the normal distribution

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The Normal Distribution

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The Normal Distribution. History. Abraham de Moivre (1733) – consultant to gamblers Pierre Simon Laplace – mathematician, astronomer, philosopher, determinist. Carl Friedrich Gauss – mathematician and astronomer. Adolphe Quetelet -- mathematician, astronomer , “social physics.”. - PowerPoint PPT Presentation

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Page 1: The Normal Distribution

The Normal Distribution

Page 2: The Normal Distribution

History• Abraham de Moivre (1733) – consultant to

gamblers. Pronunciation.• Pierre Simon Laplace – mathematician,

astronomer, philosopher, determinist.• Carl Friedrich Gauss – mathematician and

astronomer.• Adolphe Quetelet -- mathematician,

astronomer, “social physics.” Pronunciation.

Page 3: The Normal Distribution

Importance• Many variables are distributed

approximately as the bell-shaped normal curve

• The mathematics of the normal curve are well known and relatively simple.

• Many statistical procedures assume that the scores came from a normally distributed population.

Page 4: The Normal Distribution

• Distributions of sums and means approach normality as sample size increases.

• Many other probability distributions are closely related to the normal curve.

Page 5: The Normal Distribution

Using the Normal Curve• From its PDF (probability density function)

we use integral calculus to find the probability that a randomly selected score would fall between value a and value b.

• This is equivalent to finding what proportion of the total area under the curve falls between a and b.

Page 6: The Normal Distribution

The PDF• F(Y) is the probability density, aka the height

of the curve at value Y.• There are only two parameters, the mean

and the variance.• Normal distributions differ from one another

only with respect to their mean and variance.

22 2/)()(2

1)(

YeYF

Page 7: The Normal Distribution

Avoiding the Calculus• Use the normal curve table in our text.• Use SPSS or another stats package.• Use an Internet resource.

Page 8: The Normal Distribution

IQ = 85, PR = ?

• z = (85 - 100)/15 = -1.• What percentage of scores in a normal

distribution are less than minus 1?• Half of the scores are less than 0, so you

know right off that the answer is less than 50%.

• Go to the normal curve table.

Page 9: The Normal Distribution

Normal Curve Table• For each z score, there are three values

– Proportion from score to mean– Proportion from score to closer tail– Proportion from score to more distant tail

Page 10: The Normal Distribution

Locate the |z| in the Table

• 34.13% of the scores fall between the mean and minus one.

• 84.13% are greater than minus one.• 15.87% are less than minus one

Page 11: The Normal Distribution

IQ =115, PR = ?

• z = (115 – 100)/15 = 1.• We are above the mean so the answer

must be greater than 50%.• The answer is 84.13% .

Page 12: The Normal Distribution

85 < IQ < 115

• What percentage of IQ scores fall between 85 (z = -1) and 115 (z = 1)?

• 34.13% are between mean and -1.• 34.13% are between mean and 1. • 68.26% are between -1 and 1.

Page 13: The Normal Distribution

115 < IQ < 130

• What percentage of IQ scores fall between 115 (z = 1) and 130 (z = 2)?

• 84.13% fall below 1.

• 97.72% fall below 2.• 97.72 – 84.13 = 13.59%

Page 14: The Normal Distribution

The Lowest 10%

• What score marks off the lowest 10% of IQ scores ?

• z = 1.28• IQ = 100 – 1.28(15) = 80.8

Page 15: The Normal Distribution

The Middle 50%

• What scores mark off the middle 50% of IQ scores?

• -.67 < z < .67; • 100 - .67(15) = 90• 100 + .67(15) = 110

Page 16: The Normal Distribution

Memorize These BenchmarksThis Middle

Percentage of Scores

Fall Between Plus and Minus z =

50 .6768 1.90 1.64595 1.9698 2.3399 2.58

100 3.

Page 17: The Normal Distribution

The Normal Distribution

Page 18: The Normal Distribution

The Bivariate Normal Distribution

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