V1 Descriptive Statistics

Hi everyone. Welcome to this first video on descriptive statistics. I'm Renee Clark from the Swanson School of Engineering. In this first video we're going to talk about the following topics: what are the two main types of data? The two main types are quantitative and qualitative and also why it's important to differentiate between them. From there we're going to talk about the subtypes of quantitative data with some examples as well as the subtypes of qualitative data with some examples.

Okay so two main types of data: the first is quantitative, the second is qualitative. But the question is why is it important to differentiate between them? The answer is is because this enables you to choose and apply the correct statistical technique for a certain type of data or certain data set that you might have.

Okay quantitative data which is the first main type consists of measurements and numbers. Okay there are two subtypes of quantitative data. The first is continuous data and you've heard this term before. Okay continuous data has a whole number as well as a fractional or decimal portion. Okay so for example 10.912 kilograms. Okay the whole number portion is 10 the decimal or fractional portion is the 912. Okay with continuous data or continuous variables you can specify them theoretically to finer and finer levels of precision. Okay so this means that any value is possible. So for example 10.11 2 3 4 6 6 7 on and on and on inches is a theoretically possible value. Now whether you have the equipment to measure it to that level of precision is a different story but it is theoretically possible to have something that measures that. Okay the second subtype of quantitative data is discrete data. Okay now with discrete data or discrete variables it can have only certain or fixed values which we will describe it more on the next slide.

Okay still talking about quantitative data. Continuous data consists of things such as height, weight, time, length, temperature, humidity. Generally things you can measure. Okay discrete data, which as we said can only take on certain or fixed values, can consist of the following types of data. The first is count data, for example the number of pets you may have in your household. So for example you could say I have two pets in my household. You can't say you have 2.5 pets in your household right or 3.17 pets in your household. The number of pets or something similar can only be a whole number right? Another example of discrete data is shoe size. Okay, so for example, in the United States our system looks as follows: you know someone might have a size six or wear a seven and a half or wear a nine or a 12 and a half. Generally we don't see shoe sizes shoe sizes outside of that at least in the US system. Okay neck size would be another example of a of a discrete piece of data. Dollar amounts are also considered discreet.

Qualitative data also known as categorical data and this is because with qualitative data, the data comprise categories of a variable, which you'll see what we mean in a minute. There are two subtypes of qualitative data. Okay the first is ordinal qualitative data. Okay with ordinal data, the categories are naturally ordered and ranked. Okay so what do we mean by that? For example, okay teaching evaluation survey responses or OMET survey responses. Okay you as a student can rank an instructor's effectiveness on a scale anywhere from strongly disagree about a certain teacher's teaching effectiveness, to disagree, to neutral, to agree, to strongly agree. So there are five different categories that you know that measure a particular instructor's teaching effectiveness and you rank that instructor on one of those five categories. Okay another example of an ordinal qualitative variable or piece of data would be the severity of one's pain. Okay it may range anywhere from no pain, to mild pain, to moderate pain, to severe pain. Okay so as you can see, those pieces of data or the categories are naturally ordered or there's a natural ranking to them. Okay this is in comparison to nominal qualitative data, which is the second qualitative data subtype. With nominal variables, the categories have no natural ordering. Okay so for example, candy color: purple, red, or orange okay has no natural ordering. Now you may like one candy color over the next but that's a different variable. That would be preference for candy color. Candy color in and of itself has no natural ordering. Another example of a nominal variable would be region of the US, you know in terms of western region, midwestern region, etc. Okay again has no natural ordering. It is a nominal categorical variable.

We thank the National Science Foundation under Grant 2335802 for supporting our work. Thank you for watching!