Assessment 4: Presenting Data Analysis Results Effectively
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
Capella University
MBA-FPX5008
24 May 2021
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
IBM is an abbreviation for the international business machine, and it is a multinational
company whose headquarters are located in New York City. The Company was founded in then 1911
under a different name; the initial name was the computing-tabulating recording company, which
IBM dealt with computer-related services and goods. It sold middleware, software, and hardware
(Watson, 2017). Additionally, the Company has a section specifically for cloud hosting and
consulting services. For about 28 consecutive years, IBM has held the record for most US patents by
a business.
IBM is the largest IT service provider in the world’s service business, whereby it has more
than 5 percent of the market share that is almost double the closest competitor. The Company counts
on investments and acquisitions to offer future growth. History of Stock Prices Volatility entails
statistical measures used to measure the spreading of the returns for a particular security or the
market index.
Often, if the Volatility is higher, the security is riskier. It shows the rates at which stock prices
increase or decrease within a given time. Volatility also helps in measuring the risks that are attached
to various securities. Two different stocks may begin from a similar stock price and end at the same
comparable Price, yet the Volatility may indicate the magnitude of fluctuation with time. Thus, before
commenting on the Volatility of the IBM stock, it is essential to calculate the Volatility using the last
prices provided in the image above on various dates (Stikeleather, 2013, Apr 24).
The Company is one of the most valuable companies in the world of computing since it has
invested heavily in research and development, which has been paying handsomely. Through research
and development, the worlds hope that more advanced hardware and software can solve problems.
Power inadequacy, for instance, has plagued the world for a very long time.
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
This first graph represents the lowest numbers for the year. The scattergraph is one of the
more accessible graphs to look at and interpret. While studying this graph, it shows throughout the
year that the numbers are constantly changing. There are says that have a significant increase, but in
turn, it has a drastic drop in numbers; by the end of the year, the numbers were up above 130,
whereas at the beginning of the year, it was above 112.5.
While looking over the second graph, the scatter graph is showing the lows throughout the
year. If I were to place the two charts side-by-side, they would almost look the same. By this
happening, it shows that the numbers did not fluctuate throughout the high and low numbers. As in
the first graph, the numbers started above 112.5 at the beginning of the year and went up and down
throughout the year and ended above 130.
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Descritive Statistics
Descriptive Statistics
Next, I will discuss the mean, mode, median, and standard deviation for the adjusted daily
closing stock prices and the stock volume. I found the mode by putting all the numbers in sequence
order, and then I look for the number showing up the most amount of times, which is the mode. The
mode for the adjusted daily closing stock price is $121.98. The mode for stock volume is nothing
because no number is duplicated. The median is found by looking for the number that is in the middle
of all the numbers.
The median for the adjusted daily closing stock price is $5507.671. The median for stock
volume is 120.8268. The standard deviation for the adjusted everyday closing stock price was
ad close
mean 121.5773
median 120.8268
mode 121.98
standard deviation
7.08524
Volume
mean 121.577 3
54994 49
median 120.826 8
55017 35
mode 121.98 55076 71
standar d deviati on
7.0852 4
551413 0
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$104.96, and for stock, volume was 1484781. I used the formula in Excel to calculate this
information. The mean for the adjusted daily closing stock price is $1842.80. This is done by adding
all the numbers and dividing by the number of digits there are. The mean stock price is 121.5773.
First, you need to understand how to get the mean of the data. You would add up all the data
in the adjusted daily closing stock price and then divide it by the number of added numbers, which
will give you the mean. The changed daily closing stock price would be what on average for the daily
fee. This does not mean that it cannot go over that amount, but that, on average, it will be around that
price throughout the whole year. What does it imply if the median is different from the mean? The
median is the middle number of all the numbers from the data. If they are different, that means that
the data is skewed in some ways. With standard deviation, it is a statistical measure to show
volatility. Using this allows people to see the estimated price movement, which will lead to average
strengths and weaknesses (Watson, 2017).
Histogram
1761100 Frequenc y
3576220 47
5391340 114
7206460 50
9021580 22
1083670 0
7
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This graph represents IBM's adjusted closing numbers for the whole year; this graph is called
a histogram graph and shows up differently compared to the last two graphs. With this graph it does
depict the whole year, however it is with grouping of numbers. Looking at this graph it shows that
highest numbers were between 110.4895 and 144.24, which was between 0 and 120 (Stikeleather,
2013, Apr 24).
The figure shows the frequency of daily adjusted closing stock prices, which fall within equally
distributed data ranges (bin). The adjusted closing stock prices are shown on the horizontal axis, and
the frequency of adjusted closing prices falls within the vertical axis. As shown in the table above, the
histogram is skewed to the left. This indicates that a significant part of points lower on the higher
1265182 0
4
1446694 0
2
1628206 0
2
1809718 0
0
3806350 0
3
More 0
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
ranges of daily adjusted stock prices. A more substantial number of data set lies between 105 and
$140. This shows that the adjusted everyday closing stock price was more stable in that bin. The
smaller number of adjusted daily closing stock prices lies between $110 and $137. This shows that
the stock prices were less stable in that ranges.
