In the simple random sampling and the stratified sampling missions, we discussed the details around collecting data for analysis. In this mission, we'll focus on understanding the structural parts of a dataset and how they're meeasured.
Whether a sample or a population, a dataset is generally an attempt to describe correctly a relatively small part of the world. The dataset we worked with in the previous mission describes basketball players and their performance in the season 2016-2017.
Other data sets might attempt to describe the stock market, patient symptoms, stars from galaxies other than ours, movie ratings, customer purchases, and all sorts of other things.
The properties with varying values we call variables. The height property in our data set is an example of a variable. In fact, all the properties described in our data set are variables.
Notice that this particular meaning of the "variable" concept is restricted to the domain of statistics. A statistical variable is not the same as a variable in programming, or other domains.
In this mission, we'll explore the concept of statistical variables. We'll discuss the difference between quantitative and qualitative variables, discrete and continous variables, and more.
While this mission is primarily theory-based, the R programming environment inside of this mission includes answer checking so you can ensure that you've fully mastered each concept before learning the next concept.
1. Variables in Statistics
2. Quantitative and Qualitative Variables
3. Scales of Measurement
4. The Nominal Scale
5. The Ordinal Scale
6. The Interval and Ratio Scales
7. The Difference Between Ratio and Interval Scales
8. Common Examples of Interval Scales
9. Discrete and Continuous Variables
10. Real Limits
11. Next steps