersection of quantitative data and macro-environmental factors. |
| Professional Presentation | Follows academic standards with clear labels for figures and appropriate APA citations. | Writing is polished, correcting all technical terminology and “scan” errors for a corporate-ready report. |
Step-by-Step Guide
- Define compass & data — state timeframe( e.g., 9/17/2018 – 9/13/2019), variables( acclimated near, high, low, volume), and data source.
- Clean the data — remove missing/ indistinguishable rows, convert dates, and sort chronologically.
- Produce illustrations — make histograms for volume and acclimated near; scatterplots time series for high/ low/ close. Marker axes and add brief captions.
- Cipher descriptives — calculate mean, standard, standard divagation( and voluntarily min/ maximum, IQR). Present in a small table.
- Interpret results — note skewness, outliers, volatility, and temporal trends( e.g., upward trend, peak months). Connect stats to the business environment( expansion, macro pitfalls).
- Write conclusions & recommendations epitomize what the data implies for investors operation and list any caveats( data period, external events).
- Reference & tack — cite data source( s) and include numbers in an excursus.
Frequently Asked Questions
Q What maps are essential?
Histograms( distribution), time- series line maps or scatterplots( trend), and a volume vs. price smatter if you want volume – price relationship.
Q How numerous descriptive stats do I need?
At minimal mean, standard, and standard divagation for each numeric variable; add min/ maximum and IQR if space allows.
Q How do I handle outliers?
Identify them, report their effect( e.g., mean> standard), and either keep with comment or perform a perceptivity check( analysis with/ without outliers).
Q How long should interpretations be?
Keep each figure’s interpretation to 2 – 4 rulings tying the numeric pattern to business counteraccusations .
Q What common miscalculations to avoid?
Ignoring skew/ outliers, failing to label maps, and overclaiming occasion from correlation.
Q How to tie analytics to business opinions?
Translate trends into conduct( e.g., investor caution during high volatility, prioritize transnational- request monitoring) and mention external pitfalls( trade, climate).
Integrity Note
Note: Only use this assessment example for learning and structure purpose. Do not submit as your own work.
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