Print This Page
**I Like Abstract Stuff; Why Should I Care?**: At a Glance

- Topics At a Glance
- Types of Data
- Qualitative v. Quantitative Data
- Categorical Data
- Discrete v. Continuous Data
- Univariate v. Bivariate Data
- Analysis of Single-Variable Data
- Range
- Mode
- Mean/Average
- Median
- Quartiles
- Pictures of Single-Variable Data
- Stem and Leaf Plots
- Bar Graphs and Histograms
- Pie Charts/Circle Graphs
- Box and Whisker Plots
- Bivariate Data
- Scatter Plots
- Linear Regression
- Probability
- Outcomes and Events
- Important Elements
- Odds
- Compound Events
- Independent and Dependent Events
- Mutually Exclusive Events
- Factorials, Permutations, and Combinations
- Factorials and Permutations
- Combinations
- More Probability
**In the Real World****I Like Abstract Stuff; Why Should I Care?**- How to Solve a Math Problem

Mathematicians deal with two different kinds of numbers.

**Discrete** numbers are numbers that have obvious gaps between them, like Letterman's teeth. For example, integers are discrete: there's a huge, obvious gap between the numbers 1 and 2, and there are no other integers between them.

**Continuous** numbers are numbers where no matter how close two of them are, there's another number between them. The real numbers are continuous: Even if we get two numbers as close together as 0.111 and 0.1111, we can still find a number in between them: for example, 0.11105.

Our study of probability has been dealing with discrete numbers (the number of marbles in a jar, or socks in a drawer), but mathematicians also study probabilities involving continuous numbers. If you have 0.111 socks in your drawer, it's time to go sock shopping.

If you take a data set and make histograms with thinner and thinner rectangles, you see pictures that are getting increasingly closer to something called a **probability distribution**, which is usually studied in calculus and beyond. Now that we've planted that irresistible little seed of anticipation in your noggin, let's wrap this puppy up.