Shape of Normal Curves

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Shape of Normal Curves

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Shape of Normal Curves. Shape of Normal Curves. 68%-95%-99.7% Rule. Areas under Normal Curve. Areas under Normal Curve(cont). Example: Normal Distribution. - PowerPoint PPT Presentation

Transcript of Shape of Normal Curves

Page 1: Shape of Normal Curves

Shape of Normal Curves

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Shape of Normal Curves

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68%-95%-99.7% Rule

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Areas under Normal Curve

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Areas under Normal Curve(cont)

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Example: Normal DistributionThe brain weights of adult Swedish males are approximately normally distributed with mean μ = 1,400 g and standard deviation = 100 g. (No real life population follows a normal distribution exactly!)

a) What is the probability that an adult Swedish male has a brain weight of less then 1,500 g?

b) What is the probability that an adult Swedish male has a brain weight between 1,475 g and 1,600 g?

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Example: Normal Distribution (cont)μ = 1,400 g and = 100 ga) What is the probability that an adult Swedish

male has a brain weight of less then 1,500 g?

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Example: Normal Distribution (cont)μ = 1,400 g and = 100 gb) What is the probability that an adult Swedish

male has a brain weight between 1,475 g and 1,600 g?

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Area under the normal curve above

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Example: Normal DistributionThe brain weights of adult Swedish males are approximately normally distributed with mean μ = 1,400 g and standard deviation = 100 g. (No real life population follows a normal distribution exactly!)

c) What is the 55th percentile for the distribution of brain weights?

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Example (ExDispersion.sas)

Determine the percentage of data points within 1 SD? 2 SD?

7 21 12 4 16 12 10 13 6 1313 13 12 18 15 16 3 6 9 11

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Example: Normality (ExNormal.sas)7 21 12 4 16 12 10 13 6 13

13 13 12 18 15 16 3 6 9 11

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Example: QQPlots – Normal (ExQQplot.sas)

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Example: QQPlots – Right Skewed

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Example: QQPlots – Left Skewed

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Example: QQPlots – Long Tail

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Example: QQPlots – Tails?

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Example 4.4.5: Nonnormal Data

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Interpretation of Shapiro-Wilk Test

P-Value Interpretation< 0.001 Very strong evidence for nonnormality< 0.01 Strong evidence for nonnormality< 0.05 Moderate evidence for nonnormality< 0.10 Mild or weak evidence for nonnormality 0.10 No compelling evidence for nonnormality

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Objective Measure: SAS

Tests for NormalityTest Statistic p ValueShapiro-Wilk W 0.98762 Pr < W 0.8757Kolmogorov-Smirnov D 0.092489 Pr > D >0.1500Cramer-von Mises W-Sq 0.042289 Pr > W-Sq >0.2500Anderson-Darling A-Sq 0.233462 Pr > A-Sq >0.2500

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Objective Measure: SAS

Tests for NormalityTest Statistic p ValueNormal W 0.98762 Pr < W 0.8757Right Skewed W 0.949844 Pr > W 0.4226Left Skewed W 0.925624 Pr > W 0.0479Long Tailed W 0.927118 Pr > W 0.0043Short Tailed W 0.949227 Pr > W 0.0317