Chapter 2: The Normal Distribution

1. Chapter 2: The Normal Distribution 2.1 Density Curves and the Normal Distributions 2.2 Standard Normal Calculations

2. Histogram for Strength of Yarn Bobbins Bobbin #1: 17.15 g/tex Bobbin #2: 17.42 g/tex Bobbin #3: 17.93 g/tex X X X X X X X X X X X X X X X X X X X X X X X X

3. 15.60 16.10 16.60 17.10 17.60 18.10 18.60 19.10 19.60 Histogram for Strength of Yarn Bobbins X X X X X X X X X X X X X X X X X X X X X X X X

4. Density Curves • The smooth curve drawn over the histogram in the previous example is a density curve. • A mathematical model • Here, we are looking for an overall pattern; we may ignore outliers or slight irregularities. • Area under the curve represents proportions of scores/observations. • The area under a density curve is always 1.0.

5. Practice Problem • Exercise 2.3, p. 83

6. Figure 2.7, p. 83

7. 15.60 16.10 16.60 17.10 17.60 18.10 18.60 19.10 19.60 We can look at certain areas under the density curve and calculate proportions of observations between certain values.

8. Figure 2.4, p. 81: The area under the density curve is the proportion of observations taking values between 7 and 8.

9. Mean and Median of a Density Curve • Median: Equal-areas point of a density curve • Mean: Gets pulled towards any skew or outliers. • See Figure 2.5 (p. 81) • What about the mean and median of a symmetric density curve?

10. Figure 2.5, p. 81

11. Practice • Exercise 2.4, p. 84

12. Normal Distribution • If we draw a large sample from many populations of interest in science, scores tend to “stack up” in the middle, with a fewer number of scores towards the tails of the distribution. • Many, but certainly not all, distributions can be approximated by a normal density curve. • Normal density curves describe normal distributions.

13. Characteristics of a Normal Curve • Symmetric, single-peaked (uni-modal), and bell-shaped • The exact density curve for a particular normal distribution is described exactly by giving its mean µ and standard deviation σ. • Notation: N(µ , σ) • See Figure 2.10, p. 85 • We can “eye” µ and σ.

14. Figure 2.10, p. 85

15. 15.60 16.10 16.60 17.10 17.60 18.10 18.60 19.10 19.60 σ can be found by locating the point of inflection of the density curve.

16.   1   2   3 68.3% 95% 99.7% µ- 3σ µ- 2σ µ- 1σ µ µ + 1σ µ + 2σ µ + 3σ 68-95-99.7 rule

17. Practice Problem • Exercise 2.8, p. 89

18. Homework • Reading: Section 2.1, pp. 78-90 • Exercises: • 2.2, p. 83 • 2.5, p. 84 • 2.7, p. 89

19. Standard Normal Curve • Statisticians have made one particular normal distribution the standard and computed the proportions for this distribution: • This mean and standard deviation define the standard normal curve.

20. Standardizing • Use the following transformation: • A z-score tells us how far a given score falls from the mean, in terms of the standard deviation.

21. Example Problems2.19 and 2.20, p. 95

22. Normal Distribution Calculations • We first compute a z-score for a particular value, given its mean µ and standard deviation σ. • Then, because an area under a density curve is a proportion of observations in a distribution, any question about what proportion of observations lie in some range of values can be answered by finding an area under the curve. • Table A (front cover of your book) is a table of areas under the standard normal curve.

24. Practice • Exercises 2.21 and 2.23, p. 103

25. Homework for Rockmont Weekend • Read/finish reading Chapter 2 (the whole thing). • Chapter 2 test on Thursday (9/17).

26. Homework • Read through p. 103 • Problems 2.22, 2.24, and 2.25, p. 103

27. Homework • Exercises 2.41-2.45, pp. 113-114

28. Assessing Normality • Suppose that we obtain a simple random sample from a population whose distribution is unknown.  Many of the statistical tests that we perform on small data sets (sample size less than 30) require that the population from which the sample is drawn be normally distributed. • One way we can assess whether the sample is drawn from a normally-distributed population is to draw a histogram and observe its shape. • What should it look like? • Enter data from p. 17. • What other ways can we assess whether we have drawn a sample from a normally-distributed population?

29. Assessing Normality, cont. • This method works well for large data sets, but the shape of a histogram drawn from a small sample of observations does not always accurately represent the shape of the population.  For this reason, we need additional methods for assessing the normality of a random variable when we are looking at sample data. • The normal probability plot is used most often to assess the normality of a population from which a sample was drawn.

30. Normal Probability Plots • A normal probability plot shows observed data versus normal scores.  • A normal score is the expected Z-score of the data value if the distribution of the random variable is normal.  The expected Z-score of an observed value will depend upon the number of observations in the data set. • See Example 2.12, p. 106 for details. • If sample data is taken from a population that is normally distributed, a normal probability plot of the actual values versus the expected Z-scores will be approximately linear. • In drawing the straight line, you should be influenced more by the points near the middle of the plot than by the extreme points.

31. Normal Probability Plot Interpretation* negatively skewed normal positively skewed *From http://www.stat.psu.edu/~resources/ClassNotes/hrm_05/index.htm

32. Problem 2.27 (all parts except c), p. 108

33. Practice Problems • Exercise 2.30, p. 110 • Exercises, pp. 113-116: • 2.38 • 2.47 • 2.49