Griffiths D. Head First Statistics

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Probability = area

For continuous random variables, probabilities are given by area. To find the

probability of getting a particular range of values, we start off by sketching the

probability density function. The probability of getting a particular range of values

is given by the area under the line between those values.

As an example, we want to find the probability that Julie has to wait for between 5

and 20 minutes for her date to turn up. We can find this probability by sketching

the probability density function, and then working out the area under it where x is

between 5 and 20.

The total area under the line must be equal to 1, as the total area represents the

total probability. This is because for any probability distribution, the total probability

must be equal to 1, and, therefore, the area must be too.

Let’s use this to help us find the probability that Julie will need to wait for over 5

minutes for her date to arrive.

The total area under the line must be 1. What’s the value of f(x)?

f(x)

P(X > 5) is equal

to the area here.

This area represents the total

probability so it must be equal

to 1.

f(x)

Hint: It’s a

constant value.

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Before we can find probabilities for Julie, we need to find f(x), the probability

density function.

So far, we know that f(x) is a constant value, and we know that the total area under

it must be equal to 1. If you look at the sketch of f(x), the area under it forms

a rectangle where the width of the base is 20. If we can find the height of the

rectangle, we’ll have the value of f(x).

We find the area of a rectangle by multiplying its width and height together.

This means that

1 = 20

height

height = 1/20

= 0.05

This means that f(x) must be equal to 0.05, as that ensures the total area under it

will be 1. In other words,

f(x) = 0.05 where x between 0 and 20

Here’s a sketch:

Now that we’ve found the probability density function, we can find P(X > 5).

This area must be equal to 1.

f(x)

f(x) = 0.05 where x

is between 0 and 20.

f(x)

0.05

finding f(x)

To calculate probability, start by finding f(x)…

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Do I have to use area to find

probability? Can’t I just pick all the

exact values in that range and add their

probabilities together? That’s what we

did for discrete probabilities.

…then find probability by finding the area

The area under the probability density line between 5 and 20 is a rectangle.

This means that calculating the area of this rectangle will give us the probability

P(X > 5).

P(X > 5) = (20 - 5)

0.05

= 0.75

So the probability that Julie will have to wait for more than 5 minutes is 0.75.

0.05

That doesn’t work for continuous probabilities.

For continuous probabilities, we have to find the probability by calculating the

area under the probability density line.

We can’t add together the probability of getting each value within the range

as there are an infinite number of values. It would take forever.

The only way we can find the probability for continuous probability

distributions is to work out the area underneath the curve formed by the

probability density function.

Area of rectangle = base x height.

When x is 5, f(x) = 0.05.

When dealing with

continuous data, you

calculate probabilities

for a range of values.

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So there’s a function called the

probability density function. What’s

probability density?

A: Probability density tells you how

high probabilities are across ranges, and

it’s described by the probability density

function. It’s very similar to frequency density,

which we encountered back in Chapter 1.

Probability density uses area to tell you

about probabilities, and frequency density

uses area to tell you about frequencies.

So aren’t probability density and

probability the same thing?

A: Probability density gives you a

means of finding probability, but it’s not the

probability itself. The probability density

function is the line on the graph, and the

probability is given by the area underneath it

for a specific range of values.

I see, so if you have a chart

showing a probability density function,

you find the probability by looking at area,

instead of reading it directly off the chart.

A: Exactly. For continuous data, you

need to find probability by calculating area.

Reading probabilities directly off a chart only

works for discrete probabilities.

Doesn’t finding the probability

get complicated if you have to calculate

areas? I mean, what if the probability

density function is a curve and not a

straight line?

A: It’s still possible to do it, but you

need to use calculus, which is why we’re

not expecting you to do that in this book.

The key thing is that you see where the

probabilities come from and how to interpret

them.

If you’re really interested in working out

probabilities using calculus, by all means,

give it a go. We don’t want to hold you back.

You’ve talked a lot about

probability ranges. How do I find the

probability of a precise value?

A: When you’re dealing with continuous

data, you’re really talking about acceptable

degrees of accuracy, and you form a range

based on these values. Let’s look at an

example:

Suppose you wanted a piece of string that’s

10 inches long to the nearest inch. It would

be tempting to say that you need a piece of

string that’s exactly 10 inches long, but that’s

not entirely accurate. What you’re really after

is a piece of string that’s between 9.5 inches

and 10.5 inches, as you want string that 10

inches in length to the nearest inch. In other

words, you want to find the probability of the

length being in the range 9.5 inches to 10.5

inches.

But what if I want to find the

probability of a precise single value?

A: This may not sound intuitive at first,

but it’s actually 0. What you’re really talking

about is the probability that you have a

precise value to an infinite number of

decimal places.

If we go back to the string length example,

what would happen if you needed a piece

of string exactly 10 inches long? You would

need to have a length of string measuring

10 inches long to the nearest atom and

examined under a powerful microscope.

The probability of the string being precisely

10 inches long is virtually impossible.

But I’m sure that degree of

accuracy isn’t needed. Surely it would

be enough to measure it to the nearest

hundredth of an inch?

