MBA FPX 5008 Assessment 2 Using Analytic Techniques to Add Meaning to Data

Using Analytic Techniques to Add Meaning to Data  

Student Name

Capella University 

MBA-FPX 5008: Applied Business Analytics  

Dr. Alex Amegashie 

April 15, 2023

Introduction

Express Inc. is a fashion-forward clothing company that was founded in Columbus, Ohio, with a mission to create confidence and inspire self-expression (About us, n.d.). The company offers a curated collection of top trends, focusing on modern, flexible wardrobe options with great fits, premium fabrics, and impeccable details (About us, n.d.). As a commercial retailer with a significant presence in malls, Express operates in a highly competitive market. Its major competitors include American Eagle, Gap, and Forever 21, with varying annual revenues ranging from 2.7 billion to 16.2 billion dollars (Express, 2022).

Maintaining a competitive advantage and staying relevant in a market with numerous competitors can be challenging. While many companies capitalize on rapidly changing fashion trends, Express distinguishes itself by focusing on style rather than fleeting fashion trends (Cervellon & Wernerfelt, 2012; Gadel, 1985). Their mission emphasizes the importance of individual style and a modern, flexible wardrobe (About us, n.d.). This style-focused approach sets Express apart from its trend-focused competitors. However, the COVID-19 pandemic has significantly impacted consumer spending and preferences, with remote work and a preference for casual clothing becoming more prevalent.

Graphical Representations of Data

To analyze the stock performance of Express, scatter plots and histograms were created using data from Yahoo Finance. The scatter plot displays the highest and lowest daily stock prices over a year. Excel was utilized for data analysis and chart creation. The scatter plots were customized by adding trend lines to illustrate the direction of stock price changes over time (Microsoft, n.d.). Another histogram was created to visualize the adjusted closing stock prices and stock trading volume. The data analysis involved determining the appropriate range and bin size for displaying the information accurately (Capella University, n.d.). Gridlines and data labels were added to enhance the clarity of the histograms.

(For Graphics Representation Please contact OnlineClassAssignment Writers Team)

Descriptive Statistics

Descriptive statistics, including mean, median, mode, and standard deviation, were calculated for the adjusted daily closing stock prices and stock trading volume. Formulas in Excel were used to perform these calculations (Capella University, n.d.). The mean and median values of the adjusted daily closing stock prices showed a small difference, indicating a relatively even distribution of prices around the mean. The standard deviation indicated that the adjusted daily closing stock prices remained close to the mean, suggesting a reliable and consistent pattern (Lind, 2021). In contrast, the stock trading volume exhibited less reliable data, with a wide range of values. The standard deviation of the volume data indicated high volatility, likely due to extreme outliers (Lind, 2021).

Adjusted Daily Closing Stock Prices 

Mean 3.976996047 

Median 3.86 

Mode 5.37 

Standard deviation 1.089687076 

Stock Trading Volume 

Mean 3847462.055 

Median 3047700 

Mode #N/A 

Standard deviation 2688317.05 

(For Graphics Representation Please contact OnlineClassAssignment Writers Team)

Data Analysis
The analysis of stock prices over the past year revealed a downward trend, as indicated by both the scatter plots of highest and lowest daily stock prices. The histogram of adjusted closing stock prices showed an even distribution, with minimal variation between the mean and median values. This suggests a consistent pattern in the stock prices. However, the stock trading volume histogram indicated a skewed distribution with a concentration of values below a certain threshold, but also a wide range of high outliers. The high standard deviation for the stock trading volume confirms the volatility and unpredictability of this data (Lind, 2021).

Overall, the data analysis provides insights into the stock performance of Express Inc., highlighting the impact of external factors such as the COVID-19 pandemic and the need to adapt to changing consumer preferences in the fashion industry.

References 

About Us. Express. (n.d.). Retrieved July 10, 2022, from https://www.express.com/g/about-us Capella University. (n.d.). Using Analytic Techniques to Add Meaning to Data Walkthrough.  Capella University. Capella University. Retrieved July 10, 2022, from  

https://media.capella.edu/coursemedia/mba5008element19233/wrapper.asp.  

Cervellon, M.‐C., & Wernerfelt, A.‐S. (2012). Knowledge sharing among green fashion  communities online: Lesson for the sustainable supply chain. Journal of Fashion  

Marketing and Management, 16(2), 176–192. https://doi.org/10.1108/13612021211222860 Express. Owler. (2022). Retrieved July 10, 2022, from https://www.owler.com/company/express  Gadel, M. S. (1985). Commentary: Style‐oriented apparel consumers. In M. R. Solomon (Ed.),  The psychology of fashion (pp. 155–157). Boston, MA: D.C. Heath and Company. 

Lind, D. A. (2021). Basic Statistics for Business and Economics (10th Edition). McGraw-Hill  Higher Education (US). 

Microsoft. Microsoft Support. (n.d.). Retrieved July 10, 2022, from  

https://support.microsoft.com/en-us/topic/present-your-data-in-a-scatter-chart-or-a-line chart-4570a80f-599a-4d6b-a155-104a9018b86e?ui=en-us&rs=en-us&ad=us 

Yahoo! (2022, July 10). Express, Inc. (EXPR) Stock Historical Prices & Data. Yahoo! Finance.  Retrieved July 10, 2022, from https://finance.yahoo.com/quote/EXPR/history?period1=1625616000&period2=1657238400&interval=1d&filter=history&frequency=1d& includeAdjustedClose=true