Ratings

Imported ratings

I'm just creating an object that I call ratings. And that stands for the wine ratings.

I could have called it WR for wine ratings. I called it ratings. This less, what looks

look at class, ratings. We list the variable names, names ratings.

That play had good ratings.

Assigning image ratings. Please wait...

Obama's ratings can go up again.

Your cartoon ratings are really high.

So histogram ratings WoopWoop, RedTruck, HobNob, FourPlay.

So histograms, ratings, dollar sign, what, what, what that says is, go to the ratings data frame, and dollar sign picks out one of the columns in the ratings data frame.

And then finally you have the ratings agencies.

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.

So each one of those columns had 30 ratings.

And so the ratings agencies, Standard and Poor's and

I also, same as you, don't trust Yahoo Auction ratings.

It gives you the price, and the location and ratings.

And the ratings agencies mainly operate in the debt world.

So now I have, in R, an object called ratings.

So we'll do kind of a plugin review ratings reset.

Today, 16 African countries and growing have sovereign country ratings.

Moody's is one of the ratings agencies, and they rate

Credit card companies are not looking so hard at credit ratings.

You know, ratings in the 70s on a 100 point scale.

So again, think about the wine experts and the wine ratings.

So, what you see for WoopWoop is just a rectangular shape for the distribution. So, the ratings were all over the place.

Color closely correlates with the ratings that talks get on the website.

Just think of power sellers on eBay or star ratings on Amazon.

So ratings is just a variable that I made up in R. Describe is a function in R. So it takes the ratings and it applies the describe function.

Perhaps it is this appetite that underlies the ratings success of the paranormalists.

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

It took us 50 years to reach annual savings ratings of almost 10 percent.

I could have called that anything. Okay? I could have called it wine ratings.

If we want to know what type of object are these ratings, then we

Let's say that they're B I don't know, all these ratings have different terminology.

That's this line of code here. Ratings is read table. stats1.example1.txt header True.

Now let's further assume that those ratings for Red Truck in the population are perfectly normally distributed, as they were in our sample of 30.

The dollar sign again just says pick out one of my variables in the ratings data frame.

library. The next thing we do is we read the data into a data frame called ratings.

And actually, this is a good time to think about well who is doing all these ratings.

Those were the names of the columns in my file. The next thing we want to do is plot histograms for each of the wine ratings.

Equivalent of saying equal to. What we're saying is, let's set ratings equal to stats1.ex.01.txt.

Moody's, were willing to give AAA ratings to more and more, what I would argue, are risky loans.

Good examples are average, grade point average, uh, average SAT for different classes or different groups of people, um, and again, the average wine ratings in our example.

Just like we did in lecture 2. So we're going to use the wine tasting example of wine ratings.