A: Ah, but that brings us back to the

degree of accuracy you need in order for

the length to pass as 10 inches, rather

than finding the probability of a value to an

infinite degree of precision. You use your

degree of accuracy to construct your range

of acceptable measurements so that you can

work out the probability.

no dumb questions

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BE the probability density function

A bunch of probability density functions

have lost track of their probabilities.

Your job is to play like you’re the

probability density function and

work out the probability between

the specified ranges. Draw a sketch

if you think that will help.

2. f(x) = 1 where 0 < x < 1

Find P(X < 0.5)

4. f(x) = 0.1 – 0.005x where 0 < x < 20

Find P(X > 5)

3. f(x) = 1 where 0 < x < 1

Find P(X > 2)

1. f(x) = 0.05 where 0 < x < 20

Find P(X < 5)

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BE the probability density function solution

A bunch of probability density functions

have lost track of their probabilities.

Your job is to play like you’re the

probability density function and

work out the probability between

the specified ranges. Draw a sketch

if you think that will help.

2. f(x) = 1 where 0 < x < 1

Find P(X < 0.5)

4. f(x) = 0.1 – 0.005x where 0 < x < 20

Find P(X > 5)

When x = 5, f(x) = 0.075. This means we have to find

the area of a right-angled triangle with height 0.075

and width 15.

3. f(x) = 1 where 0 < x < 1

Find P(X > 2)

The upper limit of x for this probability

density function is 1, which means that

it’s 0 above this.

P(X > 2) = 0

1. f(x) = 0.05 where 0 < x < 20

Find P(X < 5)

P(X < 5) = 5 x 0.05

= 0.25

P(X < 5) is equal

to the area here

f(x)

0.05

P(X < 0.5) = 1 x 0.5

= 0.5

f(x)

0.5

The probability density

is 0 where X > 1.

f(x)

P(X > 5) = (0.075 x 15)/2

= 1.125/2

= 0.5625

f(x)

0.1

0.075

The area of a triangle is

1/2 the base multiplied

by the height.

be the probability density function solution

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Discrete data is composed of

distinct numeric values.

Continuous data covers a range,

where any value within that range is

possible. It’s frequently data that is

measured in some way, rather than

counted.

Continuous probability distributions

can be described with a probability

density function.



You find the probability for a range

of values by calculating the area

under the probability density function

between those values. So to find

P(a < X < b), you need to calculate

the area under the probability density

function between a and b.

The total area under the probability

density function must equal 1.



We’ve found the probability

So far, we’ve looked at how you can use probability density functions

to find probabilities for continuous data. We’ve found that the

probability that Julie will have to wait for more than 5 minutes for her

date to turn up is 0.75.

That’s great, at least

now I have an idea of

how long I’ll be waiting.

But what about my shoes?

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Searching for a soul mate

As well as preferring men who are punctual, Julie has preconceived ideas

about what the love of her like should be like.

I need a man who’ll be taller

than me when I wear my

highest heels. Shoes definitely

come first.

Julie loves wearing high-heeled shoes, and the higher the heel, the

happier she is. The only problem is that she insists that her dates

should be taller than her when she’s wearing her most extreme set of

heels, and she’s running out of suitable men.

Unfortunately, the last couple of times Julie was sent on a blind date,

the guys fell short of her expectations. She’s wondering how many

men out there are taller than her and what the probability is that her

dates will be tall enough for her high standards.

So how can we work out the probability this time?

sole

height probabilities

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Male modelling

So far we’ve looked at very simple continuous distributions, but it’s

unlikely these will model the heights of the men Julie might be dating.

It’s likely we’ll have several men who are quite a bit shorter than average,

a few really tall ones, and a lot of men somewhere in between. We can

expect most of the men to be average height.

Most men will be

around average height.

There’ll be a few men

who are much shorter

than the average.

We can expect some

men to be extra tall.

Given this pattern, the probability density of the height of the men is likely

to look something like this.

There are fewer shorter

guys, so the probability

density is low.

Most men will be

average height.

There’ll be a smaller

number of tall guys.

This shape of distribution is actually fairly common and can be

applied to lots of situations. It’s called the normal distribution.

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The normal distribution is called normal because it’s seen as an ideal. It’s

what you’d “normally” expect to see in real life for a lot of continuous data

such as measurements.

The normal distribution is in the shape of a bell curve. The curve is

symmetrical, with the highest probability density in the center of the curve.

The probability density decreases the further away you get from the mean.

Both the mean and median are at the center and have the highest probability

density.

The normal distribution is defined by two parameters, μ and σ

. μ tells you

where the center of the curve is, and σ gives you the spread. If a continuous

random variable X follows a normal distribution with mean μ and standard

deviation σ, this is generally written X ~ N(μ, σ

It’s great being

normal. In an ideal world,

everyone would be like me.

The greatest probability

density is around the

mean, so values around

here are most likely.

X ~ N(μ, σ

)

The further you get

from the mean, the lower

the probability density.

So what effect do μ and σ really have on the shape of the normal distribution?

We said that μ tells you where the center of the curve is, and σ

indicates the

spread of values. In practice, this means that as σ

gets larger, the flatter and

wider the normal curve becomes.

is large.

is small.

The larger σ

becomes,

the wider and flatter the

curve becomes.

The normal distribution is an “ideal” model for continuous data

introducing the normal distribution