Chapter 4

nominal data

- least sophisticates - can count, group, and take a proportion

Charts for Quantitative data

- line charts - box and whisker plots - scatter plots - filled geographical maps

ratio data

- most sophisticated - quantitative data - counted and grouped - differenced between each data point is meaningful - has a meaningful 0

Interval data

- no meaningful 0 - counted and grouped - differences between data are meaningful

standard normal distribution

- normal distribution used for standardizing data -0 for its mean and 1 for standard deviation

example of discrete data

- points in a basketball game

discrete data

- represented by whole numbers

benefit of standardizing data

you don't have to compare wildly different numbers and attempt to eyeball how one observation differs from the other

examples of normal distributions

- SAT scores - heights of newborns

normal distribution

- a type of distribution in which the median, mean, and mode are all equal - half of all observations fall below the mean and the other half fall above the mean

Qualitative Comparison Chart Types

- bar chart - pie chart - stacked bar chart - tree map - heat map

3 most used charts with qualitative data

- bar charts - pie charts - stacked bar chart

ordinal data

- can be counted, categorized, and ranked

continuous data

- can take on any value in a range - height

qualitative date

- categorical data - you can count and group

Word clouds

- count frequency of each word mentioned - the higher the frequency the larger the word

examples of ordinal data

- gold, silver, bronzee, 1-5 scales, letter grades

quantitative data

- interval or ratio data - can bee counted ranked, grouped, mean meadian , and std devation

Exploratory visualization

- the lines between performing a test plan, address and refine results, and communicate results are not clearly divided - performing test plan directly in Tableau - answers to the questions from step I won't have already been answered before working with the data in the visualization software

tree and heat maps

- use size and color to show proportional size of values - tree maps show proportions using physical space - heat maps use color to highlight the scale of values

Determining the method for communicating your results required the answers to what two questions?

1) Are you explaining the results of previously done analysis or are you exploring data through visualization 2) What type of data is being visualized

2 Questions that quadrants represent

1) is your purpose declarative or exploratory 2) What type of information is being visualized

Two ways to further define qualitative data

1) nominal data 2) ordinal data

The Fahrenheit scale of temperature measurement would best 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

What is the most appropriate chart when showing a relationship between two variables (according to exhibit 4-8)? A: Scatter chart B: Bar Chart C: Pie graph D: Histogram

A: Scatter chart

Gold, silver, and bronze medals would be examples of: A: Nominal data B: Ordinal Data C: Structured data D: Test data

B: Ordinal Data

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 follow IS NOT a typical example of nominal data? A: Gender B: SAT scores C: Hair color D: Ethnic group

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

__________________ data would be considered the least sophisticated type of data A: Ratio B: Interval C: Ordinal D: Nominal

D: Nominal

Exhibit 4-8 gives chart suggestions for what data you'd like to portray. Those options include except: A: Relationship B: Comparison C: Distribution D: Normalization

D: Normalization

If you data project is more declarative than exploratory, it is more likely that you will perform your data visualization to communicate the results

Excel

Why are bar charts more easily interpreted?

Our eyeballs are more skilled at comparing the height of columns than slices of a pie

pie chart

a chart that shows the relationship of a part to a whole

what does the meaningful 0 allow us to do?

calculate fractions, proportions, percentages

proportion

calculated by counting the number of items in a category then dividing that number by the total number of observations

examples of nominal data

color, gender, ethnic group

example of ratio data

currency

Why can qualitative data (nominal and ordinal) be called conceptual data?

data is text driven and represents concepts instead of numbers

Quantitative data can be further categorized as

discrete or continuous

example of interval data

fahrenheit temp scale

symbol maps

geographic maps used to express qualitative data proportions across areas such as states or countries

In all quantitative data the ___________ between data are meaningful

intervals

Why is tableau better for exploratory data analysis

intuitive and easy to use

What is the primary statistic used with quantitative data?

proportion

line charts

show trends over time

bar chart

shows the proportions of each category compared to the others

standardization

the method used for comparing two datasets that follow the normal distribution - standardize with z-scores

declarative visualizations

the product of wanting to declare or present your findings to an audience

excels biggest advantage over Tableau

ubiquity

scatter plots

useful for identifying correlation between two variables or for identifying a trend line or line of best fit

box and whisker plot

useful for when quartiles, median, and outliers are required for analysis and insights - used to detect outliers