Lesson video

Hello, everyone is Mr. Millar here.

Welcome to the first lesson on statistics.

And in this lesson, we're going to be learning about: The data handling cycle.

So, first of all, I hope that you're well, and if you haven't seen any of my lessons before on Oak, I'll just quickly introduce myself.

So I'm Mr. Millar, and I teach math at a school in central London.

I've been doing it for a few years now and really enjoy it.

So, I'm really happy to be part of Oak.

So, anyway, over the next 12 lessons, we're going to be looking at statistics, which is actually one of my favourite topics in maths.

And I hope that you're going to really enjoy it too.

So, just as a way of introducing statistics, I have a couple of nice quotations for you.

First of all, George Bernard Shaw said, "It's the mark "of a truly intelligent person to be moved by statistics." So, what that saying is, if you enjoy statistics, if you if you find it interesting, it means that you're smart basically.

So, It's a nice one to keep in mind.

But Mark Twain, who is a very famous writer said, "There are three kinds of lies: "Lies, damned lies and statistics." So, he was basically saying that when you see statistics, you have to be very careful because a lot of people twist statistics for their own benefit.

So, you always have to be careful, and that's why studying it is so important.

Now, I also got a couple of graphs and maps here.

And I just thought I'd bring these in because at the moment, well over the last few months, it depends when you're watching this video, but with Coronavirus, statistics has really been at the front and centre of a lot of news headlines.

So, the graph on the bottom left shows the daily new cases of Coronavirus in the UK.

And I'm doing this recording in July.

So, this is the latest available.

But this graph has caused really a lot of attention.

Because politicians and people are basing decisions based on the number of new cases in the UK.

But what you see may not always be a true picture or the reality because, for example, it's showing that in kind of March and early April, this region here that cases were quite low and they they went up quite significantly.

But actually, that may not be what actually happened because you can only get a new case if you have a test.

And at the beginning, they didn't have that many tests.

And then over time, the number of tests they were doing went up.

And actually, some people think that Coronavirus might have actually been in the UK even earlier, maybe in February or even January.

So, before March there are no cases, but that doesn't necessarily mean that there weren't any in reality.

And then finally, the graph on the right-hand side shows a map of the world.

And the bigger the dot, the more number of cases there have been.

And again, this may not be truly reflective of reality, because these are only the number of cases that each country reports.

And every country has a different way of collecting statistics.

And there's been a lot of controversy with people saying that some countries may be underreporting the actual number of cases that they have.

So, it may not be a true reflection of reality.

So, anyway, I just thought I'd show you these statistics in quotes because I thought you would find them interesting.

And over the next 12 lessons, we're going to be having a look at some of these statistics, so that you're prepared when you see them in the real world to make sure that you interpret them correctly.

Let's move on.

Okay, so over the next 12 lessons, we're going to be talking about this, what we call: The data cycle.

And you can see that there are four steps.

And I'm not going to go into this in too much detail at this moment, because we're going to be coming back to this a number of times.

But what we're going to start off with is, writing the hypothesis and planning our data collection.

And what that basically means is deciding what we're going to do our research on.

And then thinking about a hypothesis, something to test before we go and collect our data.

And so this is really at the centre of statistics.

And oh yeah, basically over the next 12 lessons, I'm going to be coming back to this and we're going to be discussing the different steps.

Okay, in this lesson, we're going to start off by having a look at the different types of data that we might see in statistics.

So, for the Try This task, let's have a look.

This is the first time I'm going to ask you to do something now cause I've been talking for a while.

So, a football scout goes to a training camp to gather some statistics on some players, how would you classify them? So, here in the boxes, I've got some different types of data, some different types of statistics.

So, there are nine pieces here that are very different.

So, pause the video now to have a read of these and to have a think about how you might classify them into different groups.

You can think of as many different groups as you want, you can think of two groups, three different groups, four different groups.

It's up to you.

Pause the video now to have a think of this about this Try This task.

Okay, great.

So, hope you had a nice thing about this and hope that was interesting.

And you might have thought of lots of different things.

You could have been thinking about maybe putting age and nationality together.

Because those are two things that are, maybe too personal to someone.

You could have put something like number of appearances and goals together.

That might tell you something interesting.

And maybe personal characteristics like height and weight, maybe you could have put them together.

But in statistics, there was actually a very special way that we group.

That we classify data.

And we're going to see this on the next slide.

So, here is the Connect slide.

And this is, as I said, how statisticians classify data.

So, first of all, the first question that we ask ourselves is, does the data have a numerical value? What that means is, can you put a number on the data? If not, we say that the data is qualitative.

So, some example of that might be, what's your favourite colour? Because your favourite colour is either pink or blue or green.

It can't be 17.

It can't be a numerical value.

If the data can't have any numerical value, then it is qualitative.

But if it can.

If you can put a number on it, then it is quantitative.

