So, here's some descriptive statistics for this example.

That would be descriptive statistics.

So there are the descriptive statistics, or summary statistics.

Well, we could just run descriptive statistics.

And we'll plot histograms and get descriptive statistics.

We'll see when we look at the descriptive statistics.

In the next video, we'll explore more descriptive statistics.

A lot of first courses in statistics start with the distinction between descriptive statistics and inferential statistics.

Most statistics courses, most textbooks on introductory statistics will start with this distinction between descriptive and inferential statistics.

Think back to descriptive statistics and the lecture on correlations.

Inferential Statistics, that's a common technique because Descriptive Statistics one, are a lot easier and two, you need to use a lot of the concepts that you learn in Descriptive Statistics to do Inferential Statistics.

Descriptive Statistics. As it's said in the first lecture, most introductory

All the descriptive statistics for these four wines for the 30 wine experts who rates these wines.

Welcome back. We're on segment three of Lecture 2 on descriptive statistics.

Hi. Welcome to the second segment, of lecture two, on descriptive statistics.

When we're doing just descriptive statistics on samples, we'll use n in the denominator for these formulas.

And, as you remember, I talked about correlations as being on the cusp of descriptive statistics and inferential statistics.

I can get the descriptive statistics and then I can just run correlations.

So then what it does is it gives me two sets of descriptive statistics.

And we ran descriptive statistics like the mean, the standard deviation, skew and kurtosis.

It should also produce descriptive statistics that look like this. Again, these are from

Descriptive name for the device

So this is like way back in descriptive statistics where we did that example with

With a new data set, it'll ask you to produce histograms and produce descriptive statistics.

A descriptive name for the profile

So I just wrote a comment saying here are the descriptive statistics for the variables in a data frame called ratings.

And then we get a bunch of descriptive statistics, okay, actually more than we need, ah, for our purposes right now.

And as we begin our journey into the world of statistics, we will be doing a lot of what we can call descriptive statistics.

lecture two on descriptive statistics we talked about how to convert any scale into a Z distribution.

We'll put all four of those on one page. Finally, what we want to do, is get descriptive statistics for all these.

Statistics for Untitled

But it is important to remember, descriptive statistics just describe sample data and inferential statistics are procedures that are used to make generalizations from samples about populations.

You have descriptive.

It reads data, it plots histograms, and it gets descriptive statistics. The first function it calls is library psych.

Enter any descriptive name you like for this filter.

Enter descriptive label here

And, what we'll do is cover a lot of the material that we covered, in the second lecture on descriptive statistics.

I used this as an example back when we were talking about histograms and descriptive statistics and I showed you this histogram.

So if you remember from lecture two on descriptive statistics, one way to look at your data is to look at histograms.

Make it short and descriptive.

As I said, this is pretty easy to pick up if you think back to our calculations in lecture two on descriptive statistics.

So, that's real easy, there's a function called describe and if I do describe ratings, that will return descriptive statistics for the four variables in my data frame ratings.

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