Greetings, in this set of lectures, we'll look at ways to summarize information

in samples of continuous data measures.

As you may recall, continuous data measures include things that, well,

are measured on a continuum, where one unit change across the scale

means the same thing across the entire range.

Things like systolic blood pressures measured in millimeters of mercury,

or healthcare costs measured in dollars.

So we'll first look at numerical ways to summarize certain characteristics

of a sample of continuous data.

Things like measures of tendency, which include the mean and the median.

And numerical summarization of variability of the individual values in our

sample around their sample mean called standard deviation and

another measure of location called the percentile.

We'll look at various percentiles to establish different points of location

under the distribution of the continuous values.

And we'll see that while numerical summaries are really helpful in

characterizing certain aspects of these distributions.

It would be also helpful and

the data would lend itself to having visual tools for exploration.

So we'll define both histograms and box plots, talk about what they consist of and

look at them for certain samples of continuous data.

So we'll also get into the idea of comparing distributions of continuous

data, both normally and visually, using differences in means and

the aforementioned box plots and histograms.

One other thing we'll talk about in this set of lectures is, what is the role

of sample size in terms of what we can expect to get in our sample?

In other words, do we expect the distribution of sample values to differ

systematically as we increase sample size?

Or do we expect to stay consistent across samples of different size?

And would we expect the sample standard deviation to remain consistent

across samples of different sizes, or to systematically increase or

decrease with increasing sample size?

So these are some of the topics we'll cover in this lecture set.

So sit back and enjoy the ride.