Do you agree with their visualization comparisons? Why or why not?
Respond to at least two of your classmates by commenting on their posts. Do you agree with their visualization comparisons? Why or why not? Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Rebecca Cline
9:50am
Mar 11 at 9:50am
Data visualization makes it simpler than ever to show data in a way that is not only accessible to the user, but also inspiring. Tables are a preferred visualization technique when you are showing data in its raw format. Tables allow for a large amount of precise data to be displayed. Tables allow for the totals to be easily identifies and allows the user to be able to get into the nitty gritty of the information being presented (Durcevic, 2019).
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Five types of charting techniques are line chart, bar graphs, pie chart, gauge chart, and area chart. Line charts are useful to show trends, accelerations, and volatility (Durcevic, 2019). Line charts can show relationship and how data changes over a period of time (Durcevic, 2019). Bar graphs come in different styles, each with their own benefit. The first is a horizonal bar graph which ideal for comparative rankings. Next is a column graph which is ideal or showing chronological data. The last is a stacked column chart which is ideal for showing part to whole relationships (Durcevic, 2019). Pie Charts are ideal for demonstrating proportional composition. Gauge Charts are ideal for “displaying a single value/measure within a quantitative context” (Durcevic, 2019). Although area Charts are closely related to the line chart, they are better at displaying a part to whole relationship while connecting data points (Durcevic, 2019).
Some charts are better at displaying data than others, and depending on the data and the information that one is looking to provide, a charting type can be selected to get the point across more effectively. For example, a line chart and an area chart are very similar and could be used to display the same information, but if a line cart was used, the part to whole relationship of that data would be lost.
Simpson’s paradox results from inappropriately combining percentages of different groups. Only comparable measurements for comparable individuals should be combined (Sharpe, De Veaux, & Velleman, 2019, pg. 39). Simpson’s paradox occurs when “looking only at the percentages in the separate data ignores the sample size” (Koehrsen, 2018). One example of this happening is when there is an uneven number of men and women who are surveyed and the data is then combined. In this example of restaurant recommendations, there were more men surveyed at Carlo’s and more women surveyed at Sophia’s. The rate of recommendation is skewed by combining the combination of the results (Koehrsen, 2018). The table below shows Simpson’s paradox.
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Resources:
Durcevic, S. (2019, May 2). Choosing the right data Visualization types to present your data. Retrieved March 11, 2021, from https://www.datapine.com/blog/how-to-choose-the-right-data-visualization-types/
Koehrsen, W. (2018, October 10). Simpson\’s paradox: How to Prove opposite arguments with the same data. Retrieved March 11, 2021, from https://towardsdatascience.com/simpsons-paradox-how-to-prove-two-opposite-arguments-using-one-dataset-1c9c917f5ff9
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from https://www.redshelf.com
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