Well there are maybe three categories.

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

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

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

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

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

lecture two.

How would we detect a violation of that assumption?

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

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

Enable usage statistics

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

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

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.

I can get the names of the variables.

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

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

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

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

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

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

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 we want to print all four histograms on one page, we use the layout function, then we plot all histograms using the histograms function.

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

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

Some of those, some of you who are familiar with statistics, and have done these calculations might have used that I used m in the denominator in all these equations, and that's because I'm still in the realm of descriptive statistics.

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

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

Times I should write like that.

From there, I can get the variance covariance matrix.

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.

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

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.

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

So are the measures, are these scores in column one, are they correlated with these scores in column two and so on with column three.

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.

So, let's go back to this example of measuring body temperature with the wand, that infra red meter.

Describes a way of computing totals for a given field or expression.

Perform symbolic and numeric calculations using Maxima

In this segment, we're going to do a lot of math, so we'll go through the calculation of the correlation coefficient.

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.

Describe Data Fields

We'll start with descriptives in, in the next lecture, but again I think it's more important to start with a distinction about how statistics are used.

The central limit theorem, and this is, was developed by a statisticians and is used in applied statistics to do inferentials.