Conclusion
In conclusion, I have learned a lot about IBM, and after creating graphs and working on the data, it
has come to my attention that IBM will be around for a long time (Stikeleather, 2013, Apr 24). If you
continue to purchase from them, IBM is a company that might eventually be able to close down other
stores. I know that Walmart and other stores are starting to do the same thing as IBM, and I have tried
that, and I don't particularly appreciate how they set up is and how if the product is not available, I
will have to wait longer than they are wanting. While working on this data and creating the graphs, I
have learned a lot more about IBM that I was not aware of. The company needs to make sure that
they are aware of the trends, showing that there will be higher traffic during the holidays or more
purchases being made during this time.
Then during the off-seasons, there still be a lot of traffic, but a lot of time, the consumers might just
be browsing and not making as many purchases. This information will help IBM to know what to be
expecting within certain times of the year and will help them with products and being able to plan.
The scatterplots for lowest and highest stock prices show a positive linear relationship between stock
value and time starting February 2019, which assists know the effect of the improved revenue growth
for IBM Corporation in 2018 compared to the previous years. Also, the scatterplots show better price
valuation at the beginning of 2019 compared to 2018 (Watson, 2017). This exhibits the underlying
impact of IBM's quarterly performance. Interpretation of histograms assists understands while the
median for closing stock prices lay on the higher bin, median for stock volume fell on lower data bin,
showing increased demand for stocks of IBM (Watson, 2017).
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
The information will help IBM to be able to plan their budget and also let them know if they can hire
more people or if they are going to lay off people. Learning about the greater world and their
expectations is also something that this data will let IBM know by watching the trends with the data.
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
Reference
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2019). Basic statistics for business and
economics (9th ed.). New York, NY: McGraw-Hill.
Chapter 5, "A Survey of Probability Concepts."
While previous chapters on descriptive statistics have concerned data on past phenomena, this
chapter addresses computing the likelihood that something will occur in the future.
Chapter 8, "Sampling Methods and the Central Limit Theorem."
Sampling a population gives us information to make judgments and inferences about the
population. This chapter discusses methods of selecting a sample from a population and how to think
about the distribution of the sample.
Chapter 9, "Estimation and Confidence Intervals."
This chapter will help you think about different aspects of sampling, such as estimating a
population value and the range of values, or the confidence interval.
Hewitt, F. (2015). Storytelling: The heart of leadership. New Zealand Management, 62(1),
26–27.
This article is a brief account of the importance of using personal connections to make data
accessible in presentations.
Nussbaumer Knaflic, C. (2015). Storytelling with data: A data visualization guide for
business professionals. Hoboken, NJ: Wiley.
Chapter 1, “The Importance of Context.”
The components of context are who, what, and how. This chapter offers strategies for
interpreting context so you can communicate visually with data.
Stikeleather, J. (2013, Apr 24). How to tell a story with data [Blog post]. Retrieved from
https://hbr.org/2013/04/how-to-tell-a-story-with-data
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
This short post highlights five steps to tell a good story with data from the journalistic
perspective, including a helpful categorization of the different types of audiences that you may be
addressing with your data visualization.
Watson, H. J. (2017). Data visualization, data interpreters, and storytelling. Business
Intelligence Journal, 22(1), 5–10.
This cautionary reading gives specific examples of what not to do when creating an executive
presentation with data visualizations.
Microsoft. (n.d.). Add a pie chart. Retrieved from https://support.office.com/en-us/article/
Add-a-pie-chart-1a5f08ae-ba40-46f2-9ed0-ff84873b7863?ui=en-US&rs;=en-
US&ad;=US#__toc348714905
Microsoft. (n.d.). Create a box and whisker chart. Retrieved from https://support.office.com/
en-us/article/Create-a-box-and-whisker-chart-62f4219f-db4b-4754-aca8-4743f6190f0d
Microsoft. (n.d.). Create a histogram in Excel. Retrieved from https://support.office.com/en-
us/article/Create-a-histogram-in-Excel-85680173-064b-4024-b39d-80f17ff2f4e8?ui=en-US&rs;=en-
US&ad;=US
Microsoft. (n.d.). Load the Analysis ToolPak in Excel. Retrieved fromhttps://
support.office.com/en-us/article/Load-the-Analysis-ToolPak-in-Excel-6a63e598-
cd6d-42e3-9317-6b40ba1a66b4?
CTT=1&CorrelationId;=9f089166-5317-42d4-8d41-56b3d207e812&ui;=en-US&rs;=en-
US&ad;=US
Microsoft. (n.d.). Present your data in a column chart. Retrieved from https://
support.office.com/en-us/article/Present-your-data-in-a-column-chart-d89050ba-e6b6-47de-b090-
e9ab353c4c00
APPLYING ANALYTIC TECHNIQUES TO BUSINESS
Microsoft. (n.d.). Present your data in a scatter chart or line chart. Retrieved from https://
support.office.com/en-us/article/Present-your-data-in-a-scatter-chart-or-a-line-
chart-4570A80F-599A-4D6B-A155-104A9018B86E.
Microsoft. (n.d.). Use the Analysis ToolPak to perform complex data analysis. Retrieved
from https://support.office.com/en-us/article/Use-the-Analysis-ToolPak-to-perform-complex-data-
analysis-6C67CCF0-F4A9-487C-8DEC-BDB5A2CEFAB6
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