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Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

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Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality
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Page 1: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Ch. 2 – Modeling Distributions of Data

Sec. 2.2 – Assessing Normality

Page 2: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Assessing Normality

Later on in this course, we will be using various statistical inference procedures to answer questions. These tests involve sampling individuals and recording data to gain insights about the populations from which they come (INFERENCE!!!!!).

When using the procedures, we usually have to assume the population has an approximately Normal distribution.

Today, we look at a strategy for assessing Normality.

Page 3: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 122

By plotting the data, we can see that the

distribution is roughly symmetric and unimodal.

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

Page 4: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 122Using computer software, the following numerical summaries were found:

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

Page 5: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 122

Counting the number observations within 1, 2, & 3 standard deviations

of the mean:

36/50 = 72%

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

Page 6: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 122

Counting the number observations within 1, 2, & 3 standard deviations

of the mean:

48/50 = 96%

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

Page 7: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 122

Counting the number observations within 1, 2, & 3 standard deviations

of the mean:

15.357 50/50 = 100%

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

Page 8: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 122

36/50 = 72%

48/50 = 96%

15.357 50/50 = 100%

The percents are quite close to 68, 95, and 99.7.

4.1 4.5 5.0 6.3 6.3 6.4 6.4 6.6 6.7 6.7

6.7 6.9 7.0 7.0 7.2 7.4 7.4 7.4 7.8 8.0

8.0 8.2 8.2 8.4 8.5 8.5 8.6 8.7 8.8 8.9

9.1 9.2 9.5 9.6 9.6 9.7 10.2

10.3

10.5

10.6

10.6

10.8

10.9

11.1

11.5

12.3

12.3

12.3

12.7

14.7

Page 9: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Assessing Normality Just because a distribution looks Normal,

doesn’t mean we can say it is. 68-95-99.7 Rule can help to provide additional

evidence for or against Normality Normal Probability Plots can also be a useful tool

when assessing Normality.

Page 10: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 123 – Normal Probability Plot How it’s made:

1. Arrange the observed data values form smallest to largest and record the percentile for each one.

2. Use the standard Normal distribution to find the z-scores at these same percentiles.

3. Plot each observation x against its expected z-score from Step 2.

Page 11: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example, p. 123 – Normal Probability Plot

Page 12: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Interpreting a Normal Probability Plot

If the points on a Normal probability plot lie close to a straight line, the plot indicates that the data are Normal.

Systematic deviations from a straight line indicate a non-Normal distribution.

Outliers appear as points that are far away from the overall pattern of the plot.

Page 13: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example p. 124 – Guinea Pig Survival

Page 14: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Example p. 124 – Guinea Pig Survival

Page 15: Ch. 2 – Modeling Distributions of Data Sec. 2.2 – Assessing Normality.

Homework: Due Tuesday P. 132 #65, 66, & 74


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