expectation random variables graphs and histograms expected value

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Expectation Random Variables Graphs and Histograms Expected Value

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Page 1: Expectation Random Variables Graphs and Histograms Expected Value

Expectation

Random Variables

Graphs and Histograms

Expected Value

Page 2: Expectation Random Variables Graphs and Histograms Expected Value

Random Variables

A random variable is a rule that assigns a numerical value to each outcome of an experiment. We will classify random variables as either

Finite discrete – if it can take on only finitely many possible values.

Infinite discrete – infinitely many values that can be arranged in a sequence.

Continuous – if its possible values form an entire interval of numbers

Page 3: Expectation Random Variables Graphs and Histograms Expected Value

Example One

Suppose that we toss a fair coin three times. Let the (finite discrete) random variable X denote the number of heads that occur in three tosses. Then

Sample

Pt. Value of X

Sample Pt. Value of X

HHH 3 HTT 1

HHT 2 THT 1

HTH 2 TTH 1

THH 2 TTT 0

Page 4: Expectation Random Variables Graphs and Histograms Expected Value

Example Two

Suppose that we toss a coin repeatedly until a head occurs. Let the (infinite discrete) random variable Y denote the number of trials.

Sample

Pt. Value of Y

Sample Pt. Value of Y

H 1 TTTTH 5

TH 2 TTTTTH 6

TTH 3 TTTTTTH 7

TTTH 4

Page 5: Expectation Random Variables Graphs and Histograms Expected Value

Example Three

A biologist records the length of life (in hours) of a fruit fly. Let the (continuous) random variable Z denote the number of hours recorded. If we assume for simplicity, that time can be recorded with perfect accuracy, then the value of Z can take on any nonnegative real number.

Page 6: Expectation Random Variables Graphs and Histograms Expected Value

Graphs and Histograms

Given a random variable X, we will be interested in the probability that X takes on a particular real value x, symbolically we write

pX(x) = P(X = x)

pX(x) is referred to as the probability function of the random variable X.

Page 7: Expectation Random Variables Graphs and Histograms Expected Value

Geometric Representation

Consider Example Two where a coin is tossed three times. From the given table we see thatp(0)= P(X= 0)= 1/8

p(1)= P(X= 1)= 3/8

p(2)= P(X= 2)= 3/8

p(3)= P(X= 3)= 1/8

Histogram

0

1/8

2/8

3/8

4/8

0 1 2 3

x-values

P(x

)-va

lues

Page 8: Expectation Random Variables Graphs and Histograms Expected Value

Line and Bar Graphs

Line Graph

0

1/8

2/8

3/8

4/8

0 1 2 4

x-values

p(x

)-va

lues

`

Bar Graph

0

1/8

2/8

3/8

4/8

0 1 2 4

x-values

p(x

)-va

lues

`

Page 9: Expectation Random Variables Graphs and Histograms Expected Value

Expectation

Arithmetic Mean

Consider 10 hypothetical test scores: 65, 90, 70, 65, 70, 90, 80, 65, 90, 90

Calculate the mean as follows:

n

xxxx n

21

5.779010

480

10

170

10

265

10

3

10

904801702653

x

Page 10: Expectation Random Variables Graphs and Histograms Expected Value

Expectation

We may express the arithmetic mean as:

As the number of repetition increases

kk xn

fx

n

fx

n

fx 2

21

1

iii pxn

f