Well there are maybe three categories.

So that's what we're going to focus on.

Let's just get into the meat of it and we'll start with the descriptive.

lecture two.

Statistics textbooks, whether they're in Psychology, or Economics, or Sociology, or

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

How would we detect a violation of that assumption?

And if I just subtract them, then I get a matrix of deviation score.

Well, let's go back and look at histograms.

The second way is we'll convert those scores into Z scores, remember from

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

And in the second segment, we'll actually write a script together.

But it does it for everybody uh, so it's just a little bias in the measurement uh, that's across the entire sample.

Is a statistical procedure that's used to measure and describe the relationship between two variables, and you can think of correlations, we're sort of at a point in this course where we're on the cusp of descriptive statistics and inferential statistics.

And if you think about it for a, just a second, you'll understand why.

Political Science, split the field of Statistics into Descriptives and

The goals for today's script, specifically are to read data into a data frame. Explore the contents of that data frame.

I can get the names of the variables.

We want to minimize the residual, we want to minimize our prediction errors.

let's remember what it means to be in scientific notation.

So the ages of people in the sample I believe ranges from around 30 up to about 70 is the highest.

And if you said any of those things, you would actually have done the same things that the people who first came up with descriptive statistics said.

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.

The first is death is a big problem.

But this is not quite official scientific notation.

And this is, obviously, not less than 10.

But here, we're taking into account these other variables so it gets a little more complicated in interpreting the gender difference.

Lecture 3, and this is an example script. What does this script do?

General Statistics

Now, to make this more concrete,

(Laughter)

A statistical edge?

So if we just looked at the histogram of residuals, we would see the extreme scores.

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

So this is 51i.

Is it just about a lack of education?

What do you want?

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

And we still have this 4.01534 times 10 to the negative 8.

That's what gets us to that eight.

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

Any line that starts with a pound sign is just a comment.

And that's indeed the case.

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

And I encourage you to play with a lot of numbers and deal with the concepts yourself.