Accounting Issues: Chapter 4 - Communicating Results and Visualizations

Exhibit 4-3

The Four Chart Types Quadrant with Detail

Nominal Data

The least sophisticated type of data on the scale of nominal, ordinal, interval, and ratio; a type of qualitative data. The only thing you can do with nominal data is count, group, and take a proportion. Examples of nominal data are hair color, gender, and ethnic groups.

Standardizing

The method used for comparing two datasets that follow the normal distribution. By using a formula, every normal distribution can be transformed into the standard normal distribution. If you standardize both datasets, you can place both distributions on the same chart and more swiftly come to your insights.

Which type of chart is best described as useful for identifying the correlation between two variables, for identifying a trend line, or line of best fit? a. Scatter Plots b. Box and whisker plots c. Line chart d. Pie charts

a. Scatter Plots

What 3 charts are most frequently considered for QUALITATIVE data?

-Bar Charts -Pie Charts -Stacked Bar Chart Others that are less considered: -Tree Maps and heat maps -Symbol Maps -Word Clouds

What are the 3 types of data that can be visualized?

1. Conceptual 2. Qualitative data or data-driven 3. Quantitative data

Standard Normal Distribution

A special case of the normal distribution used for standardizing data. The standard normal distribution has 0 for its mean (and thus, for its mode and median, as well), and 1 for its standard deviation.

Normal Distribution

A type of distribution in which the median, mean, and mode are all equal, so half of all the observations fall below the mean and the other half fall above the mean. This phenomenon is naturally occurring in many datasets in our world, such as SAT scores and heights and weights of newborn babies. When datasets follow a normal distribution, they can be standardized and compared for easier analysis.

The Fahrenheit scale of temperature measurement would be described as an example of: A) Interval data B) Discrete data C) Nominal data D) Continuous data

A) Interval data

____ data would be considered the most sophisticated type of data. A) Ratio B) Interval C) Ordinal D) Nominal

A) Ratio

Line charts are not recommended for what type of data? A) Normalized data B) Qualitative data C) Continuous data D) Trend Lines

B) Qualitative data

Which of the following is not a typical example of nominal data? A) Gender B) SAT Scores C) Ethnic group D) Hair color

B) SAT Scores

Justin Zobel suggests that revising your writing requires you to "Be egoless- ready to dislike anything you have previously written", suggesting that it is_______ you need to please: A) Yourself B) The reader C) The Customer D) Your Boss

B) The reader

Qualitative Data

Categorical data. All you can do with these data are count and group, and in some cases, you can rank the data. Qualitative data can be further defined in two ways: nominal data and ordinal data. There are not as many options for charting qualitative data because they are not as sophisticated as quantitative data. Qualitative data (both nominal and ordinal) can also be referred to as "conceptual" data because such data are text-driven and represent concepts instead of numbers.

____ data would be considered the least sophisticated type of data. A) Ratio B) Interval C) Ordinal D) Nominal

D) Nominal

Declarative Visualizations

Made when the aim of your project is to "declare" or present your findings to an audience. Charts that are declarative are typically made after the data analysis has been completed and are meant to exhibit what was found in the analysis steps.

Quantitative Data

More complex than qualitative data. Quantitative data can be further defined in two ways: interval and ratio. In all quantitative data, the intervals between data points are meaningful, allowing the data to be not just counted, grouped, and ranked, but also to have more complex operations performed on them such as mean, median, and standard deviation.

Discrete Data

One way to categorize quantitative data, as opposed to continuous data. Discrete data are represented by whole numbers. An example of discrete data is points in a basketball game.

Continuous Data

One way to categorize quantitative data, as opposed to discrete data. Continuous data can take on any value within a range. An example of continuous data is height.

Ratio Data

The most sophisticated type of data on the scale of nominal, ordinal, interval, and ratio; a type of quantitative data. They can be counted and grouped just like qualitative data, and the differences between each data point are meaningful like with interval data. Additionally, ratio data have a meaningful 0. In other words, once a dataset approaches 0, 0 means "the absence of." An example of ratio data is currency.

Proportion

The primary statistic used with quantitative data. Proportion is calculated by counting the number of items in a particular category, then dividing that number by the total number of observations.

What charts are appropriate for Quantitative Data? What are some more complex charts?

The same that are used for Qualitative (Bar charts, pie charts, stacked bar chart, tree maps, heat maps, symbol maps .... NOT word clouds) -Line Charts -Box and Whisker Plots -Scatter Plots -Filled geographic maps

Ordinal Data

The second most sophisticated type of data on the scale of nominal, ordinal, interval, and ratio; a type of qualitative data. Ordinal can be counted and categorized like nominal data and the categories can also be ranked. Examples of ordinal data include gold, silver, and bronze medals.

Interval Data

The third most sophisticated type of data on the scale of nominal, ordinal, interval, and ratio; a type of quantitative data. Interval data can be counted and grouped like qualitative data, and the differences between each data point are meaningful. However, interval data do not have a meaningful 0. In interval data, 0 does not mean "the absence of" but is simply another number. An example of interval data is the Fahrenheit scale of temperature measurement.

As discussed in the book and consistent with the writings of author Justin Zobel, your written business communications should be directed towards ________. a. your audience b. your supervisor c. your reviewer d. your customer

a. your audience

Which of the following is not a tool you should use to maximize your charts for a colorblind audience? a. Use a orange/blue color pallet b. Use natural increments in your data scale such as 1s or 100s. c. Avoid red/green color pallet d. Convert your chart to grayscale to check the constract

b. Use natural increments in your data scale such as 1s or 100s.

Gold, Silver, and Bronze medals would be examples of: a. nominal data b. ordinal data c. structured data d. test data

b. ordinal data

________ are the kind of visualizations that present findings to an audience. a. Exploratory Visualizations b. Static Visualizations c. Declarative Visualizations d. Interactive Visualizations

c. Declarative Visualizations

Polar bears, brown bears, black bears, and panda bears would be best described as an example of: a. Ratio data b. Ordinal data c. Nominal data d. Interval data

c. Nominal data

Letter grades of A, B, and C would be best described as an example of: a. Interval data b. Ratio data c. Ordinal data d. Nominal Data

c. Ordinal data

In the late 1960s, Ed Altman developed a model to predict if a company was at severe risk of going bankrupt. He called his statistic Altman's Z-score, now a widely used score in finance. Based on the name of the statistic, which statistical distribution would you guess this came from? a. Normal distribution b. Poisson Distribution c. Standardized normal distribution d. Uniform Distribution

c. Standardized normal distribution

________ is data that is represented by whole numbers. a. Ordinal Data b. Continuous data c. Interval data d. Discrete data

d. Discrete data

The following are typical examples of ordinal data except: a. Hardness of a mineral b. Military rank c. Position in a race d. Distance

d. Distance

The following are typical examples of nominal data except: a. Eye color b. Zip codes c. Gender d. Grade Point Average

d. Grade Point Average

The following are typical examples of nominal data except: a. Hair color b. Eye color c. Gender d. Height

d. Height

Exploratory Visualization

made when the lines between stepsP(perform test plan),A (address and refine results), andC (communicate results) are not as clearly divided as they are in a declarative visualization project. Often when you are exploring the data with visualizations, you are performing the test plan directly in visualization software such as Tableau instead of creating the chart after the analysis has been done.