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Download data on a company's stock history. From this data, create scatterplots, histograms, and calculate the mean, median, mode, and standard deviation of some data points. Write a 3-5-page report including the graphs and descriptive statistics you have created. Business analytics techniques are used to facilitate decision making by transforming large amounts of raw data into meaningful information. Many businesses rely on analysis of relevant historical data to make key strategic and operational decisions.

Therefore, understanding how to use techniques such as graphical representation and descriptive statistics to translate raw data into useful information can be a valuable skill in an organization. In this assessment and the next, you will have the opportunity to sharpen your analytics skills by locating and interpreting real-life stock data. You have been learning about how to explore data. In this assessment, you will apply those skills by downloading a practical dataset and creating graphical representations of that data. The work you do in this assessment will lay the foundation for future assessments in which you analyze and interpret those graphical representations.

Since the purpose of business analytics is to make sense of large quantities of raw data, this assessment helps you develop skills in applying analytics to business contexts by practicing the exploration and display of data. In addition to graphical and tabular summary methods, numeric or quantitative variables and data can be summarized numerically using various techniques of description and display. Descriptive methods, which describe existing data, are also methods for using a subset of the available data to estimate or test a theory about a measurement on a larger group. This larger group is called the population, and the measurement being studied is the parameter. The smaller group, or subset, of the population that is taken in order to make an inference (to make an estimate or test a theory) is referred to as the sample.

The measurement taken on that sample is then referred to as the statistic, which is usually the best single-number estimate for the population parameter of interest. Most often, however, the estimate should not be restricted to a single number that would be exactly correct or incorrect. Instead, it is preferable to calculate some range of possible values between which there can be a certain percent confidence that the true population parameter falls. These are referred to as confidence intervals. You are an analyst in a publicly traded company.

Your supervisor has asked you to create graphical representations from raw stock data for a company-wide meeting at the end of the quarter. YOUR ROLE Your task is to analyze the stock history of the company and create a scatterplot and a histogram. Then you will calculate mean, median, mode, and standard deviation of the adjusted daily closing stock price and the stock volume. It is your responsibility to turn that data into meaningful information using descriptive statistics.

Paper For Above instruction

In this report, I analyze the stock history of a selected publicly traded company, focusing on key statistical measures and visual representations to facilitate understanding of its stock performance over a one-year period. The process involved acquiring the data via Yahoo! Finance, creating relevant graphs, and computing descriptive statistics to interpret the stock's behavior, thereby supporting strategic decision-making.

Introduction

The chosen company for this analysis is [Company Name], a prominent player in the [industry/sector], known for its [brief description]. The company's stock performance reflects not only its internal operational health but also broader market trends and investor sentiment. Analyzing stock data helps inform both internal and external stakeholders regarding the company’s financial trajectory, risk factors, and growth potential. Business analytics techniques such as graphical visualization and descriptive statistics are vital tools in deciphering complex financial data, enabling data-driven decisions that support strategic objectives and operational improvements.

Data Acquisition and Preparation

The stock data was downloaded from Yahoo! Finance by selecting the historical prices for the past year. The specified settings included a one-year time period, daily frequency, and the ‘Historical Prices’ option. After downloading, the Excel file was reviewed to ensure completeness, with a total of approximately 250 trading days included. The essential columns for analysis—“High”, “Low”, “Adj Close”, and “Volume”—were extracted for further processing. This dataset provided the basis for creating visual representations and calculating descriptive statistics pertinent to stock price movements and trading volume.

Graphical Representations of Data

Scatterplot of Highest Stock Prices Over Time

To analyze the trend of the highest stock price over the year, a scatterplot was created with the “High” prices plotted against date. The process involved importing the dataset into Excel, selecting the date and high price columns, and generating a scatterplot through the Insert > Scatterplot feature. The graph visually depicts fluctuations and potential upward or downward trends in the stock's peak prices throughout the year, providing insights into volatility and performance patterns.

Scatterplot of Lowest Stock Prices Over Time

A similar process was followed for the “Low” prices. The respective data points were plotted against date, illustrating the stock's daily low points, which are critical for understanding the downside risk and intraday variability. This visualization helps identify periods of higher volatility or stability in the stock’s low-value range.

Histogram of Adjusted Closing Prices

The histogram of “Adj Close” prices was constructed to assess the distribution and frequency of different closing prices adjusted for dividends and splits. Using Excel's histogram tool, I adjusted the bin sizes to effectively display the data distribution, revealing the most common price ranges and the skewness or symmetry of the data. This graphical display helps understand the overall price behavior and potential price clusters during the year.

Histogram of Trading Volume

The volume histogram was generated similarly, focusing on trading activity levels. Bin sizes were adjusted to distinguish between low-volume and high-volume trading days, highlighting periods of increased investor activity or relative calm. The histogram provides a snapshot of trading intensity and liquidity over the year.

Descriptive Statistics

The measures of central tendency and variability—mean, median, mode, and standard deviation—were calculated for both the “Adjusted Close” prices and the “Volume” data. In Excel, functions such as AVERAGE, MEDIAN, MODE, and STDEV.S were used for these calculations. These statistics offer a summary of the typical stock price and trading volume, as well as insight into the variability and distribution of these data points.

Adjusted Closing Price

• Mean: The average adjusted closing price over the year was calculated to understand the typical closing value.
• Median: The middle value that separates the higher half from the lower half of the data, indicating the central tendency.
• Mode: The most frequently occurring adjusted closing price, highlighting price levels with recurring occurrence.
• Standard Deviation: A measure of the dispersion or volatility of the closing prices around the mean.

Trading Volume

• Mean: The average daily trading volume during the period.
• Median: The middle trading volume value.
• Mode: The most common trading volume level observed.
• Standard Deviation: The extent of variation in daily trading volume, indicating periods of unusual activity or stability.

Conclusion

Through this analysis, the stock data of [Company Name] revealed patterns, distribution characteristics, and volatility trends that are crucial for strategic planning. The graphical representations provided visual insights into price fluctuations and trading behavior, while the descriptive statistics summarized central tendencies and variability. These tools collectively enhance the understanding of stock performance, assisting managers and investors in making informed decisions based on historical data trends and market behaviors.

References

• Bloomberg. (2023). Stock Market Data. https://www.bloomberg.com
• Yahoo Finance. (2023). Historical Data for [Company Name]. https://finance.yahoo.com
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