And if it's quantitative, we have to ask ourselves a further question.

And that question is, can the data take only certain values? So, for example, a piece of data that can only take certain values, might be the number of goals that you score.

Because you can score zero goals in a match.

You can score one, you can score two, but you can't score 1.

4 or 2.

8, or seven and 1/2.

You can only score a whole number of goals.

So, that would be discrete.

But if the data can take any value, then we say that it is continuous.

So, for example, the height that you are, is a continuous piece of data.

Because let's say you could be anywhere from 120 centimetres to.

If you're really tall, let's say you're 200 centimetres, then your height can be anywhere within this.

So, you can be for example, 160 centimetres.

You could also be 140.

1 centimetres.

So, there are an infinite.

There are any number of values that your height can take.

So, it may be worth that you pause the video now to copy down this diagram, because when it gets to the Independent task, it will be good to remind yourself about the three types of data.

First, qualitative.

Second, quantitative and continuous.

And third, quantitative and discrete.

So, pause the video now to get this into your next.

Okay, let's have a look at the Independent task.

So, here it is.

And the first question is, classify the data you saw in the Try This task into the three columns in the table.

So, the three columns are the three different types of data that we saw.

So, qualitative, discrete quantitative, and continuous quantitative.

And you need to classify age, nationality, goals, et cetera into the table.

And then a second question, same kind of thing.

Add to your table the following pieces of data: Favourite sport, shoe size and score in a test.

So, for example, your nationality.

That is something that you can't put a number on it.

So, that will go in qualitative.

But your.

Let's say your number of red cards, well, you can only have zero, one, two, three over a season.

So, that is discrete.

So, that would go in that middle column, red cards.

So, a few more for you to do.

So, pause the video to copy down the table and to put all of these pieces of data into the table.

Great, so hope that you have done that.

And on the next slide, I'm going to show you the answers.

Okay, so here are the answers and there are just a couple that I want to through.

So, the first one I want to go through is actually age because age is quite an interesting one.

I put it in continuous here because the age that you are, can be anything.

You can be 25, you can be 25 in three months and four days, you can be 25 in four months and five days and three hours.

So, your age can take any value.

But actually, a lot of the time, your age is reported as a whole number.

So, the last birthday that you had.

So, 25, 26, 27, so often ages reported as discrete.

So, for some of these, there's not to be a right or wrong answer.

It kind of depends and it's a bit of a grey area between them.

Another thing I wanted to talk about is shoe size.

Now shoe size is an interesting one because as you know your shoes, your shoe size can be four, 4.

55, five, 5.

5.

A lot of people think well, because it can be a decimal, because it can be 4.

5 that means it's continuous.

But shoe size is definitely discreet.

And the reason why it's discrete is, even though it can be a decimal, remember that if something can only take certain values, it is discrete.

And your shoe size can only take four, 4.

55, five, 5.

5, et cetera.

It can't take 4.

3, for example.

So, shoe size is definitely discrete.

Okay, so I hope that this makes sense.

And let's move on to the Explore task.

Okay, so here is the Explore task, let's have a read.

So, a designer clothes brand, Cool Sports Inc, wants to hire a student to do some data analysis.

Over the course of this week's lesson, so the first four lessons of the week, the Explore task will build up towards a pitch that you'll wrie to the company at the end of this week.

And you can use the hashtags, hashtag LearnwithOak.

I'll show you the others in a second, to show us your work at the end of the week, when you'll be writing a pitch that you are going to write to the company to get them to hire you.

So, over the course of the week, the tasks are going to build up to this pitch.

So, let's have a look at the first task.

Okay, so for this lesson, Cool Sports Inc, wants you to classify some data they are interested in, into qualitative, discrete quantitative or continuous quantitative.

So, the three different categories that we've had a look at.

So, let's have a look.

So, we've got six different things here.

Remember, they're a clothing company, so they might be interested in the amount of money that people spend on clothes.

They might be interested in the favourite brands.

So, with all of these six different types of data, I want you to classify them into the three different types that we've talked about.

They also want you to think of three more types of data that you think will be interesting for them to study.

So, I hope this makes sense, you're going to classify these six and come up with three more and put them into a category as well.

And just to remind you that the Explore task in Lesson Four, so the final lesson of this week of lessons, is going to come up with a pitch.

So, you'll need to have a look at all of the Explore tasks over the course of the week, if you want to be successful in that.

Okay, so on the final slide, here is what you can do to show us your work.

So, you can use the hashtag LearnwithOak, and you can tag on Twitter, the Oak National accounts, and I'll remind you again in the final lesson of this week, so you can share your pitch with us if you want to.

Anyway, that is it for today's lesson.

Thanks very much for watching this kind of introductory lesson.

And in the next lesson, we're going to learning about hypothesis testing in more detail.

Thanks very much.

Take care.

Have a nice day and see you next time.

Bye-bye.