38574458
Description
| Question | Answer |
| 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. |
| 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. |
| 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. |
| exploratory visualizations | Made when the lines between steps“P”(perform test plan),“A” (address and refine results), and “C” (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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| qualitative data | Categorical data. All you can do with these data is 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. |
| 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. |
| 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. |
| 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. |
| standardization | 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. |
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