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More applications of the Z- Score NORMAL Distribution The Empirical Rule

More applications of the Z-Score NORMAL Distribution The Empirical Rule

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Page 1: More applications of the Z-Score NORMAL Distribution The Empirical Rule

More applications of the Z-Score

NORMAL DistributionThe Empirical Rule

Page 2: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Normal Distribution?These density curves are symmetric, single-peaked, and bell-shaped.We capitalize Normal to remind you that these curves are special.

Normal distribution is described by giving its mean μ and its standard deviation σ

Page 3: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Shape of the Normal curve

The standard deviation σ controls the spread of a Normal curve

Page 4: More applications of the Z-Score NORMAL Distribution The Empirical Rule

The Empirical Rule68-95-99.7 rule

In the Normal distribution with mean μ and standard deviation σ:

• Approximately 68% of the observations fall within σ of the mean μ.

• Approximately 95% of the observations fall within 2σ of μ.

• Approximately 99.7% of the observations fall within 3σ of μ.

Page 5: More applications of the Z-Score NORMAL Distribution The Empirical Rule

The Normal Distribution and Empirical Rule

Page 6: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Example: YOUNG WOMEN’s HEIGHT

The distribution of heights of young women aged 18 to 24 is approximately Normal with mean μ = 64.5 inches and standard deviation σ = 2.5 inches.

Page 7: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Importance of Normal Curve

• scores on tests taken by many people (such as SAT exams and many psychological tests),

• repeated careful measurements of the same quantity, and

• characteristics of biological populations (such as yields of corn and lengths of animal pregnancies).

even though many sets of data follow a Normal distribution, many do not.

Most income distributions, for example, are skewed to the right and so are not Normal

Page 8: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Standard Normal distribution

Standard Normal DistributionThe standard Normal distribution is the Normal distribution N(0, 1) with mean 0 and standard deviation

Page 9: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Standard Normal Calculation

The Standard Normal TableTable A is a table of areas under the standard Normal curve. The table entry for each value z is the area under the curve to the left of z.

Page 10: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Area to the LEFTUsing the standard Normal table

Problem: Find the proportion of observations from the standard Normal distribution that are less than 2.22.

illustrates the relationship between the value z = 2.22 and the area 0.9868.

How to use the table of values

Page 11: More applications of the Z-Score NORMAL Distribution The Empirical Rule

illustrates the relationship between the value z = 2.22 and the area 0.9868.

Page 12: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Example 

Area to the RIGHTUsing the standard Normal table

Problem: Find the proportion of observations from the standard Normal distribution that are greater than −2.15

z = −2.15

Area = 0.0158

Area = 1-0.0158

Area = .9842

Page 13: More applications of the Z-Score NORMAL Distribution The Empirical Rule

Practice

(a) z < 2.85

(b) z > 2.85

(c) z > −1.66

(d) −1.66 < z < 2.85

(a) 0.9978.

(b)0.0022.

(c) 0.9515.

(d) 0.9493.

Page 14: More applications of the Z-Score NORMAL Distribution The Empirical Rule

CODY’S quiz score relative to his classmates

79 81 80 77 73 83 74 93 78 80 75 67 73

77 83 86 90 79 85 89 84 77 72 83 82 x

z = 0.99

Area = .8389

Cody’s actual score relative to the other students who took

the same test is 84%