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Normal Approximation of the Binomial Distribution

Normal Approximation of the Binomial Distribution

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Page 1: Normal Approximation of the Binomial Distribution

Normal Approximation of

the Binomial Distribution

Page 2: Normal Approximation of the Binomial Distribution

What is the Probability of getting exactly 30 tails if a coin is tossed

50 times?X = tails, x = 30n = 50, p = 0.5

P(X = x) =

5030

(0.5)30(1 – 0.5)50 - 30

= 0.042

Page 3: Normal Approximation of the Binomial Distribution

Find the probability that tails will occur less than 30 times….

The magnitude of the calculation motivates us to determine an easier way…

Page 4: Normal Approximation of the Binomial Distribution

Notice:

The most frequent outcome of flipping a coin should be 25 tails, then 24/26, then 23/27 and so on….

This structure has already been modeled and studied as a normal distribution!

Page 5: Normal Approximation of the Binomial Distribution
Page 6: Normal Approximation of the Binomial Distribution

Therefore

Under certain conditions, we may use the Normal Model to approximate probabilities from a Binomial Distribution!!

Page 7: Normal Approximation of the Binomial Distribution

Conditions that must be met

1. np > 5

2. nq > 5

Page 8: Normal Approximation of the Binomial Distribution

When we will use the Normal distribution to approximate the

Binomial distribution, we have to create a z score

Page 9: Normal Approximation of the Binomial Distribution

z = x - x

= np(1 – p)

x = E(x) = np

x = given value, but it must be corrected by 0.5 either way (if it is discrete…)

s

s

Page 10: Normal Approximation of the Binomial Distribution

Ex: If you toss a coin 50 times, estimate the probability that you will get tails less than 30 times.

To approximate, first we will check to see of the conditions

are met

Page 11: Normal Approximation of the Binomial Distribution

1. Is np > 5 ?

50(0.5) > 5 ?25 > 5 YES!

2. Is nq > 5?

50(0.5) > 5 ?25 > 5 YES!

If these conditions are met, that means there were enough trials so a comparable distribution was created…

Page 12: Normal Approximation of the Binomial Distribution

Now we need to determine the z score

z = x - xs

Page 13: Normal Approximation of the Binomial Distribution

Ex: If you toss a coin 50 times, estimate the probability that you will get tails less than 30 times.

Success: tailsn = 50p = 0.5E = x = 50(0.5)

= 25

Page 14: Normal Approximation of the Binomial Distribution

= 50(0.5)(1 – 0.5)

s = np(1 – p)

= 3.54

Page 15: Normal Approximation of the Binomial Distribution

Less than 30 tails..

x < 30

Imagine the 30 bin

3029.5 30.5

Page 16: Normal Approximation of the Binomial Distribution

z = x - x

= 29.5 - 25

3.54

= 1.27P(X < 29.5) = P(z < 1.27)

= 0.8980

s

Page 17: Normal Approximation of the Binomial Distribution

A bank found that 24% of it’s loans become delinquent. If 200

loans are made, find the probability that at least 60 are

delinquent.

Page 18: Normal Approximation of the Binomial Distribution

We may analyze this situation as a binomial model because:

1. A loan is either paid back or not

2. The loans are all independent

Page 19: Normal Approximation of the Binomial Distribution

Check to see if we can approximate…

1. np = (200)(0.24)

= 48 (which is greater than 5, so yes)

2. nq = (200)(0.76)

= 152 (which is greater than 5, so yes)

Page 20: Normal Approximation of the Binomial Distribution

E(X) = x = np

= (200)(0.24)

= 48

s = (npq)1/2

=[(200)(0.24)(0.76)]1/2

= 6.04

X = 60, then adjust to 59.5

Page 21: Normal Approximation of the Binomial Distribution

z = 59.5 – 48

6.04

z = 1.90 (97.13% from the chart)

Therefore, the number of delinquent loans is

100% - 97.13% = 2.87%

Sketch this to confirm!

Page 22: Normal Approximation of the Binomial Distribution

If we were to actually calculate the probability directly, it would come out to 3.07%

Page 23: Normal Approximation of the Binomial Distribution

Page 449

1,2[odd]3,5,6,8,10

Page 24: Normal Approximation of the Binomial Distribution

Note: Discrete to Continuouspg 306